A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.     

基于统计证据的半监督多分类器融合方法

针对半监督学习中未标记示例导致性能下降的问题,提出一种新的协同训练算法LDL-tri-training.首先通过最小显著性差异（LSD）假设检验方法使得3个成员分类器两两之间具有显著性差异;然后采用D-S证据理论提高标注的稳定性;最后利用局部异常因子检测算法剔除误标记的噪声样本.实验表明,与其他方法相比,LDL-tri-training算法具有较高的分类精度和稳定性.     

改进的人工蜂群算法性能

为克服人工蜂群算法容易陷入局部最优解的缺点,提出一种新的改进型人工蜂群算法。首先,利用数学中的外推技巧定义了新的位置更新公式,由此构造出一种具有引导趋势的蜂群算法;其次,为了克服算法在进化后期位置相似度高、更新速度慢的缺陷,将微调机制引入算法中,讨论摄动因子范围,由此提高算法在可行区域内的局部搜索能力。最后通过3个基准函数仿真测试,结果表明：与常规算法相较,改进后在搜索性能和精度方面均有明显提高。     

运钞车车辆路径规划策略

针对实际运钞网点数每天动态变化问题,提出一种先划分、再优化的动态运钞车路线规划策略。第一阶段先采用Dijkstra算法求出两点之间的最短路径,再利用最近邻算法和均衡工作量因子求出动态需求车辆的车辆数和每条路径上的网点;第二阶段利用前置交叉的改进遗传算法,分别优化每条路径并求出每条路径上的网点顺序,获得距离最短和时间最少的路径。实验结果表明,该策略能有效解决车辆数目和路径根据需求动态变化的问题,达到节约和合理利用资源的目的。     

基于多尺度粗糙集模型的决策树优化算法

针对经典决策树算法构造的决策树结构复杂、缺乏对噪声数据适应能力等局限性,基于多尺度粗糙集模型提出一种新的决策树构造算法。算法引入尺度变量和尺度函数概念,采用不同尺度下近似分类精度选择测试属性构造决策树,使用抑制因子对决策树进行修剪,有效地去除了噪声规则。结果表明该算法构造的决策树简单有效,对噪声数据有一定的抗干扰性,且能满足不同用户对决策精度的要求。     

D-S证据理论中一种新的冲突证据融合方法

针对D-S证据理论在融合高度冲突的证据时可能导致与直观结果相悖的问题,通过分析现有的几种冲突证据融合方法,提出了一种有效处理冲突证据的融合方法。该方法综合了模型修正和规则修正这两类方法的优点。首先基于冲突系数矩阵计算证据的可信度,并以此作为折扣因子来修正证据结构,然后基于局部冲突、局部分配原则确定证据合成时的冲突分配空间和分配权重。实验结果表明,该方法对合理的合成方向具有较快的收敛速度,同时降低了决策风险。     

高压电网微机功率因数自动补偿装置的研究

介绍一种自行研制的高压电网功率因数自动补偿装置。实际运行表明 :该装置能实时跟踪补偿 ,运行稳定可靠 ,节能效果显著。     

液力变矩器控制系统的特性与应用

液力控制技术是近年来发展起来的一个新的技术领域。文章阐述了液力变矩器控制系统目前实用的几种组成方法和特性 ,这有助于设计液力控制系统并拓展其应用领域。     

人—机工程系统CAD精度及计算机仿真置信度研究

提出了一个典型的人－机工程系统CAD精度及计算机仿真置信度实验研究模型,通过对同一数据的物理模型和计算机模型的测量数据和计算数据进行计算和分析,定量给出CAD精度和计算机仿真置信度,研究结果表明,对于由人控制、操作的机械系统,用CAD和计算机仿真代替实物模型的物理模型,既可以在制造之前进行工效预测,也可以对现有系统进行性能评估。     

Ensemble of Independent Factor Analyzers with Application to Natural Image Analysis

In this paper the ensemble of independent factor analyzers (EIFA) is proposed. This new statistical model assumes that each data point is generated by the sum of outputs of independently activated factor analyzers. A maximum likelihood (ML) estimation algorithm for the parameter is derived using a Monte Carlo EM algorithm with a Gibbs sampler. The EIFA model is applied to natural image data. With the progress of the learning, the independent factor analyzers develop into feature detectors that resemble complex cells in mammalian visual systems. Although this result is similar to the previous one obtained by independent subspace analysis, we observe the emergence of complex cells from natural images in a more general framework of models, including overcomplete models allowing additive noise in the observables.