This PDF file contains the front matter associated with SPIE Proceedings Volume 12981, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

With the development of thermal power units towards high parameters and high power, the thermal economy analysis of thermal power plant pipeline efficiency has attracted increasing attention. Clarifying the thermal range corresponding to the thermal economy analysis and calculation of the pipeline efficiency of thermal power plants, that is, the influencing factors of the pipeline efficiency of thermal power plants, can get the way to reduce the heat loss of the pipeline of thermal power plants. In addition, the comparative analysis of pipeline thermal economy and steam turbine generator unit thermal economy is conducive to accurate analysis of the thermal economy change mechanism of the whole powerplant.

The global power system is undergoing a fundamental structural transformation, and the position of new energy in the power supply system is becoming increasingly prominent. For China, the coal-based energy supply system needs to shift to a low-carbon energy system. Vigorously developing new energy technologies such as wind power and solar photovoltaic power generation and accelerating the replacement of clean energy in the power supply system is one of the important tasks of China's energy transformation. However, the randomness and volatility of wind and light resources have a great impact on energy storage power and capacity support capacity. The configuration of energy storage on the new energy side needs to consider the characteristics of new energy output and space-time complementarity, and needs to take into account the indicators in single or multiple application scenarios. This paper will design a hybrid energy storage system based on new energy stations and supercapacitor energy storage, and elaborate the system structure in detail. The dynamic modeling of new energy station abstracts the physical model into mathematical model. Based on the frequency response characteristics of the synchronous motor, the control model of the super capacitor is designed. The improved droop control is used to make the super capacitor energy storage unit have the droop characteristics and the rotor inertia of the traditional synchronous motor, which is conducive to the coordinated control with the new energy unit. It realizes the coordination of new energy units and super capacitors, improves the inertial capacity of the system, and reliably restores the original level after the frequency step change while reducing the frequency change range as much as possible.

This paper explores the improvements of inerter-based vibration absorbers on the performance of balanced suspension in multiaxle heavy-duty vehicles. A traditional integral balanced suspension is employed and the random pavement input is used to evaluate the performance of the balanced suspension. Four inerter-based mechanical networks are adopted to replace the damper in the conventional balanced suspension to improve ride comfort and handling stability. Then, by solving related optimization problems, the optimal parameters of the components in the vibration absorber can be obtained. Based on these optimization results, the main findings are listed as follows: the ride comfort and suspension travel acceleration performance are both improved, and the dynamic tire load performance remains almost unchanged compared with the traditional balanced suspension; besides, the addition of one spring and one inerter to a traditional vibration absorber provides better performance than adding only one inerter.

The trajectory tracking is the core function to realize automatic operation of high-speed train. This paper designed a model predictive controller under consideration of multiple optimal objectives to achieve comprehensive control performance of accurate tracking, passenger comfort and energy conservation. By linearizing the cost function and constraints, the control optimization model is transformed into a mixed-integer quadratic programming model, which is solved by the Gurobi solver. There are several stages in the whole process of train operation with different priorities of multiple objectives. To adapt the different priorities of multiple objectives, this paper proposed a multi-objective and multi-stage (MOMS) MPC controller based on multi-objective particle swarm optimization algorithm and a selection strategy of optimal weight coefficient. The simulation results show that the controller designed in this paper can optimize the weight in different scenarios to improve comprehensive control performance.

The working principle and performance of hot melt disengager are introduced, and the reasons why it is eliminated by the market are analyzed. The working principle and performance of thermal explosion disengager are introduced, and some disadvantages of its current use are analyzed. A new type of thermal demagnetization disengager is proposed, and its working principle and performance are introduced. Given the defects of the above two disengagers, the application advantages of thermal demagnetization disengager are introduced.

Optical biosensors provide important opportunities and have broad application prospects in biomedicine, disease diagnosis, pharmaceutical, and other fields. In this paper, a reusable VCSEL (vertical cavity surface emitting laser) biosensor chip integrated with a multilayer nano grating structure is introduced. The selected laser wavelength is 850 nm, and the overall device adopts semiconductor laser packaging technology to achieve high integration of light source and multilayer nano grating. A small plasmonic biosensor platform based on hyperbolic metamaterial (HMMs) Au/Si₃N₄ is studied using the grating coupling principle. The transmission peak is adjusted to the wavelength of 850 nm by changing the nano grating parameters so as to improve the sensitivity of the sensor chip. However, the high cost of the sensor chip which is based on noble metal nanostructure and precision technology hinders its further application. In this paper, we propose to grow and remove parylene film on the sensor chip to realize the reuse of biosensors.

Power quality and energy consumption management is one of the key tasks of power grid companies. With the increasingly prominent power quality problems, it is necessary to study the additional losses caused by power quality problems on transmission lines. Therefore, the power quality and energy consumption prediction model of transmission lines based on grey multiple linear regression is constructed. The power quality and energy consumption prediction data of transmission lines are processed under the grey multiple linear regression, the characteristic attributes between samples are calculated, and the weight value of the characteristic attributes is obtained. The characteristic attributes of the calculation results are input into the grey multiple linear regression network, the power quality energy consumption prediction results of the transmission line are output, and the construction of the power quality energy consumption prediction model is completed. The experimental results show that the energy consumption prediction curve obtained by the model is consistent with the actual energy consumption curve, and the energy consumption in summer is generally higher than that in winter. Based on this result, we can effectively establish the demand response of the transmission line with a good prediction effect.

In order to solve the shortcomings of traditional magnetic liquid acceleration sensors such as complex structure, large size and low frequency range of use, this paper designs a single-degree-of-freedom magnetic liquid acceleration sensor. The suspended magnet and the adsorbed magnetic liquid are used as the inertial mass, and the copper coil is collected to produce current for acquisition and analysis to determine the value of acceleration. Through experiments, the inertial mass vibration amplitude and current magnitude of this accelerometer are analyzed from the three influencing factors of the spacing between the inertial mass block and the fixed ring magnet, the mass size of the inertial mass block and the damping ratio. The influence of the relevant variables on the phenomena generated by the magnetic liquid acceleration sensor is studied.

With the continuous development of industrial technology, automatic control has gradually been taken seriously and started to be applied in factory production, which has led to the unprecedented development of automatic control technology to replace people engaged in dangerous and repetitive production work. Since programmable controllers have the advantages of high safety, a low market price, perfect functions, and easy maintenance, they are extremely widely used in modern industry. According to the design requirements, this paper uses the S7 series PLC in the small machine developed and produced by Siemens Germany and focuses on its features and functions. This paper takes the inner bearing spray washing machine of a railroad wagon locomotive as the object, designs the electrical control schematic and PLC control program, which can realize fully automatic control washing and at the same time has the power-down protection function, and can realize automatic-checking conversion operation and manual setting of spray washing time.

Twin-spindle vertical machining center is a machine tool with two sets of high spindles in the Z direction and adjustable spindle center distance in the X-axis direction on the headstock. The headstock mounted on the column moves up and down the Z-direction guide rail accurately to realize the movement of the spindle and to complete the Z-direction movement requirements of the workpiece processing. Due to the double spindle configuration of the main shaft component, the no-load and load of the Z-axis lead screw are doubled, so it is necessary to make a counterweight to balance the lead screw load. The balance system of the spindle box is the core part of the double spindle machining center, which directly affects the machining accuracy. Generally, to reduce the no-load torque and wear of the ball screw, and ensure the accuracy of the screw and the accuracy of the machine tool, it is necessary to balance the weight of the spindle box and spindle. In this study, the advantages and disadvantages of various counterweight technologies are compared, and the counterweight method suitable for this product is selected to ensure the accuracy of the machine tool.

Flexible pressure sensors have potential applications in human motion detection and personal health monitoring. In this paper, foam flexible pressure sensors were prepared using polydimethylsiloxane (PDMS) and multi-walled carbon nanotubes (MWCNTs) with NaCl as a sacrificial template by adding (hydroxypropyl cellulose) HPC dispersant and (dopamine) DA binder to the conductive solution. It has the sensitivity of 0.07454 kPa^-1, fast response, and high repeatability at low stress. The sensor was fixed on the skin of a volunteer and could detect large movements of the finger, wrist, and even different walking states, showing that our sensor has application potential in wearable electronics.

A counter is a simple and practical sequential logic device used for timing, frequency division, clock pulse input counting, and clock pulse generation. Based on the above applications, this paper aims at the different characteristics of integrated chips 74LS16X—74LS160, 74LS161, 74LS162, and 74LS163. Specific design methods were adopted, such as the common asynchronous resetting or presetting method, synchronous resetting method, or presetting method. At the same time, according to the resetting and presetting mode of the integrated counter 74LS16X, different chips were selected for analysis and research, and the design method for N-system was effectively obtained.

The current conventional pressure thermometer temperature measurement error detection method mainly realizes detection by calculating the fixed error of temperature measurement, which leads to poor detection effect due to the lack of analysis of the uncertainty in the temperature measurement process. Therefore, based on Hall sensors, research has been conducted on the detection method of temperature measurement errors in pressure thermometers. Firstly, the uncertainties in different temperature measurement scenarios are analyzed and combined, and the temperature measurement error detection device is constructed. Hall sensors are used to judge the stability of the thermometer temperature values in the thermostat tank, and the actual temperature error is calculated by combining the deviation values brought by the uncertainties. In the experiment, the proposed method is tested for the accuracy of temperature measurement error detection. The experimental results show that the proposed method has a low misjudgment rate and has a more excellent detection effect when the temperature measurement error of the pressure thermometer is detected by the proposed method.

The carrier plate is an important part of the lifting mechanism of the single-car stereo garage. On the premise of meeting the requirements of usage, the lightweight design of the cargo plate is the key to reducing the production cost of the garage and improving operation stability. Therefore, a lightweight design scheme of the cargo plate is put forward. After topological optimisation, static analysis is carried out on the newly built model under the same conditions. Through comparison, the optimised cargo plate can meet the usage requirements. It also reduced weight by 81.44%.

Flying start is one of the key technologies in the control of position sensorless permanent magnet synchronous motor (PMSM) systems. The common method is to short-circuit the motor by triggering short-circuit pulses to achieve a flying start. In this paper, we propose an adaptive adjustment of the parameters of short-circuit pulse time and short-circuit pulse interval for the difficult selection of the parameters of the short-circuit pulse time and short-circuit pulse interval. The method estimates the approximate speed of the idling motor by adding a test short circuit under the idling condition. It adaptively adjusts the parameters of short-circuit pulse time and short-circuit pulse interval according to the above speed. The method solves the problem of low estimation accuracy due to the determination of parameters of the traditional two-short-circuit current method. It significantly improves the effectiveness of the two-short-circuit current method. Simulation and experimental validation of the above method are carried out, and the results show the correctness and effectiveness of the present method

In this paper, the distributed localization problem of wireless sensor networks under minuscule bias injection attacks is studied. It is assumed that the attacker launches minuscule bias injection attacks on the distance measurement between sensors during the localization process of wireless sensor network, and the accumulated bias value has a great impact on the localization accuracy. To solve the above problems, a distributed localization algorithm based on adjacent detection and numerical fitting is proposed in this paper. The proposed algorithm realizes the detection of minuscule bias injection attacks by adjacent detection and improves the localization accuracy of the algorithm by numerical fitting. Finally, the correctness and superiority of the proposed distributed localization algorithm are verified by simulation experiment.

At present, drum brake is widely used on heavy-duty vehicles because of it’s low cost and huge braking force relatively, but the temperature of the drum brake is easy to rise after braking for some time or repeatedly, which is lead probably the braking efficiency bad and even being out of control. Aimed the problem that the configuration of ordinary disc brake can’t meet the brake requirements of heavy-duty vehicles, a dual disc brake for heavy-duty trucks is designed in this paper. Test show that the clutch double disc brake is more suitable for heavy-duty truck because of it’s short braking distance, strong braking force, good performance of heat dissipation and low operation force compared with drum brake.

Accurate transmission line parameters play a very important role in the normal operation and stability of the power system, so the power system needs accurate parameter identification and measurement to ensure accurate parameter input to the power system in order to ensure the normal operation and stability of the power system, based on this, relatively high precision parameter identification has become one of the important research directions, so the proposed therefore proposes the use of dynamic factor. Compared with the traditional least recursive squares method (DRLS), which is relatively weak for the stability of model errors and sensitivity to outliers, the least squares method based on dynamic factor superposition has stability, strong resistance to error and accelerates the solution speed by cleverly selecting the pre-processing method, and the simulation verifies the resistance to error of the algorithm. The simulation verifies the algorithm's ability to resist errors, and according to the feasibility index of multi-point measurement, the results obtained improve the identification accuracy.

