Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(...Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.展开更多
The road random torsional excitation is one type of torque rooted from the road roughness and vehicle drive system. This paper aims to study how the road random torsional excitation affects the dynamic characteristics...The road random torsional excitation is one type of torque rooted from the road roughness and vehicle drive system. This paper aims to study how the road random torsional excitation affects the dynamic characteristics of vehicle power train. The method of simulating the random torsional excitation of tracked vehicle is explored at first. Secondly,the road random torsional excitations under different road roughness,vehicle speeds and pre-tensions are obtained. Thirdly,the dynamic analysis model of tracked vehicle power train is constructed with the consideration of the road random torsional excitation. Eventually,the influences of this excitation on output torque,bearing support force,vibration acceleration and dynamic shear stress of transmission shafts are intensively studied.The research conclusions are helpful to correct and refine the present virtual prototype of tracked vehicle power train.展开更多
Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mine...Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.展开更多
Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy ...Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this research.In general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data detection.In this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is analyzed.The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector.Analysis and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.展开更多
The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In...The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In this paper, an optical motion capture system based on the virtual reality technology is proposed to meet the requirements of the power enterprise for the qualified business ability. Electric power equipment, power equipment model entitative operating environment and the human model are established by electric power simulation unit, ZigBee technology and OpenGL graphics library. The problem of missing feature points is solved by applying the human model driven algorithm and the Kalman filtering algorithm. The experimental results show that it is more accurate to use Kalman filtering algorithm to extract the feature point in tracking process of actual motion capture and real-time animation display. The average absolute error of 3D coordinates is 1.61 mm and the average relative error is 2.23%. The system can improve trainees’ sense of experience and immersion.展开更多
An approach of training working staff of power system operation mode based on state evaluation is proposed. In terms of knowledge features of power system operation mode, we studied a training method based on evaluati...An approach of training working staff of power system operation mode based on state evaluation is proposed. In terms of knowledge features of power system operation mode, we studied a training method based on evaluation of learning state. This training method makes individual learning for different individual condition to give them ability to grasp learning points quickly, evaluate real-time learning effect, update learning style in time and summarize problems after one learning stage, so that learners can master professional knowledge in constant summaries and feedbacks. Obvious effects can be obtained on discontinuous learning time that trainees can master basic theories associated with their working and operations adapted to practical work quickly.展开更多
The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, know...The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, knowledge representation and the method to establish device graphical library for expert system are given, the fault setting and diagnosis for training and simulation as well as restoration technology with deep first searching arithmetic and heuristic inference are presented. The research provides a good base for developing the training, simulation, restoration system of power companies.展开更多
基金supportted by Natural Science Foundation of Jiangsu Province(No.BK20230696).
文摘Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.
基金National Natural Science Foundations of China(Nos.51405410,51505402)
文摘The road random torsional excitation is one type of torque rooted from the road roughness and vehicle drive system. This paper aims to study how the road random torsional excitation affects the dynamic characteristics of vehicle power train. The method of simulating the random torsional excitation of tracked vehicle is explored at first. Secondly,the road random torsional excitations under different road roughness,vehicle speeds and pre-tensions are obtained. Thirdly,the dynamic analysis model of tracked vehicle power train is constructed with the consideration of the road random torsional excitation. Eventually,the influences of this excitation on output torque,bearing support force,vibration acceleration and dynamic shear stress of transmission shafts are intensively studied.The research conclusions are helpful to correct and refine the present virtual prototype of tracked vehicle power train.
文摘Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.
基金the National Natural Science Foundation of China(NSFC)(No.60472089)
文摘Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this research.In general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data detection.In this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is analyzed.The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector.Analysis and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.
文摘The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In this paper, an optical motion capture system based on the virtual reality technology is proposed to meet the requirements of the power enterprise for the qualified business ability. Electric power equipment, power equipment model entitative operating environment and the human model are established by electric power simulation unit, ZigBee technology and OpenGL graphics library. The problem of missing feature points is solved by applying the human model driven algorithm and the Kalman filtering algorithm. The experimental results show that it is more accurate to use Kalman filtering algorithm to extract the feature point in tracking process of actual motion capture and real-time animation display. The average absolute error of 3D coordinates is 1.61 mm and the average relative error is 2.23%. The system can improve trainees’ sense of experience and immersion.
文摘An approach of training working staff of power system operation mode based on state evaluation is proposed. In terms of knowledge features of power system operation mode, we studied a training method based on evaluation of learning state. This training method makes individual learning for different individual condition to give them ability to grasp learning points quickly, evaluate real-time learning effect, update learning style in time and summarize problems after one learning stage, so that learners can master professional knowledge in constant summaries and feedbacks. Obvious effects can be obtained on discontinuous learning time that trainees can master basic theories associated with their working and operations adapted to practical work quickly.
基金TheKeyProblemTacklingProjectinHunanProvince! (No .Izf 9831)
文摘The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, knowledge representation and the method to establish device graphical library for expert system are given, the fault setting and diagnosis for training and simulation as well as restoration technology with deep first searching arithmetic and heuristic inference are presented. The research provides a good base for developing the training, simulation, restoration system of power companies.
