机车运行途中故障诊断推理方法的研究

针对机车运行途中故障诊断推理的方法进行了研究,通过利用系统专家的经验知识建立故障树模型,并运用双向推理控制策略和启发式搜索算法建立推理机,实现了机车运行途中故障诊断推理系统的动态建立。     

基于模糊集理论的公路建设项目综合评价

建立了一种基于模糊集理论的公路建设项目综合评价方法,分别通过评价语言变量和权重语言变量将评价指标体系的定性指标和定量指标及权重全部转化为三角形模糊数,克服了定性与定量评价指标不具可比性的缺点;基于最优度概念,将权重模糊评价矩阵转化成模糊单一评价矩阵,避免传统模糊数之间比较的复杂性和不精确性;考虑到评价者在模糊评价环境下对风险的态度,将乐观系数引入评价过程;采用TOPSIS方法计算出评价方案的综合评价分数并进行排序。实例研究结果与主成分分析法的结果一致,表明了方法的合理性和有效性。     

机车故障诊断中征兆隶属度函数的研究

研究了应用模糊神经网络进行机车故障诊断时一种征兆隶属度向量提取的方法。结合机车运行的特点,分析机车控制的规律和特性,提出了切实可行的隶属度函数确定方法,解决了模糊神经网络在机车故障诊断中应用的一个瓶颈。     

服役海上结构物破损状态评估研究

对现有的各种结构破损状态评估方法进行了综述和分析对这些方法在受损船舶和海洋工程结构中的应用作了一些探讨，并尝试用模糊数学方法估算了服役海上结构物的破损状态。最后对模糊数学和专家系统方法在服役海上结构物破损状态评估中的应用前景进行了探讨。     

旋转压实仪压实力控制方法研究

采用试验研究为主导,结合相应的理论分析,采取分段控制的模型对旋转压实仪的压实力进行试验仿真研究;利用模糊PID控制的方法对压实力进行控制,解决了常规PID控制方法用于非线性、时变特性的被控对象难于实现实时控制的问题。最后,采用80C196单片机,应用模糊自整定数字控制的方法,设计了旋转压实仪控制器。实际应用结果表明:该控制方法可以提高旋转压实仪的动态响应速度,其压实力的控制精度可以达到设计标准的要求。     

基于中关村西区的地下停车诱导信息系统设计

以北京中关村西区地下停车场为背景，对停车诱导信息系统进行了设计与研究。文章主要介绍了中关村西区地下停车场诱导信息发布方案的设计，基于BP神经网络的停车泊位预测以及基于模糊推理的地下环廊路段优化选择，并给出了Matlab环境下的仿真结果。     

Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation

The tremendous use of hazardous materials has promoted the economic development, which also brings about a growing risk causing a widespread concern. In this work, we consider a location-scheduling problem on hazardous materials transportation under the assumption that transportation risks are time-dependent fuzzy random variables. First, we formulate a scheduling optimization model and design a fuzzy random simulation based genetic algorithm to optimize the departure time and dwell times for each depot–customer pair. Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks. Finally, numerical examples are given to illustrate the effectiveness of the proposed models and algorithms.     

Application of multi-criteria decision analysis methods for assessing walkability: A case study in Porto Alegre,Brazil

The concept of walkability refers to the extent to which a neighbourhood is walking-friendly. Several walkability indexes have been developed to quantify and evaluate the pedestrian environment. These indexes differ in terms of type of data, methods and goals. The indexes variables may present either uniform or distinct weights, defined by arbitrary, empirical or other diverse weighting methods. This paper pursues the determination of a weighted walkability index, constructed on the basis of the relative importance of their attributes. Weights were determined by the application of the Fuzzy Analytic Hierarchy Process (FAHP), a robust multi-criteria method which considers the experts’ uncertainty in decision making. Moreover, FAHP weights were compared with the attribute weights obtained from other simpler methods, and a chi-square test for homogeneity was computed to compare the obtained values. The three most important walkability attributes were: Public Security, Traffic Safety and Pavement Quality, similar results to the ones found in the literature. The application to a case study in the city of Porto Alegre, Brazil, allowed categorizing the studied neighbourhoods and to analyse the effect of changes on attributes in walkability.     

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.