Evaluating the risk of operational safety for dangerous goods in airfreights – A revised risk matrix based on fuzzy AHP

The purpose of this study is to assess the risk of operational safety for dangerous goods in airfreights. Based on the relevant literature, the risk factors (RFs) of operational safety were first investigated. A revised risk matrix based on fuzzy AHP (Analytic Hierarchy Process) was then proposed to assess the risks of those RFs’ in operational safety. Finally, to validate the model, the airfreight operations of dangerous goods in Taiwan were empirically investigated. The results indicate the RFs with higher risk in shipper’s operations are inaccurate declaration, insufficient knowledge and insufficient packaging. For the results, theoretical and managerial implications are further discussed. The results can provide practical information for airfreight operators to improve the performance of safety management. Further, the revised risk matrix can provide methodological reference for risk assessment research.     

模糊综合评判在试卷质量评定中的应用

为了减少在试卷质量评定中的主观性和随意性，文章运用模糊数学理论，建立了在试卷质量评定中的模糊综合评判模型。应用于实践，简便易行、客观公正，取得了满意的效果。     

城市综合交通系统评价指标体系及评价方法

对现有的城市综合交通系统进行改善，首先应对其进行客观全面的描述和评价，找出问、题的根源，为系统改善提供依据。探讨了城市综合交通系统评价指标体系。利用层次分析法和模糊数学理论，建立了评价计算的具体方法，并进行了实例分析。     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

Assigning fighter plane formations to enemy aircraft using fuzzy logic

Fighter aircraft protect specific facilities on alert in the air by patrolling expectation zones. These zones are located in the direction from which enemy aircraft attacks are expected; fighter formations are sent from them to intercept enemy aircraft. The problem considered in this paper is to determine the optimum assignment of fighter plane formations to enemy formations. The proposed solution is based on fuzzy logic and integer linear programming. A numerical example is given to illustrate the application possibilities of the proposed solution.     

基于模糊神经网络的水火弯板双臂系统控制与研究

研究了水火弯板双机械臂的非线性控制问题,采用一种模糊神经网络算法,利用神经网络的学习能力和模糊控制的模糊信息处理能力,调整优化双机械臂运动的位置偏差,得到该控制体系下的最佳驱动参数,仿真结果表明,该控制策略提高了水火弯板双机械臂的控制精度,并且使系统具有较好的动、静态性能。     

七自由度悬架模糊控制与仿真

为提高车辆悬架减振性能,兼顾车辆行驶平顺性和操稳性,以某微型轿车为基础建立了简化的七自由度悬架模型,并提出了模糊控制方法。以车身质心加速度、俯仰角加速度、侧倾角加速度、悬架动变形、轮胎动载荷作为评价指标,在Matlab/Simulink环境中以C级白噪声路面作为激励,对模糊控制主动悬架进行仿真。结果表明,模糊控制下的主动悬架各项评价指标均得到明显改善,为主动悬架在车辆上实际应用提供了参考。     

基于模糊理论的高速公路入口匝道控制研究

入口匝道控制是缓解高速公路交通拥挤的主要控制方式之一。在分析高速公路交通流运行特性的前提下,选择交通流平均速度、入口匝道排队长度作为模糊控制的输入量,入口匝道调节率作为模糊控制的输出量;然后,借鉴入口匝道控制的原理和模糊控制系统的设计步骤,运用MATLAB仿真软件确定了相应的隶属函数、模糊控制规则、模糊推理以及反模糊化的方法;最后,从交通流平均速度、入口匝道排队长度以及入口匝道调节率这三者之间的输入/输出特性曲面,可以明显地看出该模糊控制系统更符合匝道控制的实际情况,并且在行程时间和总服务流量上均优于定时控制。