In order to study and design a high-strength suspension clamp suitable for a transmission line with a large transmission span, a finite element model of a high-strength suspension clamp for a transmission line with a large transmission span was established, and the high-strength suspension clamp was designed by using computational software to simulate and calculate the composition of high-strength materials. The results show that the cast iron overhang wire clamp in the transmission line with large span has safety risks, and the high strength overhang wire clamp designed in the study can effectively reduce the safety risks.

The umbilical winch plays a pivotal role in the launch and recovery system of remotely operated vehicles (ROVs). The control of the cable arrangement mechanism directly influences the winch's operational stability. To address the complex working conditions encountered by umbilical winches and based on existing research, a mechanism and servo control mode for automatic cable arrangement of the umbilical winch were designed. A simulation model of "Three closed loops" was developed, following the principles of servo control theory. The parameters for the current and speed loops were determined, and an adaptive fuzzy logic controller was designed for the position loop, incorporating output proportion self-adjustment. Through simulations conducted under various working conditions such as corrected inclined cable, and Lebus Rope Slots, it was demonstrated that the adaptive fuzzy logic controller outperformed the PI control. The feasibility of the proposed scheme was further substantiated through theoretical analysis and the simulation results obtained under different working scenarios.

In recent years, with the increasingly complex structure and control of power grid, in order to limit the impact of power outage accidents on the power system, it is necessary to study the decision-making method of black start scheme after power outage, and obtain a restoration scheme that is safe and reliable and can quickly restore power supply. The main contents of this paper are as follows: First, the output control method of photovoltaic power station based on sliding mode control is proposed, so that the photovoltaic power station can realize the load tracking more accurately and more quickly. Secondly, a control strategy combining MPPT control and load tracking control is proposed. Finally, based on the control strategy, a black start scheme of energy storage photovoltaic power station is proposed, and a simulation model is built in SIMULINK to determine the black start scheme and verify its feasibility.

The temperature and salinity of seawater are essential physical parameters for marine science. The measurement of conductivity is an important means of salinity measurement. In this article, seven electrodes are used to design a temperature and salinity sensor. The sensor detects voltage signal and current signal in strong marine noise environments using lock-in amplifier technology to improve the measurement accuracy of seawater conductivity. A seven-electrode temperature and salinity sensor and a Seabird SBE37-SI temperature and salinity sensor are used for laboratory comparison, the temperature measurement error and the conductivity measurement error are within ±0.005°C and ±0.0003s/m. The temperature and salinity sensor is installed on an ocean buoy, and offshore testing is carried out. The test data shows that the sensor's measurement data is accurate and reliable, and can be used to measure the temperature, conductivity, and salinity of seawater.

The servo of intelligent ammunition usually used brushless DC motor as the power source, and its control system was nonlinear, time-varying parameter and multi-variable coupling complex system. The traditional PID control algorithm had gradually failed to meet the high performance control requirements of the servo control system. In this paper, RBFPID algorithm combining RBF network and PID control was designed for the speed loop controller of the steering servo. The simulation results showed that the RBF-PID control algorithm achieved better control effect than the traditional PID control algorithm in dynamic response ability, steady-state accuracy of the system, follow-ability and robustness of the control system, etc.

The large-scale integration of distributed generation brings challenges such as operation instability and large fluctuation of distribution network margin to the new distribution network. A reasonable distributed generation configuration scheme can reduce the power supply cost and improve the energy utilization rate of the distribution system. This paper establishes a two-layer optimization model based on the economy, environmental protection and power supply reliability of distributed microgrid. The capacity of distributed microgrid is configured in the upper layer, and the economy of distributed microgrid is optimized in the lower layer. Under the constraints of the installation number, power balance and battery state of charge of distributed generation, adopt an improved multi-objective particle swarm optimization algorithm to solve the problem. The algorithm improves the learning factor and inertia weight to ensure the global search ability of the algorithm in the early stage and the fast convergence to the optimal solution in the later stage, and introduces an adaptive mutation crossover strategy in the particle search process to avoid the particle falling into local optimum. The experimental results indicate the improved MOPSO algorithm obtains lower comprehensive cost, more reasonable configuration capacity, faster convergence speed and stronger optimization ability in the simulation process, which verifies the feasibility of the algorithm.

For the complex test environment and uneven road surface, a sensor multisource fusion framework was used for point cloud map construction and localization. The internal parameters of IMU were first calibrated, and then the spatial parameters of LiDAR and IMU were calibrated. The calibrated multi-sensor fusion algorithm has better map building effect and stronger localization robustness, which is valuable for mobile robot localization and navigation applications, through experiments at Taian Jichuang robot test site.

The existing 3D target detection network based on feature layer fusion of multi-view images and lidar point cloud fusion is mostly fused by directly splicing the multi-sensor features output by the backbone or the BEV features under the unified perspective of the two modalities. The features obtained by this method will be affected by the original data feature modality conversion and multi-sensor feature fusion`s effect. Aiming at this problem, a 3D object detection network based on feature fusion based on channel attention is proposed to improve the feature aggregation ability of BEV feature fusion, thereby improving the representation ability of the fused features. The experimental results on the nuScenes open source dataset show that compared with the baseline network, the overall feature grasp of the object is increased, and the average orientation error and average speed error are reduced by 4.9% and 4.0%, respectively. In the process of automatic driving, It can improve the vehicle's ability to perceive moving obstacles on the road, which has certain practical value.

Through the systematic summary of the current common used coal-fired units in the market dust removal system process lines, from the scope of application, the actual operation of the site comparative analysis, the improvement of the dust removal system is proposed.

In many application occasions of wireless sensor network (WSN), how to deploy the sensor nodes to achieve lager coverage is a significant problem. Generally, the deployment location depends on the number of sensor nodes. The regular polygon non-overlap deployed nodes coverage and non-loophole deployed nodes coverage models are built in the paper. Then the maximum number and coverage of sensor nodes deployed with non-overlap zone and the minimum number of sensor nodes deployed with non-loophole for a specific rectangle area are calculated by analytical method. According to the calculation results, it can be judged whether non-loophole or non-overlap coverage can be achieved or optimization methods is necessary for specific number of sensor nodes to cover a rectangle area.

Simultaneous Localization and Mapping (SLAM) has attracted considerable attention, focusing on autonomous driving and mobile robot in recent years. And it brings lots of different solutions and approaches to this field. With the development of technology, the single-sensor-based SLAM gradually reaches the limit to solve complex problems, which means multi-sensor fusion SLAM is the future. This article begins with a brief overview of the development, advantages, and disadvantages of single-sensor-based SLAM based on Lidar, Camera, and IMU respectively. From a historical perspective, we survey the famous and state-of-the-art works by taking a widely in-depth look at the developing process of Lidar SLAM and Visual SLAM that still require attention to meet the needs of modern applications. Moreover, an analysis is executed from two opposing approaches based on filter and optimization. Then makes a review of the current trends of multi-sensor fusion SLAM divided into Visual-Inertial, Lidar-Inertial, and Visual-Lidar-Inertial subcategories. At last, introducing some future challenges about dense 3D reconstruction, SLAM combined with Deep Learning, and developments of mapping.

Nowadays, the smart home industry is experiencing rapid growth due to advancements in information technology, such as the Internet and artificial intelligence. As a result, an increasing number of smart home devices are finding their way into our homes. The key advantage of smart homes lies in their ability to control various household facilities and equipment. While the current lighting systems available on the market offer some level of convenience by allowing users to control and switch lighting scenes at home, they often require complex operation and setup. As people's expectations for a higher quality of life continue to rise, it is essential for smart home technology to become more intelligent and dependable. Therefore, this paper proposes an intelligent illumination system based on digital twin technology, which can automatically switch scenes, adjust light brightness and color, and create a comfortable environment by collecting data and performing intelligent calculations and analysis. By doing so, it not only offers users a more convenient and diverse home environment but also provides intelligent and personalized lighting control.

In view of the low leakage detection rate of tailings pipeline, a method of tailing pipeline leakage detection based on multisensor and multi-entropy fusion was proposed. Firstly, the parameters of VMD algorithm were optimized by Sparrow Search Algorithm. The signal is decomposed by an optimized VMD algorithm, and the decomposed modal components are screened and reconstructed by kurtosis analysis. After that, the leakage experiment platform is built, the signal is collected by infrasound and negative pressure wave sensors, and the singular spectrum entropy and sample entropy of the pipeline signal are calculated, and the threshold interval of the two are obtained as the basis for judgment. Leakage experiments show that this method can accurately judge the leakage signal, and compared with the single sensor and single entropy fusion method, the accuracy is significantly improved.

In order to easily and accurately measure the junction temperature of high-power LED, this paper designs a new k-value calibration module based on LabVIEW platform, and develops an improved LED junction temperature measurement system by combining hardware equipment such as thermostat, data acquisition card, constant current source and temperature sensor, aiming at the huge workload brought by fitting the voltage temperature coefficient K under large current with small current data and the measurement error caused by self heating effect. The measurement system can measure the LED junction temperature in real time and store the required data. Compared with the measured junction temperature by forward voltage method, the maximum relative error is 1.4%, which indicates that the system has high measurement accuracy.

To solve the problems of short lifespan and double-line-frequency current ripple caused by LED driver, and reduce the number of energy processing times, a high-efficiency single-stage electrolytic-capacitor-less flicker-free Boost LED driver based on the traditional Boost circuit is proposed. The driver introduces a ripple cancellation circuit, which outputs an AC ripple cancellation voltage to offset the total voltage ripple and achieve a flicker-free LED driver. Ceramic capacitors are used instead of electrolytic capacitors to extend the life of the LED driver. 96% of the power of the LED driver is converted once, the ripple coefficient of the output voltage is less than 2%, and output capacitor Co1 and output capacitor Co2 are 100μF and 47μF ceramic capacitors respectively. Finally, an 80W experimental prototype was built to verify the feasibility of the scheme.

A semi-active suspension control method based on multi-sensor information fusion was proposed to improve the safety and smoothness of driverless A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. The camera and radar wave were used to scan and identify the uneven road conditions, and a mathematical model of road roughness was created. The information fusion and matching of uneven road surface were carried out by The information fusion and matching of uneven road surface were carried out by detecting the edge intersection ratio and Graph Neural Network(GNN) algorithm, obtaining a more reliable mathematical model of uneven road surface in complex environment. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to the optimal damping ratio of semi-active suspension. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to this damping ratio to adapt to different road conditions in real time. The vehicle ride comfort test under typical road input conditions was carried out. The vehicle ride comfort test under typical road input conditions was carried out, and the vibration acceleration time domain response signals of different suspensions were compared and analysed. The results show that the maximum peak vibration acceleration of the unmanned suspension controlled by multiple information fusion is reduced by 43 % comparing with that of the unmanned suspension. reduced by 43 % comparing with that in the passive suspension under the same conditions, which verifies the superiority of the proposed method.

Due to GMR biosensor has the advantages of miniaturization, low-power consumption and rapid detection, so it has a widely application in bacterial, viral and cancer biomarker detection. However, the current GMR biosensor has relatively low detection sensitivity in biotarget detection, that limiting the development in biomedical detection applications rapidly. As we know, the magnetic flux concentrator (MFC) can improve detection sensitivity and resolution of the GMR sensor. So, a new micro-funnel-shaped MFC is proposed for GMR biosensor and analyzed by finite element method (FEM). The MFC shape, size and distribution of magnetic labels were investigated. The simulation results indicate that the signal gain can be increased by 30% with the hypotenuse of MFC. The distribution of magnetic labels was arranged horizontally along the X-axis can generate high magnetic field intensity and the strongest magnetic gain. The simulation results has certain theoretical guidance for high sensitivity GMR biosensors design.

With the liberalization of the epidemic and the gradual recovery of the tourism sector, the profession of tour guide has become increasingly critical. However, this profession is mainly engaged in repetitive work, not very innovative. Combined with the above characteristics, in order to comply with the trend of social automation development, the tour guide service robot was born. The multi-functional intelligent tour guide service robot system takes the Raspberry Pi 4B microcomputer as the control core, completes the robot tour function through the signals collected by the infrared sensor tour module, and realizes the robot's trajectory navigation function by combining with the TB6612FNG module to drive the DC motors to arrive at a certain attraction accurately. When it arrives at a specific attraction, it will explain the relevant information of the attraction to the tourists through the voice module. In addition, the ultrasonic obstacle avoidance module with servo gimbal can avoid the obstacles that may appear on the path and adjust the route to fulfill the obstacle avoidance function.

In vehicle position monitoring, the GPS system provides high accuracy but has a low refresh rate and is susceptible to instability in environments with weakened signals. The IMU can provide high-frequency motion data in a short time, but its integration errors accumulate over time, leading to position estimation drift. Combining the two through data fusion can compensate for their respective drawbacks and achieve better monitoring results. This paper explores GPS/IMU data fusion based on the principles of Kalman filtering, linear regression, and random forest. The results show that the Kalman filtering algorithm achieves better data fusion performance, with the Mean Squared Error (MSE) controlled below 0.68, while the MSE values obtained from linear regression and random forest algorithms are more than 6 times higher, indicating inferior performance. This confirms the superiority of the Kalman filtering approach over simple machine learning algorithms in data fusion. Additionally, the study simulates different noise environments and reveals that GPS signals have a significant impact on the fusion algorithm's results, being more susceptible to noise interference. This insight provides a feasible direction for further optimization of the Kalman filter.

This paper introduces the typical principles of electric field measurement systems, compares the corresponding measurement parameters, analyzes the advantages and disadvantages of electric field sensors. As the development trend of micro sensors, the different designs of electric field sensors based on MEMS technology are introduced. Benefit from the Wheatstone bridge method which can measure resistance accurately, the frequency range of the electric field sensor is DC-100kHz, the electric field range is 12.7-1570 kV/m, and the sensitivity is 7.86mV/(kV/cm).

The localization problem plays a vital role in the services provided by wireless sensor network (WSN). Under non-ideal conditions, the transmit power is different and the clock is not synchronized. The measured received signal strength (RSS) and time of arrival (TOA) are subjected to deviation. In this paper, a novel algorithm was proposed to alleviate these impact on localization accuracy and reduce computing consumption of localization. The algorithm called generalized trust region subproblem with improved weights (GTRS-IW) transforms the original non-convex problem into a framework of generalized trust region subproblem and solves it through a bisection process. Additionally, a critical distance was introduced to improve the weights. The proposed GTRS-IW achieves precise localization accuracy with lower computation time and was verified through simulations.

Currently, the hand rehabilitation devices used by hemiparetic patients are bulky and cumbersome, which makes it challenging to meet the needs of home-based rehabilitation. To address this issue, this study has developed a lightweight and flexible sensor specifically designed for finger joint rehabilitation training. This sensor can accurately capture the degree of finger joint flexion while also enhancing comfort. It is expected to provide a reliable, convenient, and precise support for post-stroke hemiparetic patients' hand rehabilitation.

This article designed a simulation actuation system using a brushless DC motor to drive the aircraft control surface, enabling it to deflect the control surface to a specified angle as needed. Firstly, the working principle of brushless DC motor was studied and a simulation model of a double closed-loop speed control system was established. Then, considering the multivariable, nonlinear, and strong coupling characteristics of brushless DC motor, fuzzy PID is introduced for controller design to achieve precise control of rudder surface deflection angle. Finally, the simulation results indicate that the system performance using fuzzy PID controllers has significantly improved compared to traditional PID controllers in terms of Transient Time, Overshoot and Steady-State Error, indicating the effectiveness and superiority of the designed system.

Monitoring the microstrain on the surface of equipment is of great significance for its health monitoring and daily maintenance. A microstrain detection method is researched in this article based on the photoluminescence characteristics of SWCNT materials. The detection of microstrain can be achieved by using the SWCNT strain sensing film pasted on the surface of the tested equipment. A standard strain gauge can also be pasted on the back of the corresponding position where the SWCNT strain sensing film is pasted to check the detection accuracy of the SWCNT strain sensing film. A microstrain loading device is designed in this paper. Then, a simulation study on the stress distribution of components with different shapes and sizes is conducted. Simulation results indicate that the geometric shape of the tested component has a significant impact on the stress distribution.

Steam pipeline is one of the important power sources of cigarette factory and other factories, steam pipeline inspection is the daily work of the staff, but there are many disadvantages of manual inspection. Therefore, by designing an unmanned inspection vehicle to realize steam pipeline inspection. The inspection vehicle includes infrared tracking module, infrared obstacle avoidance module, environment detection module, infrared thermal imaging sensor detection module, image transmission module. It can realize the underground pipeline corridor environment detection and steam pipeline defect detection, and realize the safe production of steam pipeline.

The slow cooling of air brake carbon disc of civil aircraft is one of the important reasons that restrict the reliability of aircraft brake system. Aiming at the engineering problem of brake carbon disc in flight cooling, this paper proposes two ventilation schemes for aircraft main landing gear compartment, and carries out refined numerical simulation for the two schemes, compares and analyses the ventilation and cooling effects of the two schemes in detail, and weighs the analysis benefits and costs. The calculation results show that the 170mm inlet opening height of plan A has a better overall ventilation effect on the landing gear compartment, and the cooling effect of the upper and lower brake carbon disk is more uniform. Plan B can achieve better cooling effect near the brake carbon disc, but it brings a large fuel compensation loss, so it is necessary to select plan A or B according to the actual situation.

There is ice-shedding process of sudden load on conductor wire in heavy icing area, and the common aluminum alloy ball-eye has hidden danger of strength damage. In this paper, the model of suspension string-conductor wire is established, and the maximum axial force of the ball-eye is analyzed when it is completely ice-shedding in first gear. The finite element model of ball-eye is established and the stress analysis is carried out. The aluminum alloy was nanomodified, and the designed nano-modified aluminum alloy ball-eye was tested by tensile test. The results show that the common aluminum alloy ball-eye has safety risks in heavy icing area, and the nano-modified aluminum alloy ball-eye can meet the safety requirements in the heavy icing area and effectively reduce the damage risk.

With the rapid development of social economy, the scale of power grid construction is constantly expanding, and higher requirements are put forward for the safety control of substation infrastructure. However, there are many points, wide areas, information and low level of intelligence. Therefore, this paper proposes a substation safety management system based on power Beidou. On the one hand, the high precision positioning technology and 3D visualization model combined with the electronic fence to realize the effective division of the construction area, safety area and dangerous area, and realize the dynamic perception and early warning of operators and construction vehicles; on the other hand, the operation video shot by the camera, effectively improve the information and intelligent level of substation infrastructure safety control, and application value.

In environments where natural disasters such as earthquakes, fires, and typhoons occur frequently, rescue workers are unable to understand the specific situation and carry out rescue operations in a timely manner due to the complex terrain. Multilegged robots can adapt well to these environments and carry out rescue work in complex environments. We design a multiterrain rescue robot system based on STM32. The system achieves remote control of the walking of the proposed hexapod robot through a mobile app, and collects indicators such as the concentrations of smoke and toxic gas, which can adapt to walking in various complex terrains. We use STM32 as a core component to design a gain control module for controlling the walking of the proposed rescue robot. In addition, we use various sensors to collect environmental indicators and detect personnel information. Experiments demonstrate that the rescue robot can adapt well to walking in various terrains. It is helpful for promoting the development of rescue robot systems.

Surface fluctuating pressure is one of the most important parameters to characterize the boundary layer and the flow condition near the wall of the body, and it is an important basis for studying the turbulent boundary layer structure and its development, and grasping the boundary layer separation, turning and other flow states. To predict and control the hydrodynamic noise of the underwater body, it is necessary to accurately measure the turbulent pulsation pressure distribution on the curved surface of the attached body or model. For the measurement of pulsation pressure on the curved surface of a small-volume body model, a flexible MEMS pulsation pressure sensor line array that can be bent by 0°~5° has been developed, which consists of 32 MEMS microphones, a flexible circuit board, and a metal mask, with a volume of 170 mm×60 mm×1 mm, and the detection sensitivity of a single array element is 12.6 mV/Pa, with a wide frequency range of 100 Hz-10 KHz. At the same time, the FPGA-based control circuit and LABVIEW-based upper computer display interface are proposed to realize the waveform display and data storage of multiple pulsating pressure signals. The amplitude consistency of the flexible sensor array and the reliability of the acquisition system was verified using B&K microphones and NI acquisition cards. The results show that the amplitude difference between the array elements is within 2dB, indicating that the array has good linearity; the amplitude difference between the acquisition system and the NI acquisition card acquisition is better than ±2 %. Analyzing the conclusions, it can be seen that the whole system shows reliability in the process of measuring pulsating pressure signals.

With the gradual promotion of the application of lithium battery power ships and the increasing battery installation, the demand for battery energy storage container is gradually increasing. This paper mainly studies the key technology of the containerized battery energy storage system, combined with the ship classification requirements and the lithium battery system safety requirements. This paper also designs a scheme including the parallel connection, charge and discharge control and DC power grid protection of battery energy storage containers, which can be used as a reference for future application of the containerized battery energy storage system in electric vessels.

Since the study of the purification and separation of mixed signals has always been one of the core directions of algorithm application, we have analyzed the problem of sound blind source separation of several people or dozens of people, and deeply understood the main algorithms used to solve the blind source separation problem and its improvement methods. Here we want to analyze ICA (Independent Component Analysis) firstly and describe some of the basic knowledge required for ICA separation, then talk about the derivation of the detailed Fast-ICA algorithm and use it to solve the sound source separation problems and the decryption problem of pictures. Finally, based on the direction of convergence velocity, Fast-ICA’s improved method is proposed, which is more robust and faster convergence than the original method.

After a stroke, muscle spasms will occur randomly, which will result in failure of normal training. To ensure the safety of the training process, rehabilitation training equipment must have the function of spasm detection and related processing mechanism. In this paper, a method for detecting spasms based on motor current is proposed. The calculation process is given. The spasm is detected when the average motor current and its duration exceed the pre-set value. The sensitivity of spasm detection can be set by setting different current and duration pre-set values. This method is used in the active and passive rehabilitation training system for upper and lower limbs, which passed the safety test and got the NMPA permission. This system has been used in hospitals and nursing homes for more than 1,000 hours. This method has been proven to be effective according to the safety test and applications.

Based on EEMD and RMS, a method of abnormal power consumption detection by using a high-dimensional statistical index was proposed. Firstly, power users were divided into typical load types based on the density space clustering algorithm. For each load type, EEMD was used to transform the energy consumption curve into the high-dimensional empirical mode function group, and then Chebyshev polynomial index in the random matrix theory was used to monitor the abnormal behavior. The high-dimensional statistical index was constructed to judge abnormal power consumption. The algorithm in this paper has general applicability to multi-source load and a good discriminating effect.

Based on the information provided in Problem D of the 2022 National Undergraduate Mathematical Modeling Contest. Focus on the transmission scheme from the sub-station to the master station, and establish the relationship model between the maximum number N of master stations and the transmission time K firstly. Supposing each team’s two sub-stations transmit simultaneously to one master station for n times, the general model of sub-stations transmitting to each master station is studied, and the average number N_x of master stations that can successfully receive meteorological messages from at least one sub-station of each team under certain probability requirements is found, as well as the average number N_n of sub-stations that can be successfully received by any master station. At the end, the relations of the number n(successive transmissions of the sub-station), the probability P (each master station successfully receiving information from at least one sub-station of the team) and the time K are found, and the general transmission probability model of the sub-station is established.

In recent years, more and more air-conditioning equipment has been added in the school, which has become a major aspect of campus electricity consumption. In this paper, an infrared cloud remote controller is designed, which can be connected to MQTT (Message Queuing Telemetry Transport) cloud platform through WIFI or 4G interface, by subscribing to and publishing messages, it can accept configuration data and instruction information from the control center, also can upload local state to the control center, after two-way data channel connection, through the advanced management strategy, realize the energy-saving control of air conditioning.

Nonlinear friction and interference are the main factors affecting the performance of the navel gun servo system. Targeting improving the control accuracy of the servo system, the mathematical model of the servo system was established based on the LuGre friction model, and the load during the movement of the pitch system was analyzed. Then the adaptive robust controller was designed according to the characteristics of the system, and the control effects of the adaptive robust controller and PID controller were analyzed through simulation comparison. The simulation results have revealed that the controller can effectively estimate and compensate for the nonlinear friction and interference, and significantly improves the control accuracy and robustness of the system.

The use of tens of thousands of power electronic equipment and large-scale new energy has already caused tens of thousands of harmonics into the traditional power system, which has brought challenges to the monitoring of electrical quantities in the power system. Aiming at the problem that the measurement accuracy of traditional wide-frequency measurement algorithms is greatly lost in complex situations such as wide-frequency oscillation, a time-frequency domain dynamic phasor estimation method based on Window Interpolation FFT and Taylor Least Squares is proposed. First, a fast Fourier transform is performed on the electrical signal to sense the spectrum range of the signal, and then the dominant frequency band is obtained based on energy screening, according to which the sampling rate and model order of the Taylor least squares algorithm is selected. Next, the amplitude and phase position of each component is obtained by Taylor’s least squares. Finally, the simulation is carried out under steady and dynamic conditions concerning the IEEE standard of the fundamental wave, and it is proved that the algorithm can accurately obtain the time domain index and change trend of each harmonic and has strong practicability, which provides an effective means for the analysis of power grid wide-frequency oscillation.

With the rapid development of the radar-guided air-to-air missile, its precision strike capability has seriously threatened the survival of fighter aircraft. To address this problem, the jamming model of towed decoy to the radar-guided air-to-air missile is established. The influencing factors of the interference effect of towed bait are studied in this paper. Firstly, the characteristics of the radar-guided air-to-air missile are analyzed, and the missile motion model is established according to the kinematic principle of the missile. Then, combined with the towed decoy jamming technology model and the missile model, the influence of factors such as dry letter ratio and confrontation situation on jamming effectiveness is compared and analyzed. Finally, the usage rules of towed bait in air combat are obtained. The effectiveness of towed decoys against radar-guided air-to-air missiles is verified by simulation experiments, which can provide a reference scheme for pilots to get rid of enemy locks quickly.

Aiming at the high randomness and uncertainty of lightning trips in overhead transmission lines, the prediction accuracy and efficiency of lightning trips are low, and a prediction method of the lightning trip for overhead transmission lines based on CNN-GRU is proposed. Firstly, the feature set of lightning trip prediction influencing factors of overhead transmission lines is constructed based on the main characteristics influencing factors of overhead transmission lines and operating environment characteristics influencing factors. Secondly, the association rules are used to quantify the correlation between influencing factors and lightning trips. Finally, the Convolutional Neural Network (CNN) -gated Recurrent Unit (GRU) combined network is used to extract the high-dimensional internal connections between the influencing feature factors and line lightning tripping and train them. Combined with practical examples, the prediction results show that the CNN-URU prediction model proposed in this paper has higher prediction accuracy and efficiency than other prediction models.

In order to improve the interception capability of anti-submarine combat effectively, multi-static detection technology is applied to the UUV swarm to form a distributed detection system. Firstly, this paper describes the basic structure composition and operational characteristics of the distributed underwater unmanned detection swarm and then researches the detection principle and detection model of the multi-static underwater unmanned detection swarm deeply, finally, simulates and analyses the anti-submarine combat capability of the distributed UUV swarm. The research integrates multi-static detection theory, unmanned operation concept, and swarm tactics, which can promote the autonomy, coordination, systematization, and intellectualization of anti-submarine operations effectively, and improve the underwater battlefield awareness ability and the anti-submarine interception ability.

Anomal flow detection is an important cornerstone in the field of network security, and has been widely used in various fields. Traditional anomal flow detection schemes are usually based on statistical methods for flow detection. However, with the rapid development of artificial intelligence technology, more and more anomal flow detection schemes are mainly based on machine learning and deep learning, and their application scope is also becoming more and more extensive. Due to the shortcomings of a single model, research on flow detection using multiple models is increasing. Researchers hope to use multiple model schemes to improve the performance of flow detection, but in the use of multiple models, there are many problems such as the number of models and the difficulty of selecting multiple combinations. Based on the F2HDM (Filtering 2-stage Hybrid Detection Method) in multiple model schemes, this paper conducts a mathematical analysis of it, studies model selection issues in different scenarios, proposes a method to calculate the performance of multiple models based on the parameters of a single model, and selects the appropriate combination of anomal flow detection multiple models according to the desired scenario. Finally, we demonstrate through experiments that the selection method proposed in this article has high accuracy.

The unmanned boat is the key unmanned equipment developed in recent years. The unmanned boat has the characteristics of small size and flexible movement and it can perform military tasks such as patrol, reconnaissance, and attack on ships. Because of its operational characteristics, radar wave stealth application is also more common. The surface design of an unmanned boat is simple, but the surface equipment on the boat is not focused on, and there is much messy surface equipment, which is the focus of radar wave stealth design. In this paper, the radar stealth threat, stealth key technology, and stealth applicability faced by unmanned boats with high radar stealth are discussed, which provides a reference for the research on radar stealth requirements, stealth performance realization, and stealth combat of unmanned boats.

This paper presents a novel multi-strategy fusion neural network Named Entity Recognition (NER) method aimed at identifying entities in Process Specification Text (PST). To address the challenges of short sentence length, entity type error, and entity boundary error caused by limited context and semantic information in small sample data, the proposed method incorporates semantic enhancement (BM25, SBERT) and data augmentation (AUG) techniques. Additionally, a constraint matrix (Matrix) is designed to improve the consistency of the model and address the inconsistency caused by the pre-training model in the BERT-CRF model. The proposed method demonstrates significant improvements in both model consistency and F1 value. Specifically, the model consistency is improved from 90.71% to 94.51%, and the F1 value is increased from 76.90% to 77.21%. These results validate the effectiveness of the proposed approach for NER in PST.

In order to verify the applicability of the white light interferometer in the field of micro-nano size measurement. This paper analyzes the application of the white light interferometer in the field of micro-nano size measurement by using four cases, namely, the worn glasses lens, the ruby microsphere, the resolution plate and the solder joint. The measurement results show that the white light interferometer is not only suitable for the traditional roughness and step measurement, but also has a wide range of applications in the measurement and evaluation of micro-scratches, microsphere radius, micro-irregular size area and so on.

L3 automated driving allows the driver to engage in non-driving tasks, but when the system exceeds or is about to exceed the ODD (operational design domain), the system will remind the driver to take over the vehicle in time. In order to explore the impact of different interaction modes (visual, auditory and tactile) and their combined TOR (take-over request) interaction strategies on take-over performance, five different take-over interaction strategies are designed for TOR. Using the driving simulator and platform equipment, the automated driving take-over experiment was carried out with 24 automotive engineers for 120 person-times’ experiments. The results show that when the TOR period is 10 seconds, the multimodal interactive strategies can make the driver take over the vehicle successfully. Strategy 3 (visual + auditory + tactile) takes the shortest take-over time (M=3.8 seconds), which is most suitable for unplanned TOR. In strategy 4 (two-level TOR), drivers have the best performance in taking over the vehicle for the planned TOR with a shot take-over time (M= 4.15 seconds).

The intelligent robot car in this design uses Raspberry PI 4B-4GB motherboard to realize infrared obstacle avoidance, tracking, Bluetooth/infrared /WiFi remote control and video monitoring and other applications. The system integrates infrared obstacle avoidance module, tracking module with five-way detection, battery charging circuit, high-quality N20 motor, rubber wheel and RGB full-color lights and other external modules. Raspberry PI 4B+5 million pixel HD camera, support 1080P, can realize MJPG-streamer video monitoring, intelligent recognition driving and other advanced applications. The signal transmission and real-time control are realized by writing commands in Python language, and the command signals are transmitted to the Raspberry PI controller in real time so as to control the motion state of the car. Moreover, the current road environment information is captured by the camera, and various traffic signs are intelligently recognized^[1], so as to realize the purpose of intelligent control of the car.

Positioning plays a crucial role in parts manufacturing. While technical equipment may have the ability to work at multiple stations, adopting the same locating device can be challenging. In light of this issue, this paper introduces a new multistation positioning system that has been tested. The system offers reliable positioning, simple operation, and material savings.

This article proposes a modular design structure for a high-frequency box that meets the working requirements of a vehicle-mounted radar. The structure underwent static analysis under specified working conditions, and was optimized through the addition of reinforcing ribs and blocks based on the analysis results. Additionally, a topological optimization was performed on the optimized model to determine the optimal locations for weight reduction slots or holes while ensuring the structural strength and stiffness of the antenna tower. The best weight reduction scheme was ultimately determined, and the lightweight model was verified through testing. Results show that the optimized antenna tower was able to reduce its weight by 152KG, while maintaining the required strength and stiffness of the structure.

To realize the online monitoring and condition assessment of transformers in a substation, synchronous acquisition of the secondary signals from multiple interval transformers across the entire station is required. A signal acquisition system has been designed, consisting of a time synchronization unit, a signal conditioning unit, a data acquisition unit, and a host computer. The time synchronization unit synchronizes the secondary signals from multiple interval transformers. The signal conditioning unit converts the output signals from the transformers into low-voltage signals. The data acquisition unit controls the sampling timing of the analog-to-digital converter (ADC) using FPGA and reduces phase errors through a time-digital-converter-based ADC sampling delay compensation method. The host computer employs the FFT algorithm with a fourth-order Blackman-Harris window to calculate the amplitude and initial phase of the secondary signals. The test results demonstrate that the system's error meets the requirement of a 0.05-class accuracy. The measurement errors between different channels and different data acquisition units are all less than 0.05%

Garbage classification can be seen everywhere in today's society, but due to the changes of the times, the awareness that different types of waste should go into different types of bins is not widespread among most people. Even some people don't even know what kinds of garbage there are. Therefore, it is very important to identify garbage. In order to speed up training and recognition, the author improved the YOLOv8 algorithm newly released this year, and at the same time used crawlers to crawl thousands of pictures of different types of garbage on the Internet for training. The author obtained the model with the highest accuracy by increasing the number of epochs and adjusting the batch.

Classification of environmental sounds plays a key role in surveillance systems, crime detection etc. Since the study of the sounds in a real environment can get significant information. Deep learning models, such as convolutional neural networks, have been shown very useful for environmental sound classification (ESC). Recent work has shown that Vision Transformer (ViT) models can achieve comparable or even superior performance on image classification tasks. In the paper, an environmental sound classification method based on Vision Transformer is proposed. We represent sound files with their image representations, namely Log Mel Spectrogram Images and train a Vision Transformer model on these image representations. Specifically, the method obtains an average classification accuracy of 94.6633%. The classification result reveals that the proposed approach is with a good performance on the ESC accuracy.

To address the problem of poor accuracy of transmission line tower construction tilt detection, a transmission line tower construction tilt detection method based on secondary point cloud matching is proposed. The point cloud data of the pole tower is obtained through the aerial survey of UAV, and the secondary point cloud matching method is designed. Firstly, the extended Gaussian image clustering algorithm is introduced to complete the coarse matching of the point cloud, and the improved iterative nearest method is used to carry out the fine matching of the point cloud, and the change amount of the pole tower tilt is calculated through the secondary point cloud matching results, and whether the pole tower has tilt problems is judged according to the change amount of tilt. The experimental results show that the point cloud matching accuracy of the method is better, the point cloud matching time is shorter, and the tilt of transmission line towers can be accurately detected.

The information of a skeleton model mainly includes spatial information and critical interface data. It is an important tool for constraining detailed design in the early stages of the design process. The design of complex products follows a top-down decomposition and refinement process, followed by bottom-up iterative validation. It involves a high level of knowledge intensity and uncertainty. Automating the hierarchical modeling and evaluation of skeleton models has become a fundamental problem to be solved in top-down design. This paper analyses the characteristics of skeleton models, and defines the component correlations in skeleton models based on the design knowledge representation in conceptual design. A hierarchical modeling and evaluation method for multi-level skeleton models based on the transitive closure is proposed. Finally, the effectiveness of the method is validated through the skeleton modeling and evaluation of the fairing on a rocket.

Small target detection is a difficult point in target detection. Small target detection needs to identify the location and type of targets with few pixels in the picture and little resolution and feature information, and the algorithms used in the current application of mature medium and large target detection do not work well in detecting small targets. Therefore, improving the capability of small target detection is a current challenge in the field of target detection and an important research direction. In this paper, we will focus on deep learning small target detection technology, first introduce the definition of small targets and the reasons for the difficulty of small target detection, then comprehensively discuss the methods to improve the effectiveness of small target detection, and finally introduce the common small target datasets and the evaluation index of detection algorithms.

With the demand for energy, the marine current turbine (MCT) has been further developed. Since the blades are exposed to the sea water for a long time, they are prone to be affected by dirt and turbidity, and such imbalance fault features are difficult to be extracted owing to the frequent fluctuation of the current speed. Based on this, this paper proposes a method of Park's vector transformation and signal reconstruction. Firstly, the three-phase stator currents of a marine current generator are projected into the two-phase static coordinate system to obtain the square of modulus signal, which is filtered by wavelet transform and decomposed by empirical mode decomposition (EEMD) to obtain some IMF components, and then the signal reconstruction of some IMF components is carried out. By wavelet time-analysis, MCT blade state can be detected. The method proposed in this paper successfully detects the blade imbalanced fault.

