基于Black-burst的多跳广播方法在车联网环境中能够有效地传播紧急消息,该类方法利用车辆间的Black-burst交互,通过迭代分区快速缩小最佳候选车辆的竞争范围,从而实现减少竞争冲突和提高紧急消息传播速度的目的.然而,现有这类方法的迭...基于Black-burst的多跳广播方法在车联网环境中能够有效地传播紧急消息,该类方法利用车辆间的Black-burst交互,通过迭代分区快速缩小最佳候选车辆的竞争范围,从而实现减少竞争冲突和提高紧急消息传播速度的目的.然而,现有这类方法的迭代分区机制较为固定,没有考虑车流密度对分区机制效果的影响.针对上述问题,本文提出了一种基于动态迭代分区机制的多跳广播(Dynamic and Iterative Partitioning Scheme based Multi-hop Broadcast,DIPS-MB)方法,该方法首先估算当前的车流密度,并利用Black-burst交互确定最佳的迭代分区机制,在尽可能减少冲突区域的前提下缩小了寻找最佳中继车辆的时间.数学分析和仿真实验验证了DIPS-MB方法的有效性.与同类型方法进行对比,基于DIPS-MB的紧急消息在动态车流环境中具有更小的单跳时延和更快的传播速度.展开更多
A Newton iteration-based interval uncertainty analysis method(NI-IUAM) is proposed to analyze the propagating effect of interval uncertainty in multidisciplinary systems. NI-IUAM decomposes one multidisciplinary syste...A Newton iteration-based interval uncertainty analysis method(NI-IUAM) is proposed to analyze the propagating effect of interval uncertainty in multidisciplinary systems. NI-IUAM decomposes one multidisciplinary system into single disciplines and utilizes a Newton iteration equation to obtain the upper and lower bounds of coupled state variables at each iterative step.NI-IUAM only needs to determine the bounds of uncertain parameters and does not require specific distribution formats. In this way, NI-IUAM may greatly reduce the necessity for raw data. In addition, NI-IUAM can accelerate the convergence process as a result of the super-linear convergence of Newton iteration. The applicability of the proposed method is discussed, in particular that solutions obtained in each discipline must be compatible in multidisciplinary systems. The validity and efficiency of NI-IUAM is demonstrated by both numerical and engineering examples.展开更多
By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The...By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.展开更多
文摘基于Black-burst的多跳广播方法在车联网环境中能够有效地传播紧急消息,该类方法利用车辆间的Black-burst交互,通过迭代分区快速缩小最佳候选车辆的竞争范围,从而实现减少竞争冲突和提高紧急消息传播速度的目的.然而,现有这类方法的迭代分区机制较为固定,没有考虑车流密度对分区机制效果的影响.针对上述问题,本文提出了一种基于动态迭代分区机制的多跳广播(Dynamic and Iterative Partitioning Scheme based Multi-hop Broadcast,DIPS-MB)方法,该方法首先估算当前的车流密度,并利用Black-burst交互确定最佳的迭代分区机制,在尽可能减少冲突区域的前提下缩小了寻找最佳中继车辆的时间.数学分析和仿真实验验证了DIPS-MB方法的有效性.与同类型方法进行对比,基于DIPS-MB的紧急消息在动态车流环境中具有更小的单跳时延和更快的传播速度.
基金supported by the National Natural Science Foundation of China(Grant No.11602012)the 111 Project(Grant No.B07009)+1 种基金the Defense Industrial Technology Development Program(Grant No.JCKY2016601B001)and the China Postdoctoral Science Foundation(Grant No.2016M591038)
文摘A Newton iteration-based interval uncertainty analysis method(NI-IUAM) is proposed to analyze the propagating effect of interval uncertainty in multidisciplinary systems. NI-IUAM decomposes one multidisciplinary system into single disciplines and utilizes a Newton iteration equation to obtain the upper and lower bounds of coupled state variables at each iterative step.NI-IUAM only needs to determine the bounds of uncertain parameters and does not require specific distribution formats. In this way, NI-IUAM may greatly reduce the necessity for raw data. In addition, NI-IUAM can accelerate the convergence process as a result of the super-linear convergence of Newton iteration. The applicability of the proposed method is discussed, in particular that solutions obtained in each discipline must be compatible in multidisciplinary systems. The validity and efficiency of NI-IUAM is demonstrated by both numerical and engineering examples.
基金supported by General Program (60774022)State Key Program (60834001) of National Natural Science Foundation of ChinaDoctoral Foundation of Qingdao University of Science & Technology (0022324)
文摘By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.
