为满足电网对输变电设备检修工作票的高可靠性要求,消除因工作票误填而导致的电网安全事故,文章提出基于分层有限状态机(hierarchical finite state machine,HFSM)的检修工作票智能防误技术,从而降低因信息误填引起的电网潜在风险,提高...为满足电网对输变电设备检修工作票的高可靠性要求,消除因工作票误填而导致的电网安全事故,文章提出基于分层有限状态机(hierarchical finite state machine,HFSM)的检修工作票智能防误技术,从而降低因信息误填引起的电网潜在风险,提高电网运行安全性和可靠性。首先基于有限状态机理论和分层技术建立分层有限状态模型,提高逻辑分析效率;其次建立基于分层有限状态机的检修工作票防误模型,其中外层为数据信息防误层和安全逻辑分析层,内层为有限状态机流转状态;然后提出用以刻画误填状态频次的防误状态概率指标,从而降低潜在的工作票误填风险;最后通过设计的算例验证该文所提模型的有效性和可行性。展开更多
This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous drivi...This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.69873035.60273026(国家自然科学基金)the K.C.Wong Education FoundationHong Kong(王宽诚教育基金)
文摘为满足电网对输变电设备检修工作票的高可靠性要求,消除因工作票误填而导致的电网安全事故,文章提出基于分层有限状态机(hierarchical finite state machine,HFSM)的检修工作票智能防误技术,从而降低因信息误填引起的电网潜在风险,提高电网运行安全性和可靠性。首先基于有限状态机理论和分层技术建立分层有限状态模型,提高逻辑分析效率;其次建立基于分层有限状态机的检修工作票防误模型,其中外层为数据信息防误层和安全逻辑分析层,内层为有限状态机流转状态;然后提出用以刻画误填状态频次的防误状态概率指标,从而降低潜在的工作票误填风险;最后通过设计的算例验证该文所提模型的有效性和可行性。
基金funded by Chongqing Science and Technology Bureau (No.cstc2021jsyj-yzysbAX0008)Chongqing University of Arts and Sciences (No.P2021JG13)2021 Humanities and Social Sciences Program of Chongqing Education Commission (No.21SKGH227).
文摘This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.