Due to the complex environmental background of the substation, the wide field of view of drone inspection, and the small proportion of the target in the image, it is difficult to extract the defect feature of the small target and the high false detection rate. How to improve the recognition rate of the defect of the small target equipment is an urgent problem to be solved at present. The multimodel cascaded module, which is used to locate device components before defect detection or defect classification, is introduced into the Cascade-Rcnn algorithm to improve the accuracy of defects of small target device components and achieve accurate location of device defects.

The metrological characteristics of three-gas incubator directly affect the quality of cell culture. In order to promote the development of modern biotechnology, the corresponding calibration method is studied to ensure the accuracy and reliability of the measurement value. On the basis of fully understanding the structure and working principle of the three-gas incubator, the main measurement parameters were analyzed, and the calibration feasibility was analyzed in combination with the corresponding standards, measurement technical specifications and related research. The specific calibration method was determined, which was proved to be reasonable and feasible by experiments and provided technical basis for the effective traceability of the three-gas incubator.

Positioning and navigation functions are often required in grid design and operation and maintenance, general grid model building, and power plant operation. GDOP (Geometric Dilution of Position Precision) is often used to evaluate positioning accuracy. The smaller the GDOP value, the higher the positioning accuracy, and vice versa. Through the calculation and analysis of GDOP, it can help the positioning and navigation functions in the power digital twin application to more accurately locate equipment or monitoring points. This is of great significance to the operation and maintenance of the power system. In order to improve the calculation of DOP value using CPU/GPU collaborative acceleration in this paper, this paper uses GPU to optimize the core operator in GDOP calculation, and proposes a CPU/GPU collaborative computing solution. The experimental results show that the GDOP value calculation time is reduced from the original 3753 seconds to 232 seconds, and the performance is improved by 16.18x.

Silicone rubber insulators are widely used in power transmission lines due to their excellent hydrophobicity and strong resistance to pollution and flashover, but the insulators are subject to pollution and other factors during operation, which can destroy many of their excellent properties and cause very serious impacts on the power system. It is an effective strategy to prevent accidents and ensure the safe operation of the power grid to obtain the insulator pollution level quickly and accurately. In this paper, the hyperspectral test platform is used to obtain the hyperspectral spectral information of silicone rubber samples with different pollution levels, and the obtained spectral data are pre-processed with black and white correction, multiple scattering correction and SG smoothing to classify the hyperspectral data of silicone rubber samples with different pollution levels into 1-4. The training data and the test data are the hyperspectral data after pre-processing, divided in the ratio of 7:3, and the accuracy of the final test results reaches 95%. Finally, the training results of the deep extreme learning machine were compared with the support vector machine and BP neural network, and it was found that the DELM algorithm can combine the advantages of accuracy and rapidity, and has a greater application value in the classification of the pollution level of silicone rubber composite insulators. It provides a technical and theoretical reference for the non-contact detection of insulator pollution level.

In view of the problem of under-segmentation and over-segmentation in the point cloud segmentation process of irregular parts, a point cloud segmentation method for irregular parts based on Euclidean clustering is studied. First, the initial feature point extraction is performed based on the fast point feature histogram (FPFH); then the method of adjacent point angle and projection point angle is used to optimize the initial feature point extraction effect; finally, the threshold parameter is set, and the improved Euclidean clustering algorithm is used for clustering segmentation, and the clustering segmentation effect result graphs are obtained. The experimental results show that the algorithm can improve the segmentation accuracy of the irregular parts point cloud, not only ensure the integrity of each feature, but also enhance the processing of each feature detail part, and improve the speed of the clustering segmentation process.

The efficiency of manual loading and unloading in CNC machine tool processing is low, and the quality is not guaranteed. This article provides a design scheme for robot loading and unloading. The system mainly includes four parts: PLC control system, loading and unloading robot, machine vision system, and touch screen. Select SIMATICS7-200 SMART as the PLC to collect signals from various parts of the system and coordinate and control the actions of each part; The loading and unloading robot adopts the EFORT ER series 6-axis robot; The machine vision system adopts Huarui Technology's industrial camera and Dahua Vision Processing Software Algorithm Platform MVP; The touchscreen adopts Kunlun's TPC1031Kt (1024 * 600) for manual control and monitoring of the system. In addition, the robot is equipped with additional axes to enable one robot to serve multiple workstations, achieving flexible and intelligent processing and production.

The switchgear is one of the important electrical equipment in the power system. In the actual operation process, various types of discharges may occur in the switchgear, which poses a serious threat to the stable operation of the power system and the safe operation of electrical equipment. Therefore, the status monitoring and diagnostic technology of switchgear has become a necessary means for the safe and stable operation of the power system. This article studies the time-frequency characteristics analysis and application research of typical discharge signals in switchgear. By comparing and analyzing different signal sensing technologies, suitable detection frequency bands and signal sensing methods have been determined, achieving more accurate switchgear status detection and diagnosis.

Fall detection plays an important role in the healthcare of older adults. Falls are reported to be the leading cause of death in people over the age of 65. For fall detection, computer vision-based methods are currently the best performing methods. This paper proposes a fall detection system with a Orangepi 5B development board as the core processor and a shape and motion change method. First, we use the VIBE detection algorithm to extract human silhouettes from the video, and improve the effect through certain post-processing. Then, the information obtained by ellipse fitting and the vertical and horizontal projections in the projection map are used as features to distinguish different postures of the human body. The abnormal downward movement speed measured by the optical flow method was used to preliminarily judge the fall, and then the occurrence of the fall was determined by the auxiliary judgment of the posture feature. Experiments show that our fall detection system achieves good performance in terms of both accuracy and error.

This article analyzes the phenomenon of LED junction temperature changing with the circuit and environment, and proposes an improved measurement of LED junction temperature based on infrared thermal imaging method. By fitting the curve between heat transfer parameters and LED chip and packaging parameters, an LED experimental model is constructed, and junction temperature detection experiments and theoretical simulations are carried out. The results indicate that compared to traditional infrared thermal imaging methods, this method is more convenient and intuitive, and can quickly measure LED junction temperature. Finally, this article compares the LED junction temperature detection results under different experimental conditions to verify the universality of this method.

Cold chain transportation and storage equipment are widely used in logistics industry, scientific research institutions, and medical and pharmaceutical industries. According to GB/T 28577-2021 Classification and basic requirement for cold chain logistics, the cold chain facilities and equipment should be equipped with temperature and humidity recorders to continuously monitor the environmental conditions of cold chain. Therefore, thermometers and hygrothermographs are widely used in cold chain warehousing and transportation to monitor the temperature and humidity. However, there is no calibration equipment especially designed for thermometers and hygrothermographs used for cold chain storage and transportation monitoring at present. And with the fast development of cold chain monitoring, the requirements of accuracy for thermometers and hygrothermographs are becoming higher and higher, and the currently used calibration devices are gradually becoming unable to meet the requirements of both measurement range and accuracy. This article presents a novel design of temperature and humidity calibration device for cold chain warehousing and transportation. The temperature measurement range of the calibration device is (-40~60) ℃, and the humidity measurement range of the device is (20~95)% RH. The temperature uniformity of the device is not more than 0.3 ℃, the temperature fluctuation of the device is not more than ± 0.2 ℃, the humidity uniformity of the device is not more than 1.0% RH, and the humidity fluctuation of the device is not more than ± 0.8% RH. The experimental verification results show that the designed device can meet the requirements of temperature and humidity calibration for cold chain warehousing and transportation with efficiency and accuracy.

In the process of balise uplink signal transmission, the transmission signal is vulnerable to unknown electromagnetic interference, which leads to the increase of demodulation signal error rate and balise message error, which affects the train operation efficiency. In order to reduce the influence of electromagnetic interference on signal transmission, firstly, the signal transmission characteristics of balise uplink are analyzed. Secondly, according to the characteristics of uplink signals, the cost function based on Constant Modulus Algorithm (CMA) blind equalization algorithm is used, and the steepest descent method is used to optimize and update the equalizer weights, so as to equalize the uplink signals and reduce the interference of noise signals to useful signals. Finally, the variation of demodulation bir error rate (BER) indifferent signal-to-noise ratio (SNR) environments and during train operation after adding CMA algorithm to the balise transmission system is simulated and analyzed. The simulation results show that the demodulation method with CMA equalization has a lower bit error rate than the envelope detection method and the coherent demodulation method in different SNR environments. When the SNR is greater than or equal to 6dB, the demodulation BER with CMA equalization is much lower than that of the envelope detection method and the coherent demodulation method. The demodulation BER with CMA equalization is stable when the train speed changes, and far below 10 -6 , which meets the railway test standard. The content of this paper can provide theoretical reference for the application of balise in complex electromagnetic environment.

The Resistance Temperature Detector (RTD) signal processing circuit is an important part of the RTD temperature measurement module. In this paper, the RTD processing signal circuit with a dual redundant design is developed, which realizes the high-precision by the symmetrical design of the circuit layout and the precision control of components to ensure the consistency of the two outputs, and the error of the two output voltages is less than 0.36%. Meanwhile, the circuit adopts high-density thick film hybrid integration technology to realize circuit miniaturization. It is verified that the output voltages accuracy of the circuits for mass production is less than 0.19% at room temperature of 25°C, less than 0.39% at high temperature of 125°C, and less than 0.27% at low temperature of -55°C. The size of the circuit is only 14.40mm×14.40mm×3.55mm. This circuit can be widely used in the field of miniaturized and high-precision temperature measurement as a key device of high-reliability and general-purpose RTD temperature measurement module.

This paper proposed an in-situ electrical method that can identify the drive beam width and the sense beam width of the MEMS gyroscope at the wafer level. The mechanical frequencies of two special modes were selected to characterize the beam width. Ten chips distributed on a wafer was selected for experiments. The experimental results indicate that the etching error increasing from the wafer center to the edge, and the dispersion on a wafer with a radius of 75 mm is about 300 nm. Optical microscopy was used to prove the reliability of the method and the wafer-level data verified the distribution of the etching error. This work is aimed at providing a method for fabless designers to explore the fabrication imperfections during the MEMS wafer processing.

This paper mainly studies the acoustic optimization design based on typical valve-controlled hydraulic system. Firstly, by changing the output pressure and other key parameters, the pipeline pressure curve of the pump, valve group, hydraulic cylinder and other key equipment is calculated, and its noise characteristics are analyzed. Secondly, the accumulator, throttle valve and other typical vibration damping components are added to the system to simulate the influence of typical vibration damping components on the system noise under different parameters. In addition, this paper also studies the influence of pipeline parameters on the noise of the hydraulic system, and obtains the influence of different pipeline parameters on the pressure pulsation of the system by setting different pipe clamp distances and other influences on the natural frequency of the system. In order to complete the above analysis process, this paper first builds simulation models of hydraulic oil source and typical hydraulic system by AMESim. Then by using the control variable method , various working conditions are calculated. According to the simulation results, the noise characteristics and the acoustic design principles of the typical hydraulic system are summarized in this paper.

In practical, the signal to noise ratio (SNR) for the near bit signals is extremely low. To address this problem, empirical mode decomposition (EMD) is proposed to be applied to the extraction of weak SNR signals from downhole near bits. EMD detects the local extremes of the signals iteratively, interpolates the neighboring extremes to get the accurate upper and lower envelopes, and then takes the mean value of the upper and lower envelopes and subtracts them from the original signals to get a set of eigen values that meet specific conditions. Intrinsic mode function (IMF) is the set of eigen values, and then will be properly selected for reconstruction to finally realize the extraction of effective signals. Through simulation tests, it is verified that the measurement error of tool face angle is within ±1.0°, and the measurement error of inclination angle is within ±0.5°.

