KEYWORDS: Data modeling, Unmanned aerial vehicles, Electroencephalography, Data acquisition, Cross validation, Control systems, Support vector machines, Education and training, Cognitive modeling, Calibration
This article designs a cognitive data collection paradigm that integrates PVT (Psychomotor Vigilance Task) with cluster missions to activate operator mental fatigue states and accurately collect sample data. Based on the sample data, a support vector machine (SVM) based classification model for the mental fatigue states of cluster drone operators is trained and constructed. Using this model, a cluster drone operation duration determination model is developed by setting criteria for extreme fatigue states and calibrating model thresholds. Through 100 rounds of 5-fold cross-validation on the dataset, the results indicate an accuracy rate of 95.5% for determining the mental fatigue states of cluster drones.
In recent years, UAVs have played an increasingly important role in the military and civilian fields, especially UAVs swarm with high mission execution and diverse execution scenarios. The operator should have high proficiency in manipulation due to the complexity of UAV swarm, and cognitive analysis is an efficacious method used to train and assess operators of UAV swarm. This paper put forward an UAV swarm simulation system for operator cognitive analysis. First the requirements of simulation system for operator cognitive analysis is introduced. Subsequently the architecture and detailed module design of simulation system is proposed. Finally, the result of vehicle searching mission simulation and EEG data of operator verify the effectiveness of the simulation system.
The extreme attention state is one of the cognitive states and it is extremely important for cluster operators due to the diversity and complexity of tasks. However, existing research tends to be more theoretical and there is relatively little research on extreme attention states. Therefore, we combine theoretical research with practical cluster drone control task to design our experimental paradigm. We used machine learning method to build classifiers and all of these classifiers achieved good results, which validates the rationality of our method. Finally, we choose the support vector machine (SVM) as our classifier due to the excellent result.
KEYWORDS: Telecommunications, Control systems, Power supplies, Computing systems, Data communications, Unmanned aerial vehicles, Safety, Cooling systems, Inspection, Error control coding
Based on the power management system of a medium-sized unmanned helicopter, an automatic test system is designed and implemented. Aiming at the shortcomings of ordinary test methods, such as low efficiency, poor accuracy and poor security, this paper discusses the design and implementation of an automatic test system for power management system from both software and hardware aspects and give the specific functions and implementation methods. The embedded system based on STM32F427 is adopted, and the host computer software runs on the Windows platform and is developed in the Microsoft Visual studio integrated development environment. The automatic test system realizes the full-function automatic test of the communication of the power management system, power supply control, voltage detection, current detection, power supply abnormal handling, and power consumption abnormal handling. Compared with ordinary detection methods, this automatic test system shortens the test time, improves the test efficiency and accuracy, and ensures the safety of equipment and personnel.
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