IVI.NET仪器驱动及其应用研究

IVI.NET是继IVI-C和IVI-COM之后的新一代IVI驱动标准,它继承了.NET的诸多优点并拓展了传统IVI的功能.研究了IVI.NET仪器驱动实现仪器互换的基本原理,分析了基于IVI.NET仪器驱动开发自动测试系统软件的优势,并以直流电源为例详细说明了开发和使用IVI.NET仪器驱动的方法和步骤.实践表明,IVI.NET仪器驱动在分布式的网络环境下开发,且使用高效,能够便捷地实现测试仪器的互换,对ATS软件开发人员具有一定的借鉴意义.     

基于杂合支持向量回归机的电子装备故障预测

针对电子装备性能特征参数间的耦合关联问题,提出一种基于杂合支持向量回归机的电子装备故障预测方法。运用D-S证据理 论,结合参数的纵向历史状态数据和横向的相关参数数据,设计杂合支持向量回归机预测算法,利用特征参数的时间相关性和空间相关性提高预测精度。实验结果表明,相对于单独使用纵向或者横向的支持向量回归机,该方法具有更高的精度,可有效地对复杂电子装备实施故障预测。     

Parallel Test Tasks Scheduling and Resources Configuration Based on GA-ACA

A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.     

基于神经网络的故障诊断效能模糊综合评价研究

构建了并行测试系统(PATS)故障诊断效能评价指标体系,针对该指标体系在评价过程中存在的不确定因素,结合神经网络和模糊理论的优点,提出了基于神经网络的模糊综合评价方法(FCEANN);详细给出了FCEANN的求解算法及步骤,结合实例进行仿真实验并与专家评价结果进行比较,对影响模型预测精度的因素进行了分析;仿真结果表明,基于BP神经网络和Elman神经网络的FCEANN均能够很好地模拟专家评价的全过程,能够准确地对PATS的故障诊断效能指标进行评价;并将两种神经网络的仿真结果进行了比较,结果表明,Elman神经网络得到的仿真结果精度更高.