Thin film thermocouples are widely used for temperature measurement because of their high measurement accuracy, low thermal inertia, and small size. The static calibration method is an important factor in determining the temperature measurement accuracy of thin film thermocouples. Different calibration methods will result in different Seebeck coefficients obtained. The calibration methods studied so far can make the calibration process difficult to obtain accurate Seebeck coefficient values due to the mismatch between the distribution of the temperature field and the physical dimensions of the thin-film thermocouple. In this paper, we first designed and prepared NiCr/NiSi thin film thermocouples, and then characterized and analyzed their film thickness, structure, and composition, and built a multichannel fast acquisition thin film thermocouple static calibration system to calibrate the prepared NiCr/NiSi thin film thermocouples. The results show that the NiCr/NiSi thin film thermocouple designed and prepared in this paper meets the requirements of temperature measurement, the static calibration system built to test the standard K-type patch thermocouple Seebeck coefficient of the difference between the standard and the standard is only 0.3%and can be realized at the same time 9 groups of static calibration and Seebeck coefficient of consistency is good. This paper intends to solve the problem of difficult and inaccurate calibration of Seebeck coefficient in the temperature measurement process of thin film thermocouples and promote the efficient application of thin film thermocouples in temperature measurement and other fields.

Detection and localization of unexploded ordnance (UXO) on the battlefield is an urgent requirement for post-war cleanup. However, many existing research efforts have limited functionality and cannot comprehensively achieve the detection and localization of UXOs. In this paper, we propose a unmanned mobile system that combines both detection and localization capabilities for UXOs. The system consists of an unmanned tracked vehicle platform, a magnetic sensor array, a SLAM mapping system, a satellite positioning system, an environmental detection system, and a ground data processing terminal. By integrating the principles of magnetic sensors, visual sensors, and SLAM mapping, they complement each other in detecting and localizing UXOs. Experimental results demonstrate that the designed system can achieve real-time detection and localization of UXOs with strong practicality. It effectively avoids potential hazards to personnel and equipment during UXO detection and lays a foundation for future UXO detection and localization technologies.

In order to improve the safety level of tailings transportation pipelines, a pipeline leakage detection and positioning method based on infrasound signals is proposed. The paper uses the quadratic cross correlation time delay estimation method to calculate the time difference between the two ends of the pipeline receiving the leakage signal, and combines the pipeline leakage location principle to locate the leakage point. The experimental results show that for a 2km tailings conveying pipeline, the positioning error of the leakage point at a 25mm leakage aperture is within ±50m, and the relative positioning error is maintained within 2.5%, effectively meeting the technical requirements of the engineering project.

This paper proposes a wireless dynamic tire pressure monitoring system that utilizes electromagnetic induction-based wireless power supply technology to address the issue of wireless power supply for on-wheel pressure sensors. The system utilizes an IEPE pressure sensor, a 24-bit A/D conversion circuit, and a bluetooth MCU circuit to achieve high-frequency and high-resolution acquisition of dynamic tire pressure signals. The collected signals are then transmitted to a bluetooth receiver and converted into USB communication data for storage in the host computer by using bluetooth communication technology. Finally, the root mean square of the speed-corrected and frequency-weighted dynamic tire pressure signals is defined as the equivalent dynamic tire pressure. Various tests demonstrate that the system performs well in terms of wireless power supply and data transmission. The speed-dynamic tire pressure fitting algorithm can effectively suppress the impact of driving speed on dynamic tire pressure, enabling the equivalent dynamic tire pressure to respond effectively to pavement materials and typical diseases.

To address the problems of inaccurate dose rate and long-period measurement, this paper presents a nuclear radiation detector with the function of directional determination, as well as the corresponding Bayesian-based strength estimation method. Firstly, a vehicle-mounted radiation detector with large measurement range is designed, together with the steering mechanism and size-differentiated shielding shell, completing the ultra-wide measurement range of 0.15μGy/h~500Gy/h and the precise directional aiming functions. Enlightened by the characteristics of conjugated Gamma distribution, the radiation strength estimation method is established in an iterative manner. By comparing and analyzing the convergence properties and prediction efficiency under different shape parameters γ and scale parameters β, the selection criteria for initial model parameters is clarified. Finally, the proposed nuclear detector and estimation method are applied in the actual radiation source searching task, verifying the real-time, effectiveness, and accuracy of the overall radiation source detection system. The experimental results show that, the measurement method based on conjugated distribution theory can significantly reduce the number of estimated iterations to less than 15 steps, and improve the accuracy of the directional aiming function, ensuring the ability of on-line exploitation and exploration for the mobile system.

Temperature compensation at the cold end of thermocouple is an important problem in the application of thermocouple. In practical use, it is difficult to keep the cold end temperature of thermocouple at 0 °C or a certain value. In this paper, several common methods of thermocouple cold-end compensation are introduced, such as Ice Bath method, Compensation Bridge method. This paper analyzes the working principle, advantages and disadvantages, and the scope of application of these methods, and finally designs an integrated hardware solution, which can be used in aero-engine instrumentation. Thermocouple using integrated IC, direct thermocouple measurement and reference joint compensation together, effectively solve the problem of temperature compensation.

In view of the shortcomings of the current conventional technical means for partial discharge monitoring of high-voltage switchgear, an on-line partial discharge monitoring system considering the design principle of installation inside the switchgear is proposed on the basis of the combined transient earth voltage and ultrasonic monitoring technology, and has been put into use in a 110kV substation. The technology has the advantages of direct data collection in the cabinet, small interference of the data signal by the environmental background, high monitoring process efficiency and strong data comparability. By comparing the partial discharge monitoring results of various defect model and the monitoring results in the normal operation state of the switchgear, it is found that the system has good diagnostic ability for the above types of partial discharge defects and has good field application prospects.

As one of the important components of the automobile driving system, the automobile tire will not only bear the weight of the whole vehicle, but also have a crucial impact on the performance of the automobile, such as power performance, economy performance, passing ability, ride comfort, handling stability and braking performance. In the actual application process, the tires of all kinds of cars are worn to varying degrees, and the designed service life of tires is generally not less than 50000 km. However, many domestic self-owned brand cars fail to meet this requirement. The wear of tires will directly affect the service life of tires. It is of great significance to analyze the causes of tire wear correctly and ensure the correct use of tires to extend the service life of tires and improve the service performance of automobiles. This paper analyzes the causes of tire wear through the study of K&C characteristics of suspension, and puts forward improvement measures.

Electromagnetic interference is an important factor affecting the reliability of the engine fuel consumption measurement and control system. The interference will lead to an increase in the indicating error and a decrease in the sensitivity of the fuel consumption instrument, resulting in a decrease in the accuracy and reliability of the instrument. The key to the normal operation of the floating ball fuel consumption meter measurement and control system is whether effective anti-interference measures can be taken. This paper analyzes the influence of electromagnetic interference on the reliability of the engine fuel consumption measurement and control system and the method of eliminating or reducing the influence of electromagnetic interference. The results show that the measuring accuracy of the float-type fuel consumption meter is high after anti-jamming measures are adopted. By using various effective anti-jamming measures, the interference can be suppressed or eliminated, and the reliability of fuel consumption measurement and control system can be improved.

To solve the problem of wind-induced vibration of heliostat mirror in the solar thermal power generation system, it is necessary to build a wind-induced vibration monitoring and control system and use a vibration absorber to track and suppress wind-induced vibration. In this paper, the wind-induced vibration signal of the heliostat is collected, displayed, analyzed and extracted based on the LabVIEW platform. The Python deep reinforcement learning algorithm is used to build the vibration state and control state data sets, and the optimal control decisions are obtained through training. The excitation current is precisely controlled to control the stiffness of the magnetorheological elastomer in the vibration absorber, and then the vibration frequency of the dynamic vibration absorber is adjusted and controlled in real time, so that the time varying vibration signal of the mirror can be quickly tracked and suppressed.

Insulation breakdown occurred in the transmission process of a 500kV GIS circuit breaker. Through the analysis of the differential protection action of the system, gas composition and X-ray detection, the disintegration of the fault gas chamber, and the investigation and analysis in the factory, it was concluded that the contact spring of the fault circuit breaker gas chamber was broken, causing discharge in the transmission process. With the continuous transfer of the internal fault arc, the gas in the cavity expands, and the ablative decomposition falls to the shield part on the side of the moving contact through the support base porosity and generates discharge to the ground at the exit of the circuit breaker housing. The analysis and research of this accident can provide a reference for similar fault treatment and analysis in the future and the improvement of circuit breaker manufacturing and assembly process.

In order to explore the performance of the wire-plate ionic air cooling device, the mathematical and physical model of the device was established to simulate and calculate the influence of different channel lengths on the flow state and heat transfer effect of the ionic air cooling model. The results show that the airflow in short channel forms a large eddy due to electro-hydro Dynamics (EHD) effect and the air velocity near the hot wall is greatly increased, and the longer channel is easy to lead to the generation of multi-vortex structure, which is not conducive to heat dissipation. The convective heat transfer coefficient of the wall to be cooled at different electrode voltages and inlet velocity was comprehensively compared. The ionic-wind’s heat transfer effect was the best when the channel length was 13mm, which provided a reference for the size optimization of ionic wind cooling devices.

It is difficult to model and solve the dynamics of a multilevel mechanical transmission system. When the structural composition of the mechanical transmission system changes, it needs to be remodeled and solved. To solve this problem, a reconfigurable dynamic analysis method of a multilevel mechanical transmission system is proposed. By analyzing the structural composition of a multilevel transmission system, it is divided into several combinations of basic mechanism units. The dynamic analysis model of multiple mechanism units is established, and the combination interface of the dynamic model of mechanism units is constructed using the object-oriented idea, and the reconfigurable packaging is carried out to form the dynamic analysis model library of mechanism units. Aiming at the dynamic analysis of the combined connection between the mechanism units, the dynamic analysis method of the mechanism unit combination based on the equivalent component method is studied, the rapid reconfigurable dynamic analysis of the multilevel mechanical transmission system is realized, and the corresponding reconfigurable dynamic analysis software is developed. It provides an effective method for fast dynamic analysis of multilevel mechanical transmission systems.

Aiming at the nonlinear characteristics of the rolling bearing operation, a method based on the combination of EEMD and complexity is proposed to analyze the bearing fault. Firstly, EEMD decomposition is performed on the vibration signals under different operating states such as normal rolling bearing, inner ring fault, outer ring fault, and cage fault. Next, the kurtosis theory is used to filter out effective IMF under various operating conditions. Finally, the complexity analysis and comparison of the corresponding layer IMF in each operating state are performed in turn. The experimental results show that complexity is a characteristic parameter of the state of the system represented by the signal. This method can accurately and effectively identify the running state of the rolling bearing.

Aiming at the problem of insufficient structural reliability analysis of meta-action units, a reliability evaluation method of meta-action units based on the fault tree Bayesian network is proposed. Firstly, the composition diagram of the ball screw element action unit is modeled and analyzed. The element action unit’s abnormal operation fault tree is established using fault mode and impact analysis. The Bayesian network structure of the meta-action unit fault tree is adopted to enhance the failure traceability reasoning function and realize the optimization of the reliability evaluation function of the meta-action unit of the ball screw. Finally, the rationality of the model is proved by taking the unit test bench of the ball screw element as an example.

Aiming at the problem of limited fault information of a single sensor and low fault identification rate in the meta-action unit's fault identification process, a meta-action unit's fault identification method based on multisource information fusion and D-S evidence is proposed. Firstly, the fault identification framework of the mechanical element action unit is established, and multisource information acquisition and pre-processing are carried out on the built signal acquisition test platform of the mechanical element action unit; Then, the grey correlation method is used to analyze the correlation degree of the multisource information of the meta-action unit, and the multisource information fusion of the meta-action unit is realized based on the covariance cross method; Finally, the D-S evidence theory is used to complete the fault identification of the meta-action unit. This method is applied to the fault identification of the ball screw element action unit and compared with the single vibration signal and the traditional information fusion method to verify the accuracy and superiority of this metho

To greatly improve the design efficiency and the prediction of the welding properties of on-site solder joints in the welding workshop, an intelligent calculation algorithm based on CATIA. API for secondary development of the solder joint level is designed. This algorithm, based on different interference collision radiation radii, calculates the correlation accuracy between different solder joints and BIW products, and extracts the BIW product information data related to the solder joints, to realize the automatic generation technology of digital and intelligent welding attribute BOM. On this basis, we use Python to write the linear regression algorithm in machine learning to train the welding attribute parameters of the solder joints.

A method for extrapolating the real road fuel consumption data for hybrid heavy trucks based on the Monte Carlo method is proposed in this study. Firstly, the typical user usage condition characteristics of hybrid heavy trucks are defined. And then the hybrid heavy truck fuel consumption data is collected based on the predefined condition. Finally, the fuel consumption and corresponding statistical distribution of heavy trucks are obtained through the proposed Monte Carlo method.

