Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human f...Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.展开更多
叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier t...叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier transform,FFT)算法提取动叶片异步振动幅值和异步振动频率的非整数阶次;随后,改进现有多信号分类(multiple signal classification,MUSIC)算法,提出基于阶次搜索-多信号分类(engine order search-based multiple signal classification,EOS-MUSIC)算法的动叶片异步振动频率整数阶次搜索策略;最后,融合EOS-MUSIC算法与FFT算法分析结果辨识动叶片异步振动参数。基于MATLAB软件仿真动叶片异步振动信号,与现有MUSIC算法比较,验证了EOS-MUSIC算法的可信性和准确性。在离心压气机试验台开展叶轮叶片振动试验,与应变片法相比,EOS-MUSIC算法频率辨识绝对误差为3.36 Hz,相对误差仅为0.53%。本文在FFT算法预处理的基础上,通过阶次搜索辨识动叶片异步振动参数,克服了现有MUSIC算法搜索周期长和辨识精度低的难题,为动叶片异步振动参数辨识提供了理论支撑。展开更多
为提升高速公路合流区交通运行效率及驾乘人员舒适性,在保障安全的前提下,面向人工驾驶车辆(Human Driven Vehicles,HDV)和智能网联车辆(Connected and Autonomous Vehicles,CAV)混行的异质交通流环境,提出高速公路CAV合流次序优化与轨...为提升高速公路合流区交通运行效率及驾乘人员舒适性,在保障安全的前提下,面向人工驾驶车辆(Human Driven Vehicles,HDV)和智能网联车辆(Connected and Autonomous Vehicles,CAV)混行的异质交通流环境,提出高速公路CAV合流次序优化与轨迹规划方法.首先,以车辆通行时间和延误作为合流区交通运行效率表征指标,建立合流次序优化函数,采用并调整蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)算法,获得最优合流次序;其次,依据合流次序,建立最小化加速度和急动度的CAV合流轨迹规划(Minimize Acceleration and Jerk Trajectory Planning,MAJTP)函数,运用最优控制理论,求解车辆纵向最优轨迹解析解,进而形成高速公路合流区CAV协同控制方法;最后,联合运用SUMO软件和PYTHON库,对本文所提方法进行交通仿真验证.仿真结果表明:在CAV渗透率分别为0.2、0.4、0.6和0.8时,相较于先进先出(First In First Out,FIFO)算法,基于MCTS算法的合流次序优化方法累积延误分别降低5.75%、8.84%、12.24%和11.06%;相较于最小化加速度的车辆轨迹规划(Minimize Acceleration Trajectory Planning,MATP)方法,MAJTP方法平均急动度更趋近于零,驾乘人员舒适性有所提升,验证了方法的有效性.研究成果可为高速公路合流区交通运行管控研究提供理论支持.展开更多
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Foundation of Air Force Engineering University(KGD08101604)
文摘Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.
文摘叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier transform,FFT)算法提取动叶片异步振动幅值和异步振动频率的非整数阶次;随后,改进现有多信号分类(multiple signal classification,MUSIC)算法,提出基于阶次搜索-多信号分类(engine order search-based multiple signal classification,EOS-MUSIC)算法的动叶片异步振动频率整数阶次搜索策略;最后,融合EOS-MUSIC算法与FFT算法分析结果辨识动叶片异步振动参数。基于MATLAB软件仿真动叶片异步振动信号,与现有MUSIC算法比较,验证了EOS-MUSIC算法的可信性和准确性。在离心压气机试验台开展叶轮叶片振动试验,与应变片法相比,EOS-MUSIC算法频率辨识绝对误差为3.36 Hz,相对误差仅为0.53%。本文在FFT算法预处理的基础上,通过阶次搜索辨识动叶片异步振动参数,克服了现有MUSIC算法搜索周期长和辨识精度低的难题,为动叶片异步振动参数辨识提供了理论支撑。
文摘为提升高速公路合流区交通运行效率及驾乘人员舒适性,在保障安全的前提下,面向人工驾驶车辆(Human Driven Vehicles,HDV)和智能网联车辆(Connected and Autonomous Vehicles,CAV)混行的异质交通流环境,提出高速公路CAV合流次序优化与轨迹规划方法.首先,以车辆通行时间和延误作为合流区交通运行效率表征指标,建立合流次序优化函数,采用并调整蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)算法,获得最优合流次序;其次,依据合流次序,建立最小化加速度和急动度的CAV合流轨迹规划(Minimize Acceleration and Jerk Trajectory Planning,MAJTP)函数,运用最优控制理论,求解车辆纵向最优轨迹解析解,进而形成高速公路合流区CAV协同控制方法;最后,联合运用SUMO软件和PYTHON库,对本文所提方法进行交通仿真验证.仿真结果表明:在CAV渗透率分别为0.2、0.4、0.6和0.8时,相较于先进先出(First In First Out,FIFO)算法,基于MCTS算法的合流次序优化方法累积延误分别降低5.75%、8.84%、12.24%和11.06%;相较于最小化加速度的车辆轨迹规划(Minimize Acceleration Trajectory Planning,MATP)方法,MAJTP方法平均急动度更趋近于零,驾乘人员舒适性有所提升,验证了方法的有效性.研究成果可为高速公路合流区交通运行管控研究提供理论支持.
