探讨模拟数学在学术论文搞质量综合评价中的应用

首先提出，目前仍采用三审制，以语言设计院评价学术文稿质量，其结果模糊，缺乏量化、缺乏稿件之间的可比性，还受到评价人人为因素的影响，有失公正性，探讨应用模糊数学方法评价论文稿质量的可行性，介绍同属模糊数学范畴的方法一和方法二的运用过程，并将运用两方法的结果作比较，提出：方法二的基本内容是主因素决定型的数学模型M（∧，∨），其运用结果仍显模糊，缺乏准确性；方法一简单，实用，结果直观，因为其运用过程中采用了以百分数表达的单因素评价矩阵与几项权重向量的合成运算，运用方法一，可促进编辑部审稿工作的程式化，标准化、自动化。     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

基于模糊综合评价法的馈能悬架结构方案评价

馈能型悬架是可以回收悬架之间振动能量的新型悬架,研究国内外几种馈能型悬架方案的结构和工作原理,运用模糊综合评价法对馈能型悬架的结构方案进行评价.结果表明液电馈能型悬架方案具有明显的优势,是最可行的馈能型悬架结构方案.     

A survey on planning semi-flexible transit systems: Methodological issues and a unifying framework

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, due to its fixed structure. New transportation alternatives are therefore increasingly proposed, combining on-demand service adjustment capabilities to the regular route and schedule characteristics of traditional transit. Such so-called semi-flexible systems require careful planning, but no formalization of the corresponding decisions problems, nor any comprehensive methodology has been proposed yet. This paper aims at contributing to fill this gap by presenting a comprehensive literature review, and a general and unifying modeling framework for representing and planning semi-flexible systems. The latter takes the form of the Demand Adaptive Systems, which generalizes the semi-flexible systems described in the literature, and also offers a number of advanced features, the scheduling mechanism, in particular. The paper then provides a classification of planning decisions, which is used to structure a comprehensive and comparative literature review of the field of semi-flexible systems, including methodological contributions as well as a number of particularly significant practical experiences.     

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

A yaw-moment control method based on a vehicle's lateral jerk information

Previously, a new control concept called ‘G-vectoring control (GVC)’ to improve vehicle agility and stability was developed. GVC is an automatic longitudinal acceleration control method that responds to vehicle lateral jerk caused by the driver's steering manoeuvres. In this paper, a new yaw-moment control method, which generates a stabilising moment during the GVC command and has positive acceleration value and the driver's accelerator pedal input is zero, was proposed. A new hybrid control, which comprises GVC, electric stability control and this new control, was constructed, and it was installed in a test vehicle and tested on a snowy surface. The very high potential for improvement in both agility and stability was confirmed.     

機車騎士未戴安全帽辨識之研究

臺灣地區機車車輛數眾多,但在應用在機車騎士執法上這一層面較少討論到。國人普遍駕駛習慣不良,而道路交通事故的發生常常就是因爲用路人不守法的行爲所致,近年來未戴安全帽而招舉報的案件上昇10萬多件,使得交通安全的問題日益嚴重。文中使用監視攝影機,透過模糊類神經演算法的運算,篩選出未戴安全帽者,並可結合顔色特徵作進一步分析,提高其辨識率,進行未戴安全帽的辨識工作。這一研究經簡單測試有九成的準確率,在智慧型執法系統中有輔助的效應,也殿立了後續發展的基礎。     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

Influence of some vehicle and track parameters on the environmental vibrations induced by railway traffic

A study is performed on the influence of some typical railway vehicle and track parameters on the level of ground vibrations induced in the neighbourhood. The results are obtained from a previously validated simulation framework considering in a first step the vehicle/track subsystem and, in a second step, the response of the soil to the forces resulting from the first analysis. The vehicle is reduced to a simple vertical 3-dof model, corresponding to the superposition of the wheelset, the bogie and the car body. The rail is modelled as a succession of beam elements elastically supported by the sleepers, lying themselves on a flexible foundation representing the ballast and the subgrade. The connection between the wheels and the rails is realised through a non-linear Hertzian contact. The soil motion is obtained from a finite/infinite element model. The investigated vehicle parameters are its type (urban, high speed, freight, etc.) and its speed. For the track, the rail flexural stiffness, the railpad stiffness, the spacing between sleepers and the rail and sleeper masses are considered. In all cases, the parameter value range is defined from a bibliographic browsing. At the end, the paper proposes a table summarising the influence of each studied parameter on three indicators: the vehicle acceleration, the rail velocity and the soil velocity. It namely turns out that the vehicle has a serious influence on the vibration level and should be considered in prediction models.