To study the influence of air resistance on the theoretical calculation of fuel consumption under different working conditions and the conditions for using the simplified calculation form of ignoring air resistance to calculate fuel consumption, this study starts with specific examples of theoretical analysis of air resistance and fuel consumption and uses MATLAB software to fit and analyze the influence of air resistance on the calculation of fuel consumption. The scope of application of the simplified calculation form of the theoretical calculation of vehicle fuel consumption is further proposed. Finally, it is found that under the acceleration condition, when the acceleration reaches a certain value, the vehicle speed is within the maximum allowable error range of power calculation. The influence of air resistance on power calculation can be ignored. Under the condition of constant speed and given road conditions, the influence of air resistance can be ignored when the speed reaches a certain critical value and increases with the increase of the rolling resistance coefficient. This calculation model is put forward with a specific example, which provides ideas and suggestions for the theoretical design of vehicle fuel economy.

The self-adaption cycle time of the P0315 flywheel with multiple fault codes reported by a CVT vehicle is at the limit. To solve this problem, this study researches the cause of the fault code formation, the cleared conditions, the cause of the fault code, the fault diagnosis process, the fault vehicle data acquisition, and other aspects. The reason the engine fault report P0315 flywheel self-adaption cycle time is at the limit is finally verified.

An elastic stroke reduction mechanism based on the principle of differential lever is designed. The mechanism uses flexible hinges to transfer potential energy, and the displacement of the input end is transferred to the output end through the designed reduction ratio, so that the displacement resolution is improved, and the nonlinear and hysteresis errors are small. Firstly, the theoretical model of the stroke reduction mechanism is designed by differential principle and flexible hinge, then the static and modal analysis of the mechanism is carried out by ANSYS finite element, and the stroke reduction ratio of the mechanism is simulated to be 31.55 times. Finally, an experimental platform is built to measure the displacement output characteristics of the mechanism. The experimental results show that the designed stroke reduction mechanism has a displacement reduction ratio of 30.4 times, and the error with the simulation results is only 3.6%, which is consistent. At the same time, the dynamic input test shows that the mechanism has good dynamic stability and controllability.

With the advent of the holographic communication era, the amount and types of information continue to increase. People urgently need to explore new methods of information interaction. Tactile interaction technology can produce tactile feedback, greatly enhancing multimedia interactivity and user immersion, and it is an indispensable key technology in future multisensory interactive communication. This paper will introduce the latest research progress of tactile feedback devices in the past 5 years. According to different installation methods, it will be divided into four types and will summarize the characteristics of the most representative tactile feedback devices in the past 5 years in a table. Subsequently, the paper will summarize the existing problems of each type of tactile feedback device and analyze the future development trends of this field. The application of haptic feedback technology is prospected in the end.

A parameterized modeling and spatial layout method for marine parallel shaft gear transmission system is proposed using the idea of parameterized modeling and the characteristics of Open CASCADE technology, combined with the structural parameters of transmission system feature units and the logical relationship of transmission system. Based on the basic structural parameters and topological principles, parameterized models of gears, bearings, couplings, shafts, etc. were designed for typical units of the transmission system. The initial model generation of the transmission system shaft system was achieved. In response to the complexity and diversity of the spatial layout of the transmission system, a set of shaft system spatial layout schemes was summarized to improve the configuration efficiency of the marine transmission system, it has reference significance for the configuration design stage of the transmission system.

Wave buoy is one of the most comm small monitoring buoys in the field of ocean observation. It is widely used. The study on the structure optimization design and force analysis of small wave buoy is of great significance to improve the overall performance. The previous wave buoy body shape is mostly spherical, the upper cover area is small, the operation space is narrow, the installation and debugging is not convenient, and the number of batteries can be loaded inside is limited. The newly designed cylindrical wave buoy can increase the internal space and the number of batteries that can be installed under the condition that the diameter remains the same. At the same time, it can also increase the opening area of the upper cover, which greatly improves the operating space. The three-dimensional model was established by Inventor 3D modeling software, and then the static analysis was carried out by Ansys software. The results meet the design requirements.

Valve-side bushings in converter transformers are critical components that are subjected to various transient over voltages and complex high-order harmonic voltages during normal operation. The insulation state of these bushings faces significant challenges. Existing offline inspection methods have limitations in terms of real-time performance and applicability, and they cannot fully simulate real operating conditions. Therefore, this study investigates the amplitude and phase distribution patterns of the direct voltage, power frequency, and harmonic voltages in valve-side bushings. It establishes the mapping relationship between the time series correlation of leakage current and the insulation state. Based on this, an on-line insulation monitoring method based on time series correlation of leakage current is proposed, which ensures real-time performance through data-driven approaches and applicability through mutual supervision, while maintaining the realism of operating conditions through online implementation. The results of simulation experiments demonstrate that this method could accurately identify early-stage degradation of insulation in valve-side bushings. It exhibits higher sensitivity compared to existing methods and holds significance for ensuring the safety and stability of direct current transmission systems.

Digital Twin is a hot research topic in the context of smart manufacturing. This study aims to create a remote multi-platform robotic arm Digital Twin system based on OPC UA and Unity engine. Firstly, the basic principle model of the system is established, including the “real control virtual” and “virtual control real” frameworks. Subsequently, the virtual model is created using solid works, imported into Unity to complete the scene construction, various functional scripts are written using c# and given to each corresponding model, different resource packages are downloaded and different environments are configured for different platforms, Finally, connect the device and package the program to the corresponding platform device to realize the multiplatform Digital Twin of VR, AR, PC, etc.

The altitude difference along the western plateau railway is very large. The highest altitude of the whole line is more than 4000 meters, the lowest altitude is only a few hundred meters, the lowest temperature is lower than -30 °C, and the bridge-tunnel ratio is as high as 90 %. These special line conditions will lead to great changes in the basic resistance of rolling stock operation. According to the calculation formula of train dynamics, this paper analyzes the influence of these special environmental factors on the basic resistance of the train by adding altitude, temperature, tunnel and other factors. The results show that the basic resistance of the locomotives is closely related to altitude, temperature, tunnel and other factors. The basic resistance decreases with the increase of altitude and increases with the decrease of temperature. Compared with the open line in the plain, the altitude change in the western plateau will lead to a decrease of about 70% in the basic resistance of the open line, and the temperature change will lead to a decrease of about 17% in the basic resistance of the train operation. The basic resistance of the train running in the tunnel in the western plateau area will increase by about 133%.

The service performance of the rail transit vibration isolator is a necessary condition for the normal operation of the rail vehicle. In order to ensure the long-term safe and stable operation of the rail vehicle, the various performances of the rail transit vibration isolator must be scientifically and strictly tested. It is particularly important to use the "Rail Transit Vibration Isolator Fatigue Testing Machine" to measure and test the deformation performance, fatigue characteristics, fatigue life, prefabricated cracks, and crack propagation of various urban subway and high-speed rail vibration isolators under tensile, compressive, or tensile, compressive alternating loads at room temperature. Anhui Weiwei Rubber Group Co., Ltd. is an enterprise that develops and produces rail transit isolators. The company has entrusted our team to develop a fatigue testing machine for rail transit isolators with corresponding functions according to the requirements and standards of GB/T41492. After appraisal, the overall design of the fatigue testing machine is reasonable, the technology is advanced, and the actual indicators meet the design indicators. It also has a large expansion test margin and meets the expected requirements.

In this paper, we represent a robotic fish with rotary-to-swing mechanism and flexible tensegrity joints and its modal analysis method. The modal analysis method comprises two parts: stiffness analysis and derivation of the mass matrix. We simplify the robotic fish into a five-link mechanism which is a serial connection of a closed chain four-link mechanism with tail fin to make its modes analyzable. Based on this, we first analyze the stiffness matrix though the elastic potential energy and then obtain the mass matrix of the simplified mechanism considering hydrodynamic effects. Finally, the first-order modal analysis results in two cases which are both a C-shape at natural frequency are shown.

Given the shortcomings of the traditional tobacco feeder nozzle spatial position and angle adjustment process, such as the need for multiple people to cooperate, long time-consuming, and lack of quantitative adjustment devices and methods, this paper puts forward an improved design scheme for the nozzle spatial position and angle adjustment device. Combined with the new adjustment method, through the installation, debugging, and testing of the improved device, it can realize the visual, quantitative, and standardized adjustment of the nozzle’s spatial position and angle outside the drum. The field test results show that the application of the adjustment device and method greatly shortens the adjustment time of nozzle spatial position and angle, and improves the adjustment efficiency by about 82.79%. The visual and quantitative nozzle spot inspection work is realized, which is conducive to the timely discovery and rectification of its abnormal spatial position and angle, improves the homogenization level of the liquid spraying process, and reduces the weight of tobacco leaves adhered to the surface of the drum, which is conducive to improving the internal quality of tobacco leaves and reducing the waste of tobacco raw materials.

Aiming at the problem that the fault diagnosis accuracy of flexible DC converter valve is not high, a fault diagnosis method of Flexible DC converter valve of PCA-LSSA-LightGBM is proposed. Using the fault feature library collected by the simulation system, the normalization and standardization processing are carried out first, and then the key fault features are extracted through principal component analysis to reduce the complexity of fault diagnosis. Combined with the operation mode of flexible DC converter valve, a lightweight gradient elevator model is established, and optimizing the parameters of the light gradient boosting machine are found by introducing Singer mapping and the sparrow search algorithm optimized by Levy flight strategy. Experiments show that the comprehensive accuracy rate of PCA-LSSALightGBM model diagnosis results is 96.09%, which is higher than that of other fault diagnosis methods, which verifies the correctness and effectiveness of the fault diagnosis of flexible DC converter valve applied in this method.

Traditional tobacco feeder is equipped with multiple types of sensors, with a large number. Due to the lack of effective technical solutions to detect the sensor status and provide early warnings, the feeding process of the tobacco feeder has many drawbacks such as a high failure rate and a serious impact on product quality improvement. This article proposes a design of an intelligent tobacco feeder system based on self-diagnosis technology. Firstly, all sensor information of the feeder is collected, analyzed, and processed. Then, self-diagnostic algorithms are used to detect, judge, and alert the status of each sensor. Finally, the system reminds relevant personnel to conduct timely inspections and repairs, and when necessary, the system will activate emergency control plans to ensure smooth production. Before production, the fault detection rate increased by 77.43%, during the production process, the fault detection rate increased by 666.87% and the fault rate decreased by 69.57%.

This paper presents a vortex field analysis of a four-joint robotic fish using the Viscous Vortex Domain (VVD) method. Firstly, the structural composition of the robotic fish is briefly introduced. Secondly, by establishing discrete Lagrangian mesh points and analyzing the fish's oscillation posture based on discrete vortex field points, the swimming performance of the robotic fish in a flowing vortex field and its oscillation posture in a stationary vortex field are analyzed.

To solve the problem of missing values in dispatching data in power system, so as to improve the security and stability of power system. This article proposes a missing sequence interpolation algorithm for power grid dispatch data based on multi-source data fusion. Firstly, preprocess the multi-source power grid dispatch data, including data normalization, data serialization, etc. Then, construct an LSTM long-term and short-term memory network model, and train the model using existing data to learn the features and patterns in the original data. Next, tensor PARAFAC decomposition is implemented through first-order partial derivatives to interpolate sequence data containing missing values. Finally, the effectiveness of the evaluation algorithm is verified through experiments. The experimental results show that the algorithm proposed in this paper has high interpolation accuracy and real-time performance, and can effectively interpolate missing values in power grid dispatch data.

Aircraft ground air conditioning is an important component of replacing aircraft APUs in operating ground equipment, used to provide cabin air conditioning to aircraft instead of aircraft APUs. In view of the poor cooling (heating) effect of aircraft ground air conditioning in extreme weather (extremely cold, extremely hot), through on-site data acquisition and simulation verification of a domestic airline hub, the results show that CFD simulation technology can better simulate the aircraft cabin in the ground air conditioning working mode. Compared with the field test results, the simulation results have a small error and basically conform to the temperature distribution in the cabin.

In modern industry, motor failures are difficult to avoid. Traditional diagnosis methods are limited by feature extraction, and manual feature extraction can cause great variability. This paper proposes a diagnosis method based on vibration image and Convolutional Neural Network (CNN). By normalizing and binarizing the vibration data in one dimension, it forms the two-dimensional vibration images. Then, CNN completes the feature extraction and learning of image data, avoiding the limitations of traditional methods. A variety of common fault data can be generated by the motor fault state simulation of the experimental platform. After the test of experimental data, the proposed method can achieve 100% accuracy and realize error-free discrimination.

