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Deep Learning Model Based Behavioural Recognition Technology for Electricity Operators and Its Safety Guardianship Analysis


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Adumene, S., Islam, R., Amin, M. T., Nitonye, S., Yazdi, M., & Johnson, K. T. (2022). Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships. Ocean engineering, (251-May 1). Search in Google Scholar

Kaur, RK, Singh, LK, Pandey, & B. (2017). Security analysis of safety critical and control systems: a case study of a nuclear power plant system. Nucl Technol. Search in Google Scholar

Yang, Y., & Yao, L. (2021). Optimization method of power equipment maintenance plan decision-making based on deep reinforcement learning. Mathematical Problems in Engineering. Search in Google Scholar

Wan, W., Liu, Y., Han, X., & Wang, H. (2021). Evaluation model of power operation and maintenance based on text emotion analysis. Mathematical Problems in Engineering, 2021. Search in Google Scholar

Qian, F., Liu, Y., Yang, Y., Gao, W., & Wu, Y. (2018). Equipment operation and maintenance management of shanghai power distribution network after power system reform. Energy Procedia, 152, 1182-1187. Search in Google Scholar

Wu, G., Yu, M., Shi, W., Li, S., & Bao, J. (2020). Image recognition in online monitoring of power equipment. International Journal of Advanced Robotic Systems, 17(1), 172988141990083-. Search in Google Scholar

Li, J., & Wen, Y. Y. (2022). Association analysis of human error causes of electric shock construction accidents in china. Archives of civil engineering. Search in Google Scholar

Yu, K. S., & Kim, J. M. (2018). A study on the form of electric shock accident using swiss cheese model. Transactions of the Korean Institute of Electrical Engineers, (12). Search in Google Scholar

Chae, D. J., Kim, J. H., Lim, Y. B., Kim, D. W., & Lim, S. T. (2020). A study on the grounding environment for securing electrical safety in wireless power transfer system for electric transportation. Transactions of the Korean Institute of Electrical Engineers, 69(6), 947-954. Search in Google Scholar

Jalil, B., Leone, G. R., Martinelli, M., Moroni, D., & Berton, A. (2019). Fault detection in power equipment via an unmanned aerial system using multi modal data. Sensors, 19(13), 3014. Search in Google Scholar

Yang, J., & Kim, J. (2020). Accident diagnosis algorithm with untrained accident identification during power-increasing operation. Reliability Engineering and System Safety, 202. Search in Google Scholar

Li, W. L. (2019). An intelligent identification algorithm for obtaining the state of power equipment in sift-based environments. International Journal of Performability Engineering, 15(9). Search in Google Scholar

Wu, G., Mao, Y., He, Y., Lu, Y., Jia, M., & Fu, S. (2022). Safety archetypes identification and behavior simulation for nuclear power plant operation human reliability improvement. Annals of nuclear energy(Sep.), 174. Search in Google Scholar

Huaishuo, Xiao, Jianchun, Wei, Hongshun, & Liu, et al. (2017). Identification method for power system low-frequency oscillations based on improved vmd and teager–kaiser energy operator. IET Generation, Transmission & Distribution, 11(16), 4096-4103. Search in Google Scholar

Zhang, X., Yan, W., Lu, Z., Tan, H., & Li, H. (2023). Bad data identification for power systems state estimation based on data-driven and interval analysis. Electric Power Systems Research, 217, 109088-. Search in Google Scholar

Peng, MJ, Liu, YK, Jiang, & Wang, et al. (2018). Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized pca method. ANN NUCL ENERGY, 2018,113(-), 105-117. Search in Google Scholar

Lope, J. D., & Graa, M. (2020). Behavioral activity recognition based on gaze ethograms. International Journal of Neural Systems. Search in Google Scholar

Duong, H. T., Le, V. T., & Hoang, V. T. (2023). Deep learning-based anomaly detection in video surveillance: a survey. Sensors (Basel, Switzerland), 23. Search in Google Scholar

Benabderrahmane, S., Mellouli, N., & Lamolle, M. (2018). On the predictive analysis of behavioral massive job data using embedded clustering and deep recurrent neural networks. Knowledge-Based Systems, 151(JUL.1), 95-113. Search in Google Scholar

Fang, C., Xiang, H., Leng, C., Chen, J., & Yu, Q. (2022). Research on real-time detection of safety harness wearing of workshop personnel based on yolov5 and openpose. Sustainability, 14. Search in Google Scholar

Wang, H., Zhang, Y., Liu, W., Gu, X., & Liu, Z. (2021). A novel gcn-based point cloud classification model robust to pose variances. Pattern Recognition(19), 108251. Search in Google Scholar

eISSN:
2444-8656
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik