液压系统油液污染度模糊故障诊断方法

文章利用模糊数学在描述油液污染与机器磨损的模糊关系中的优势,建立了模糊故障诊断的数学模型,并在现有的污染度模糊故障诊断的基础上,通过故障征兆空间的压缩和重构,大大减少了计算量,提高了故障诊断的效率。     

基于Petri网的地铁信号系统可靠性分析

地铁信号系统是地铁系统的中枢神经,同时也是提高地铁运营水平的重要保障。在分析信号系统功能及故障类型的基础上,划分了设备重要度水平。通过地铁信号系统Petri网5大对象子类框架的搭建,分析研究了信号系统重要设备发生故障的情况下,系统各子类的动态Petri网可靠性模型。     

基于Petri-马尔科夫链的中老铁路通关模式研究

中老铁路作为新开通的跨境运输铁路，为了提高通关效率，研究其通关模式十分重要。分析已有的通关模式，先确定中老铁路口岸站采用“一地两检”的模式，再以货运通关流程为例，构建Petri-马尔科夫链模型，通过建立基本Petri网、随机Petri网以及同构的有穷马尔可夫链，最后求解得到稳定概率及系统的性能参数，根据计算结果验证通关模式的合理性以及发现通关模式中的可优化环节。     

物元Petri网在公路工程质量监理中的应用

公路是人们出行的必需通道,要保证公路运营的安全,就要求相关部门对公路工程质量进行有效的监理。影响公路工程质量的因素主要包括监理控制体系不完整、监理过程不严格、领导决策不合适、设计人员的设计不到位等。基于以上因素,对公路工程的质量监理提出了一种新的方法,即利用物元Petri网技术对公路进行监理控制,并通过研究如何建立基于物元Petri网技术的公路监理模型,对公路监理进行了深入的阐释。     

铁路货运装车作业流程仿真与优化

铁路货运装车作业是铁路货运业务的重要环节,其作业速度对铁路货物运输效率有着重要影响,需要对铁路货运作业流程进行优化。本文从铁路货运装车作业出发,分析了目前我国铁路货运装车作业的流程现状,并以整车运输为例,建立相应的Petri网模型,利用Exspect仿真软件对优化前后的模型进行仿真,并对装车作业流程优化以后的效果进行量化评价,验证了优化方案的有效性。     

基于Petri网的城市交通应急管理系统分析

交通应急管理系统是交通安全、可靠性运营的保障,是交通发展战略规划中的重要研究内容。为了能对交通应急管理系统进行定量分析,文章从定性的角度分析了影响交通应急管理的因素,建立了城市交通应急管理系统的Petri模型,并利用随机petri网和模糊集理论对建立的模型进行性能分析,同时根据库所繁忙概率和变迁利用率确定影响应急系统性能的薄弱环节,提出提高该交通应急管理系统性能的改善措施。     

船厂管加工车间生产计划数字化仿真研究

本文就船厂管加工车间的生产计划管理,提出了对船舶管系托盘,在车间计划任务的管理上依据管径大小按类划分加工路线的分组方法,在加工完成后,依据管子的命名规则,进行聚类分检,重新归类托盘的方法。研究了在管子加工的过程中,利用工作抽样法、模特法等获取各加工通道上自动弯管设备和手工拼接焊管加工过程,每个工种系列工步的正常作业时间;以及利用Petri网合理地规划各加工通道上AGV小车的工作路径,并将依据每个工种系列工步的正常作业时间制定好的生产计划赋予各加工通道上的AGV小车,进行管路的生产仿真,验证计划的合理性,从而得出的设备负荷和劳动力负荷对生产计划的影响,以及合理调整计划的方法。     

神经网络在输油泵故障诊断中的应用

人工神经网络是模仿人脑神经元结构、特性和大脑认知功能而构成的新型信号、信息处理系统。本文采用Levenberg Marquardt算法建立了输油泵故障诊断的人工神经网络模型 ,以输油泵常见故障现象作为网络的输入模式 ,对输油泵常见的故障进行分类训练 ,并应用于待识别故障样本的识别计算。结果表明该方法在输油泵故障诊断中是有效的。     

区间层次分析法在电控发动机故障诊断中的应用

为了改进层次分析法在电控发动机怠速不良故障诊断应用中的缺陷,文章引入区间层次分析法,两两比较故障因素,计算区间数权重,并利用基于可能度的区间判断矩阵排序法,给出了与参考文献完全相同的排序结果,较好地说明了区间层次分析法在电控发动机故障诊断中应用的可行性。     

山区公路涵洞质量综合评价方法

本文基于模糊理论中模糊综合评判法研究公路涵洞安全质量体系.首先确定影响因素的层次结构模型和评价指标体系;然后建立模糊综合评判的数学模型,计算各层因素权重和隶属度,得出单因素评判矩阵,进行公路涵洞安全质量模糊综合评判.     

Calibrating the membership functions of the fuzzy inference system: instantiated by car-following data

The fuzzy rule based inference is known to be a useful tool to capture the behavior of an approximate system in transportation. One of the obstacles of implementing the fuzzy rule based inference, however, has been to calibrate the membership functions of the fuzzy sets used in the rules. This paper proposes a way to calibrate the membership function when a set of input and output data is given for the system. First, the mathematical operations of the fuzzy rule based inference system are represented by a neural network construction. The operations of each node of this neural network are designed so that they correspond to specific logical operations of the fuzzy rule based inference system. The values of the weights of this neural network are set to correspond to the parameters that control the shape and location of each membership function. Second, given a set of input–output data, the weights are corrected sequentially using the principle of the generalized delta rule based back-propagation mechanism. After correction, the values of the weights are used to specify the exact shape of the membership functions of the fuzzy sets in the rules. The procedure implements a set of logical rules that can be applied when calibrating the shapes of the membership functions of a fuzzy inference system. An example, in which the membership functions of a fuzzy inference model for car-following behavior are calibrated using the real world data, is shown.     

Coloured Petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions

The Traffic Alert and Collision Avoidance System (TCAS) is a world-wide accepted last-resort means of reducing the probability and frequency of mid-air collisions between aircraft. Unfortunately, it is widely known that in congested airspace, the use of the TCAS may actually lead to induced collisions. Therefore, further research regarding TCAS logic is required. In this paper, an encounter model is formalised to identify all of the potential collision scenarios that can be induced by a resolution advisory that was generated previously by the TCAS without considering the downstream consequences in the surrounding traffic. The existing encounter models focus on checking and validating the potential collisions between trajectories of a specific scenario. In contrast, the innovative approach described in this paper concentrates on quantitative analysis of the different induced collision scenarios that could be reached for a given initial trajectory and a rough specification of the surrounding traffic. This approach provides valuable information at the operational level. Furthermore, the proposed encounter model can be used as a test-bed to evaluate future TCAS logic changes to mitigate potential induced collisions in hot spot volumes. In addition, the encounter model is described by means of the coloured Petri net (CPN) formalism. The resulting state space provides a deep understanding of the cause-and-effect relationship that each TCAS action proposed to avoid an actual collision with a potential new collision in the surrounding traffic. Quantitative simulation results are conducted to validate the proposed encounter model, and the resulting collision scenarios are summarised as valuable information for future Air Traffic Management (ATM) systems.     

城市燃气管道第三方损坏的评价方法

第三方损坏评价是指对由于各种外力作用而使管道受损的可能性进行分级.根据影响第三方损坏因素的情况,采用故障树分析法和模糊综合评判法相结合的数学模型进行评价.通过故障树分析,得到引起第三方损坏的各种因素的重要度.再将之作为模糊综合评判法的权重向量,求得第三方损坏的综合评判向量,最后按照最大隶属度原则确定各管段第三方损坏可能性的等级.     

A rule-based neural network approach to model driver naturalistic behavior in traffic

This paper proposes a rule-based neural network model to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning method reinforcement learning is used to train the neural network such that the neural network can mimic driving behavior of individual drivers. Vehicle actions by neural network are compared to actions from naturalistic data. Furthermore, this paper applies the proposed method to analyze the heterogeneities of driving behavior from different drivers’ data.Driving data in the two driving situations are extracted from Naturalistic Truck Driving Study and Naturalistic Car Driving Study databases provided by the Virginia Tech Transportation Institute according to pre-defined criteria. Driving actions were recorded in instrumented vehicles that have been equipped with specialized sensing, processing, and recording equipment.     

层次分析法在铁路运营隧道健康状态综合评判中的应用

影响铁路运营隧道健康状态的因素众多,并且具有模糊性.按照指标体系中的评价指标的选择原则,在对影响铁路运营隧道结构安全的因素进行分析的基础上,从铁路运营隧道健康诊断研究的需要出发.构建评价指标体系.采用10/10～18/2标度法确定评价指标的权重,运用岭形函数计算评价指标的隶属度矩阵,以加权平均算子进行定量化综合分析评判...     

Modeling discrete choice behavior using concepts from fuzzy set theory,approximate reasoning and neural networks

Models of discrete choice analysis are usually based on the random utility framework. They assume that decision makers make decisions that maximize their utility. Alternative formulations of the problem have also been proposed in the literature. These approaches model the decision makers’ perceptions of the attributes of the various alternatives using fuzzy sets and linguistic variables, and the decision process itself, using concepts from approximate reasoning and fuzzy control. The underlying assumption is that decision makers use a few simple rules that relate their vague perceptions of the various attributes to their preferences towards the available alternatives. The paper extends this approach by incorporating rule weights, which capture the importance of a particular rule in the decision process. It also presents an approach for calibrating the weights using concepts from neural networks. A case study, involving mode choice, is used to demonstrate the potential of the approach and compare it to alternative formulations and methodologies.     

防风网技术在广西北部湾港的应用研究

文章通过对广西北部湾港粉尘污染类型和防治措施的研究,提出粉尘污染控制存在的问题,指出防风网在防风抑尘方面的应用优势,对防风网的抑尘机理及应用情况进行了总结,并提出了广西北部湾港应用防风网技术的对策。     

防风网工程防撞措施研究

防风网工程是一种应用于港口露天煤堆场的环保构筑物,然而煤堆场内转运煤炭的货车数量多且出入频繁,给道路周边的防风网带来了安全隐患。文章分析了港区货车撞击防风网等构筑物事故发生的原因,介绍了防撞措施的设置原则,提出了刚性防撞、柔性防撞、绿化带防撞、夜间防撞等被动防撞措施。