KEYWORDS: Sensors, Clouds, Detection and tracking algorithms, Performance modeling, Design, Machine learning, Wearable devices, Data conversion, Control systems, Computing systems
With the advancement of technology and social development, significant changes have occurred in people's production and lifestyle, with prolonged sedentary work and study becoming the norm for contemporary individuals. However, incorrect or irregular sitting postures for extended periods have increasingly severe health implications, affecting people's learning and work efficiency, and even leading to spinal diseases, causing inconvenience in daily life. The identification and monitoring of human postures have always been a research focus. The core of this system lies in the pressure array sensors embedded in the seat, which can real-time collect the force data on the chair surface and perform in-depth analysis through the algorithm embedded in the hardware system, accurately identifying the user's sitting posture. Once the system detects an improper sitting posture, it will immediately display relevant information and issue reminders to guide the user to adjust to a correct sitting posture. The system also features data uploading and cloud storage capabilities. By uploading the sitting posture recognition results to the cloud platform, users can conveniently check their prolonged sitting time and the cumulative time of various sitting postures through a mobile phone app. This function not only facilitates users to understand their sitting habits, but also plays a corrective and reminding role, helping users establish healthy sitting habits.
Based on the Cloud-Edge system architecture proposed by the research team, this article investigates the method of Kalman filtering in acoustic emission signal denoising processing. Acoustic emission (AE) signals are small sounds generated when a material is subjected to stress or deformation, often disturbed by environmental noise, which reduces the clarity and usability of the signal. In order to effectively reduce the impact of noise on AE signals, this paper adopts the Kalman filtering algorithm. By dynamically modeling and estimating the signal, noise suppression and signal recovery are achieved. Through comparative experiments and analysis, it has been proven that the proposed method has significant advantages in noise reduction and signal fidelity of acoustic emission signals, and has good application prospects.
KEYWORDS: Universities, Java, Information security, Design and modelling, Intelligence systems, Printing, Databases, Data storage, Control systems, Telecommunications
The personnel management system in universities is playing an increasingly important role in the management of university teachers. However, traditional personnel management systems only focus on the collection of basic information and simple statistics of tables, there is a lack of research and practice on the personalized needs by universities, especially teacher performance management. Based on the personnel management system of a university, this paper summarizes the needs of the personnel management system and the needs of teachers' performance management, carries out theoretical analysis and system design, and finally implements the system based on Java technology, Spring Framework, Mybatis and other technologies. The practical results show that the system functions well to meet the needs of the university for personnel system management.
KEYWORDS: Bridges, Data modeling, Structural health monitoring, Sensors, Clouds, Acoustic emission, Data processing, Safety, Computing systems, Data storage
With the continuous development of social economy and the continuous increase of transportation, bridges play an increasingly important role in transportation. Bridges are the basis of accelerating urbanization, and also the key to ensure safe and smooth transportation. With the increase of traffic load, the safety problems of bridge structures also appear. Due to the limitation of construction level, the understanding of structural complexity and the influence of external unpredictable environmental factors, people can not effectively understand the damage of the bridge structure and accurately evaluate the operation and maintenance of the bridge, resulting in a series of traffic accidents. In view of the above problems, this paper carried out the research of bridge health monitoring system based on "cloud edge". It takes acoustic emission (AE), capacitance, impedance, optical sensor, etc. as the basic sensing unit, and combines edge based big data processing with edge computing model as the core and centralized big data processing with cloud computing model as the center. A bridge health monitoring platform based on the cloud-edge-end architecture is designed, which can effectively process data in real time and realize cloud backup, so as to achieve real-time assessment and diagnosis of bridge operation safety without interrupting bridge traffic functions.
With the rapid development of the economy and the continuous improvement of people's living standards, the production of daily waste has increased sharply. In order to effectively realize waste reduction and resource recycling, garbage classification has been widely promoted. However, despite various publicity efforts, there are still problems with unclear, ambiguous, and incorrect garbage classification among residents. To address these issues, an intelligent garbage classification system based on vision and speech recognition technology has been designed. The system includes an intelligent management terminal at each park's garbage collection point, which collects voice and image information for processing and analysis on a central server. The terminal is equipped with the ability to control the opening and closing of the intelligent garbage can lid and monitor the status of the garbage collection points. Furthermore, a supporting mobile application is available, enabling users to learn about garbage classification knowledge online and to carry out proper garbage classification. The implementation of this intelligent system solves the problem of garbage classification, which improves the efficiency of garbage treatment and provides significant practical and social value.
KEYWORDS: Engineering education, Process engineering, Systems engineering, System integration, Statistical analysis, Software engineering, Quality systems
Engineering education accreditation is an important way for colleges and universities to promote the reform of engineering education and the construction of "New Engineering and Technical Disciplines". There are many problems in the process of promoting engineering accreditation, such as the fragmentation of teaching data, the complexity of process management, the heavy calculation workload and human handling errors of achievement evaluation. In order to solve these problems, an accreditation management system of engineering education is designed and developed based on B / S architecture. The system supports manual input and batch import of graduation requirement index points, index points support matrix, course objectives, assessment methods, course scores and other information. The system can automatically calculate and generate achievement evaluation reports. The system also supports comparison and analysis of evaluation data of different grades. The results show that the system can save a lot of manual work and effectively improve the efficiency of the accreditation process.
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