两步纯追踪航向校正模型算法

为了提高农业机械导航精度,在纯追踪算法的基础上,设计了两步纯追踪航向校正模型算法,通过校正车体的航 向角,减小车体的横向误差。在MATLAB环境下仿真结果表明,该算法比纯追踪算法的精度高,最大误差和标准差均有 所减小。在4轮电动车上,搭建液压系统和卫星接收系统,进行现场试验,验证了两步纯追踪航向校正模型算法的可行 性和有效性。     

ARM运动控制平台下高精度高速插补算法的研究

采用ARM嵌入式微处理器,结合MCX314运动控制芯片,构建成可独立运行的嵌入式运动系统。运用了型值点S型曲线加减速前瞻控制算法,根据加工路径的型值点的实际情况,确定每一型值点处的最大衔接速度,采用S型曲线加减速控制,实现各路径段之间进给速度的快速衔接,从而达到高速高精的运动控制目的。     

Bayesian sequential analysis for multiple-arm clinical trials

Use of full Bayesian decision-theoretic approaches to obtain optimal stopping rules for clinical trial designs typically requires the use of Backward Induction. However, the implementation of Backward Induction, apart from simple trial designs, is generally impossible due to analytical and computational difficulties. In this paper we present a numerical approximation of Backward Induction in a multiple-arm clinical trial design comparing k experimental treatments with a standard treatment where patient response is binary. We propose a novel stopping rule, denoted by τ ^p , as an approximation of the optimal stopping rule, using the optimal stopping rule of a single-arm clinical trial obtained by Backward Induction. We then present an example of a double-arm (k=2) clinical trial where we use a simulation-based algorithm together with τ ^p to estimate the expected utility of continuing and compare our estimates with exact values obtained by an implementation of Backward Induction. For trials with more than two treatment arms, we evaluate τ ^p by studying its operating characteristics in a three-arm trial example. Results from these examples show that our approximate trial design has attractive properties and hence offers a relevant solution to the problem posed by Backward Induction.