Manipulators with cams as the core components play an important role in industry. According to the designed end trajectory of the cam manipulator and the given necessary structural parameters, the cam profile can be inversely obtained. It is noteworthy that the cam profile is designed based on the theoretical correct size of all structural parameters. The size of the structural parameters of the parts changes within the given tolerance range during machining, so the manufacturing error of the actual structural parameters will inevitably lead to errors between the actual end track and the ideal track. In order to study the influence of structural parameter error on the end track accuracy, the cam profile curve is designed by analytical method, and the specific structural parameters that affect the end track accuracy are obtained through theoretical derivation. The theoretical analysis and provided a basis for strictly controlling the sensitive error in actual processing.

Motor drive circuits have been widely used in various fields in recent years, but there is no mature method for online evaluation of their health status under multiple failure mechanisms. By analyzing the common DC motor drive circuit, this paper selects the key devices and key parameters that can characterize the remaining life of the drive circuit. Using existing experimental data of device reliability, the remaining life prediction algorithm is constructed. The measured data are mapped into the typical failure model by gradient descent and similarity analysis method, so as to predict the remaining life of the driving circuit in the current operating environment, and then evaluate the health status of the circuit. Finally, the Δ R_ds (on) parameter of MOSFET was selected as the feature parameter, and the algorithm was simulated and validated using existing data.

The amplitude envelope model of locomotive signal induced voltage is established, and the TCR receives the amplitude characteristics of each segment in the track circuit bounded by the position of compensating capacitor, and find out the characteristic value of each segment, to achieve the initial diagnosis^[1]. For the case of false alarm at the critical point, a second diagnosis method is proposed under the constraint of the first derivative. To realize the accurate and fast diagnosis of multi-compensation capacitor fault at the same time.

The aerodynamic performance of a certain aircraft wing structure at different angles of attack was analyzed using FLUENT finite element software. According to simulation, the lower surface of the wing is a high-pressure zone, while the upper surface is a low-pressure zone. As the angle of attack increases, the high-pressure zone will move towards the leading edge of the wing, while the trailing edge of the wing slowly forms a low-pressure zone; At the same time, the airflow accelerates on the surface of the wing, causing a decrease in pressure and an increase in lift. This provides a theoretical basis for further optimizing the wing structure.

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly available datasets, and the results demonstrate the excellent performance of the proposed method.

As the core component of a converter transformer, the valve-side bushing is used to provide insulation and mechanical support for high-voltage current-carrying conductors passing through the transformer's metal casing at different potentials. During long-term operation, the valve-side bushing is subject to various transient over voltages and complex higher-order harmonic voltages, and the insulation condition faces serious challenges, requiring monitoring of its insulation status. DC leakage current is an important indicator reflecting the insulation status, while current leakage current measurement methods exist for measuring accuracy up to the μA level, making it more difficult to achieve accurate measurement of mixed signals at the mA-level AC μA-level DC. When a fault occurs in the system, the leakage current undergoes a large sudden change, and the existing sensors are unable to determine whether the sudden change comes from the deterioration of the casing itself or from a system fault. In this paper, based on the study of the leakage characteristics of the valve-side casing of HVDC transmission systems, and based on the harmonic characteristics of leakage in normal and fault conditions, a new method of abnormal data rejection is proposed to design a wide-frequency characteristic electromagnetic current sensor with μA-level high resolution and an external AC wide-frequency sampling structure to collect the AC voltage in the presence of weak DC signals in the presence of large AC characteristics. At the same time, the current criterion is set to reject the abnormal data to ensure the accuracy of the sensor measurement, and thus the accuracy of the leakage current measurement.

Gear pumps are widely used in the aviation field due to their advantages of simple structure, small size, and strong pollution resistance. With the improvement of aircraft performance and the limitation of internal space, the development of small and lightweight aviation gear pumps has become an inevitable trend. However, downsizing and lightweight design of gear pumps can lead to problems such as reduced strength of key components and increased wear, resulting in shortened lifespan and premature failure during service. Among all parts, the journal bearing is the most severely worn part of the gear pump. Therefore, the lifespan of the journal bearing determines the overall lifespan of the gear pump. This article designs the journal bearing based on the radial force of the gear pump and creates herringbone groove on the journal bearing. Through analysis and calculation, it is found that the bearing with herringbone groove has a larger oil film carrying capacity and lower temperature rise, which can provide a new method for the design of high-speed and small-sized gear pump bearings.

With the increasing demand of engineering surveying, the surveying and mapping instruments used in engineering surveying are also increasing, and the problem of tracing the source of these large surveying and mapping instruments is becoming increasingly prominent. This paper mainly studies the traceability problems of four kinds of surveying instruments, namely steel rule water level meter, large space handheld scanner, total station scanner and frequency modulation laser rangefinder, using data comparison and fitting analysis of indicating errors. Finally, we get that the FM laser rangefinder is the micron level measuring instrument, the steel rule water level meter and the whole station scanner are the millimeter level measuring instruments, and the large space handheld scanner is the centimeter level mapping instrument.

Fault diagnosis is an effective means to improve the reliability of the electric drive system of new energy vehicles. At present, inter turn short circuit faults in new energy vehicle motors are mainly detected using single fault features and small sample fault datasets. This method has low fault diagnosis accuracy and poor robustness. In this paper, a combined sample expansion strategy of conditional generative adversarial networks for attention mechanism optimization is proposed, and a fault diagnosis method combining improved Convolutional neural network is proposed. First, the combined characteristic data set of third harmonic and negative sequence current is made, and input into Attention CGAN to realize the expansion of original data samples. Then the expanded data and the original data are combined into a new data set, which is input into the improved CNN for training, and finally the fault diagnosis results are obtained. The experimental results show that compared to traditional fault diagnosis methods, the proposed fault diagnosis method based on combined features and data expansion improves the fault diagnosis accuracy by about 6.92%.

Static random access memory (SRAM) is a high-speed memory, which will produce performance degradation with the increase of use time, and the current research mostly analyzes its degradation process from an experimental point of view, but the correlation between basic circuits and components is insufficient. Starting from the physical degradation effect of transistors in SRAM unit circuits, this paper simulates the circuit-level characteristics of SRAM unit circuits to simulate their performance degradation, forming a complete research system from principle to phenomenon, and finding a new path for studying the circuit-level health monitoring method of SRAM performance degradation.

When using support vector machine (SVM) for transformer fault diagnosis, it is difficult to choose the optimal values of the kernel parameters g and c based on manual experience, resulting in lower accuracy of fault diagnosis. The Dung Beetle Optimizer (DBO) algorithm has the drawbacks of getting trapped in local optima and low convergence accuracy in later stages. To address these two issues, an improved Dung Beetle Optimizer (IDBO) is proposed to optimize the transformer diagnostic model for SVM parameters g and c. This method improves and optimizes DBO using the tent chaotic mapping, thereby enhancing the algorithm's optimization capability and accelerating convergence speed. Through comparative testing with the original DBO, Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), the superiority of the IDBO algorithm is verified. Compare the IDBO-SVM fault diagnosis model with SVM, SSA-SVM, and GWO-SVM models in fault diagnosis experiments, and compare it with the DBO-SVM model in stability simulation experiments. The results indicate that IDBO-SVM model has the best accuracy, better stability, and faster diagnosis speed, resulting in better fault diagnosis performance.

Fault diagnosis is an important part of the intelligent development of industrial robots. Aiming at the problem of lack of data in industrial robot fault diagnosis, this paper introduces a fault diagnosis method based on digital twin and data-driven fusion. The consistency between the model and the actual device is achieved by constructing a digital twin model of the industrial robot and mapping it to the actual industrial robot in real time. In order to solve the problem of lack of data, the fault injection technique was used to inject fault data into the digital twin model and combined with historical data to construct a training dataset. Through simulation experiments on real welding robot data, the machine learning fault diagnosis model was trained and evaluated for precision, recall and F-Score. The experimental results show that this method can effectively solve the problem of lack of fault data and train a reliable fault detection model, providing an effective solution for industrial robot fault diagnosis.

The capacitor voltage transformer running on the grid must carry out error detection within a fixed time according to the requirements of JJG 1021 《Instrument Transformer in Power System》verification regulation. When CVT conducts offline error detection, there is no other running equipment around. However, when the CVT is connected to the grid for operation, there are electric field effects produced by metal supports, lightning arresters and other grounding and electrified bodies around. There are differences in the environment between offline error detection and online operation status. Firstly, the paper analyzes the characteristics of the intermediate voltage change of the capacitor cell of CVT under the influence of high voltage under the condition of grid connected operation. Secondly, the equivalent model simulation model is established to analyze the quantitative impact of the electric field generated by these objects on the CVT error. These objects include metal supports, lightning arresters and multiple CVTs. Finally, the verification test of the influence of external electric field on CVT error is carried out. The test results show that the external electric field will cause the CVT ratio difference to shift in the negative direction, and the influence degree will be in the thousandth place, and the influence amount will become smaller with the increase of the distance between it and the surrounding objects. The conclusion of this paper provides technical support for the off-line error detection of CVT and the correction of additional errors caused by external electric field.

Rising environmental pollution necessitates advanced water quality testing technologies. Traditional methods are giving way to innovative strategies merging sensor detection and IoT technologies, ready for deployment on Unmanned Surface Vehicles (USVs). This review delves into the expanding landscape of remote water quality testing, highlighting diverse methodologies' advantages and the use of IoT-enabled testing on USVs. Responding to urban river monitoring needs, a small USV-based cloud platform has been deployed. It remotely monitors real-time water quality indicators such as temperature, pH, conductivity, TDS, and turbidity. Utilizing IoT, it allows remote operation and real-time data transmission to the networked LabVIEW instrument. This facilitates instant data access and analysis, providing a comprehensive view of the Qianshan River Basin water quality. The platform's innovative design offers a scientific basis for improving the river basin's water quality and environment.

The work aims to analyze the reason of material blockage in the screw feeder of the horizontal-tube continuous boiling system in daily work. Based on the analyses above, a method to solve the problem is put forward by controlling the axial movement of the screw shaft to achieve the spatial size change of the screw shaft and the shell, which will improve the working performance of screw feeders. And the feasibility of the design was verified by EDEM simulation experiments.

To address the problems of traditional and neural network-based methods in rolling bearing fault prediction, we proposed a fault prediction model for rolling bearings based on double adaptive sliding time windows. Firstly, the rolling bearing vibration signal is mapped into fault features that can characterise its degradation state by setting up a state estimation non-linear operator that can remove correlations. Secondly, a loss function is used as a criterion to set up an adaptive update mechanism for the model parameters, as well as a sliding time window capable of adaptively selecting the data length. Finally, the validity of the proposed failure prediction model is verified by simulating the occurrence of failures under the combined sudden and gradual failures in practice using the whole life cycle data of rolling bearings released by Xi'an Jiaotong University.

From the point of view of product quality, considering the performance characteristics of laminated elastomers, this paper proposes a classification method for laminated elastomers based on the K-medoids algorithm. In addition, the author also analyzed and verified the feasibility and beneficial effects of this method. The analysis results of this paper show that the method of mass classification of laminated elastomers proposed in this paper is feasible. Moreover, according to the classification results of this method, assembling the elastic element group can further improve its performance of the elastic element group. When the elastic element group completes the same action, its required force decreases significantly and its bearing capacity further increases.

The driving and driven gears located in the rear axle of a vehicle play a crucial role in power transmission. The bearings that support these gears are vital components whose longevity and operational conditions directly impact the efficiency of power transfer. This study focuses on enhancing the structural rigidity of both the gears and bearings by investigating the optimal spacing between two bearings on the driving gear. By extending the lifespan of these bearings, the overall performance of the main reduction unit is improved, leading to a reduction in noise and vibration, thereby enhancing the vehicle's NVH (Noise, Vibration, and Harshness) characteristics.

Smart manufacturing plays an important role in the development of the national economy, and mechatronics technology has the advantages of intelligence and microscopy, which can promote the innovation of smart manufacturing. This paper first analyzes the connotation and characteristics of intelligent manufacturing and then discusses the development characteristics of mechatronics technology. On the premise of the above theory, the new progress of mechatronics technology in the practice of smart manufacturing is presented from the following five aspects: the whole life cycle of an intelligent manufacturing process, the change of market demand inflection point, the creation of a digital collaborative foundation, the development of intelligent manufacturing simulation link, and the development of modular whole machine and the ability to cope with production changes. This paper can provide some theoretical value for the integration and development of smart manufacturing and mechatronics.