一种ATS系统自动化测试工具的设计与实现

列车自动监控系统(ATS)是车载信号系统中的重要组成部分.本文提出一种ATS系统的非侵入式自动化测试工具,模拟测试人员真实测试的整个流程,可配置的测试脚本更具有灵活性,可以实现不同的测试需求;采用基于计算机视觉与光学字符识别的信息提取方式,使测试流程可以完全模拟真人测试;通过验证状态机的结果,简化了测试验证流程.该工具...     

高等级公路弯沉检测方法发展动态及评价

分析了国内外弯沉检测技术现状及发展动态,比较了静态弯沉量测、稳定弯沉量测及冲击荷载下动态弯沉量测等几种弯沉检测方法,详述了贝克曼梁弯沉检测与落锤式弯沉仪各自的特点与不足,最后介绍了FWD弯沉仪在工程检测中的应用实例,有利于FWD在国内的推广应用。     

新建地铁控制中心大屏幕系统的设计优化

地铁控制中心的大屏幕系统能够便捷地为调度人员提供直观的信息显示,但也因为能耗大、维护成本高等诸多原因而饱受争议.分析了大屏幕系统在使用过程中存在的争议,提出了大屏幕系统设计的优化和改进措施.随着运营需求的不断增加和对大屏幕系统要求的不断提高,在进行大屏幕系统设计时,必须在合理规划运营需求的同时,积极探讨和使用新技术、新工艺.     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

The visual impact of development in coastal zone areas

Abstract

The focus of this paper is theoretical, as well as methodological. It reviews previous studies of visual impact and reports research by the author on people's evaluation of the appropriateness of different man‐made facilities in different coastal‐area contexts and other environments. In this study, subjects were asked to react to slides developed through the use of landscape models and experimental facilities to simulate the appearance of a building in the landscape, while systematically varying the visual relatedness between the two. Specific interest centered on the variable of congruity vs. contrast between the appearance of the building and its landscape context, manipulated by co‐varying the attributes of color and size to create a five‐point scale of contrast/obtrusiveness. The hypothesis was that judgments of appropriateness, and to a lesser extent judgments of liking, would vary inversely with level of contrast. The role of two further variables, the functional significance of the building in its setting, and the character of the setting itself, was also investigated. The author reviews methodological issues involved where subjects respond to visual stimuli for purposes of judging change in visual quality. These methodological issues include: stimulus‐selection and problems of simulation, choice of response measures, and choice of respondents, and the treatment of individual differences. The author calls for development of standardized scales and indices such as “perceptually‐relevant environmental quality indicators.”     

Ghrelin对大鼠小肠转运的作用及对中枢和胃肠道c-Fos表达的影响

目的观察静脉注射ghrelin对大鼠小肠转运的作用及对中枢和胃肠道c-Fos表达的影响。方法大鼠禁食24h,静脉注射ghrelin(2、5、10、20μg/kg),经预先埋置在十二指肠内的导管注入伊文氏蓝溶液,观察不同剂量ghrelin对大鼠小肠转运的影响及ghrelin受体拮抗剂(D-Lys3)GHRP-6对其作用的影响。采用免疫组织化学和图像分析方法观察静脉给予ghrelin对大鼠中枢和胃肠道的c-Fos蛋白的激活情况;观察(D-Lys3)GHRP-6对ghrelin作用的影响。结果 1静脉给予ghrelin 2μg/kg对大鼠小肠转运无显著影响,给予ghrelin 5、10、20μg/kg可剂量依赖性促进小肠转运,此作用可被(D-Lys3)GHRP-6阻断。2静脉注射ghrelin可激活中枢多个部位的c-Fos表达,包括下丘脑室旁核、弓状核、杏仁内侧核、迷走神经背核、孤束核、延髓最后区和胸腰段脊髓背角c-Fos均有表达;胃、十二指肠、空肠和近端结肠肠神经丛的c-Fos有不同程度的表达,其中以胃和近端结肠的c-Fos表达最为显著。应用ghrelin受体拮抗剂(D-Lys3)GHRP-6可抑制ghrelin激活的c-Fos表达。结论 Ghrelin可促进小肠转运,其促动力作用由其受体GHS-R所介导;静脉给予ghrelin可通过肠神经系统和中枢神经系统调节小肠运动。     

A hybrid optimization-simulation approach for robust weekly aircraft routing and retiming

We address the robust weekly aircraft routing and retiming problem, which requires determining weekly schedules for a heterogeneous fleet that maximizes the aircraft on-time performance, minimizes the total delay, and minimizes the number of delayed passengers. The fleet is required to serve a set of flights having known departure time windows while satisfying maintenance constraints. All flights are subject to random delays that may propagate through the network. We propose to solve this problem using a hybrid optimization-simulation approach based on a novel mixed-integer nonlinear programming model for the robust weekly aircraft maintenance routing problem. For this model, we provide an equivalent mixed-integer linear programming formulation that can be solved using a commercial solver. Furthermore, we describe a Monte-Carlo-based procedure for sequentially adjusting the flight departure times. We perform an extensive computational study using instances obtained from a major international airline, having up to 3387 flights and 164 aircraft, which demonstrates the efficacy of the proposed approach. Using the simulation software SimAir to assess the robustness of the solutions produced by our approach in comparison with that for the original solutions implemented by the airline, we found that on-time performance was improved by 9.8–16.0%, cumulative delay was reduced by 25.4–33.1%, and the number of delayed passengers was reduced by 8.2–51.6%.     

Boundedly rational user equilibria (BRUE): Mathematical formulation and solution sets

Boundedly rational user equilibria (BRUE) represent traffic flow distribution patterns where travellers can take any route whose travel cost is within an ‘indifference band’ of the shortest path cost. Those traffic flow patterns satisfying the above condition constitute a set, named the BRUE solution set. It is important to obtain all the BRUE flow patterns, because it can help predict the variation of the link flow pattern in a traffic network under the boundedly rational behavior assumption. However, the methodology of constructing the BRUE set has been lacking in the established literature. This paper fills the gap by constructing the BRUE solution set on traffic networks with fixed demands. After defining ε-BRUE, where ε is the indifference band for the perceived travel cost, we formulate the ε-BRUE problem as a nonlinear complementarity problem (NCP), so that a BRUE solution can be obtained by solving a BRUE–NCP formulation. To obtain the BRUE solution set encompassing all BRUE flow patterns, we propose a methodology of generating acceptable path set which may be utilized under the boundedly rational behavior assumption. We show that with the increase of the indifference band, the acceptable path set that contains boundedly rational equilibrium flows will be augmented, and the critical values of indifference band to augment these path sets can be identified by solving a family of mathematical programs with equilibrium constraints (MPEC) sequentially. The BRUE solution set can then be obtained by assigning all traffic demands to the acceptable path set. Various numerical examples are given to illustrate our findings.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.