蒸汽动力装置回汽制动工况下倒车级动叶安全性分析

针对蒸汽动力船舶回汽制动工况下倒车汽轮机叶片的运行安全性问题,通过回汽制动工况下倒车汽轮机的变工况工作过程分析,建立回汽制动工况下倒车级动叶的弯曲应力模型,并基于Matlab-Simulink环境,建立倒车级动叶的弯曲应力的仿真模型.仿真结果表明,回汽制动工况下,采用理想制动策略时倒车级第二级动叶的弯曲应力会超过安全许用值,导致倒车汽轮机的损坏,影响蒸汽动力装置的安全运行,因此需对倒车汽轮机进汽阀开启的时机及幅度进行限制.     

智能交通检测设备及道路事件探测研究

通过阐述目前智能交通系统交通量信息采集子系统中广泛使用的几种车辆检测设备。对各自的检测原理作一分析,可有助于不同车辆占有率情况下交通异常事件判别模型的正确选择。     

列车运行调整辅助决策系统设计

列车运行调整辅助决策系统，是铁路行车调度指挥自动化系统的关键环节，通过对该系统工作流程及各主要组成部分的分析设计，建立列车运行调整的混合0-1线性规划模型，针对传统处理方法的不足，提出并利用有限状态机制控制处理过程，为该系统提供可行的方案，此方案不仅符合结构化系统设计的基本思想，容易编制出相应的软件，而且有较强的实用性。     

面向机器人应用的虚拟现实系统

介绍了虚拟现实系统的基本构成。基于几何模型的虚拟环境的构成方式，改善实时性的方法和处理技巧。最后研究了面向机器人应用的虚拟现实系统结构，基本原理和软件开发平台，并进行了机器人仿真。     

S型电气绝缘节的建模与仿真

针对无绝缘轨道电路中S型电气绝缘节 ,本文运用电路理论中的支路电流法讨论了其数学模型的建立与仿真算法的实现 ,给出了绝缘节分别在空载和有负载两种情况下的几种典型特性曲线 ,并进一步分析了绝缘节的输入阻抗、电压传输及电气隔离特性。其各种数字仿真结果与实物测试结果具有相近的变化趋势 ,仿真精度满足要求。结果表明 :该模型能够很好地模拟S型绝缘节的电气特性 ,为S型绝缘节的设计提供了有力的分析工具。     

Investigating the nature and impact of reporting bias in road crash data

This paper investigates the nature, and impact of the reporting bias associated with the police-reported crash data on inferences made using this data. In doing so, we merge a detailed emergency room data and police-reported crash data for a specific region in Denmark. To disentangle potentially common observable and unobservable factors that affect drivers’ injury severity risk and their crash reporting behavior, we formulate a bivariate ordered-response probit model of injury severity risk and crash reporting propensity. To empirically identify the reporting bias in this joint model, we exploit an exogenous police reform that particularly affects some specific municipalities of the region under consideration. The empirical analysis reveals substantial reporting bias in the commonly used police-reported road crash data. This non-random sample selection associated with the police-reported crash data leads to biased estimates on the effect of some of the explanatory variables in injury severity analysis. For instance, estimates based on the police-reported crash data substantially underestimate the effectiveness of seat belt use in reducing drivers’ injury severity risk.     

Why early adopters engage in interpersonal diffusion of technological innovations: An empirical study on electric bicycles and electric scooters

Early adopters promoting electric vehicles in their social network may speed up market uptake of this technology. Apart from their opinion leader status, few previous research details the motivations which turn early adopters into advocates for innovation who approach the non-adopters among their family and friends, or casual acquaintances.Drawing on a survey among 1398 e-bike and 133 e-scooter early adopters in Austria, personal drivers of engagement in interpersonal diffusion are investigated. Longitudinal data one year later for 157 e-bike users allows tests of causal relations. A complementary sample of 33 network peers illustrates the early adopters’ social impact.Early adopters engage actively in discussing product features, instigating trial behavior and recommending purchase. Analyses by structural equation modeling show that efforts at interpersonal diffusion are driven by opinion leadership, experienced product performance, and perceived normative expectations of others toward pro-environmental technologies. Mediator and moderator analyses underline that opinion leadership is conveyed upon early adopters because personal norms and technophilia qualify them as credible and competent for the specific topic of e-vehicles. Social norm interrelations point to dynamic interactions and discourse between early adopters and their addressees. Evidence from the peer sample suggests though that the persuasive impact of early adopters is small.To accelerate market entry of electric vehicles, public or private agencies should foremost approach early adopters scoring high in the identified drivers, and empower them in their role as multiplicators by providing pre-prepared product information and encouraging them to continuously address peers.     

Pricing local emission exposure of road traffic: An agent-based approach

This paper proposes a new approach to iteratively calculate local air pollution exposure tolls in large-scale urban settings by taking the exposure times and locations of individuals into consideration. It explicitly avoids detailed air pollution concentration calculations and is therefore characterized by little data requirements, reasonable computation times for iterative calculations, and open-source compatibility. In a first step, the paper shows how to derive time-dependent vehicle-specific exposure tolls in an agent-based model. It closes the circle from the polluting entity, to the receiving entity, to damage costs, to tolls, and back to the behavioral change of the polluting entity. In a second step, the approach is applied to a large-scale real-world scenario of the Munich metropolitan area in Germany. Changes in emission levels, exposure costs, and user benefits are calculated. These figures are compared to a flat emission toll, and to a regulatory measure (a speed reduction in the inner city), respectively. The results indicate that the flat emission toll reduces overall emissions more significantly than the exposure toll, but its exposure cost reductions are rather small. For the exposure toll, overall emissions increase for freight traffic which implies a potential conflict between pricing schemes to optimize local emission exposure and others to abate climate change. Regarding the mitigation of exposure costs caused by urban travelers, the regulatory measure is found to be an effective strategy, but it implies losses in user benefits.     

Predictive usage mining for life cycle assessment

The usage modeling in life cycle assessment (LCA) is rarely discussed despite the magnitude of environmental impact from the usage stage. In this paper, the usage modeling technique, predictive usage mining for life cycle assessment (PUMLCA) algorithm, is proposed as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, and formulate predictive LCA equations for current and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations.     

Discovering themes and trends in transportation research using topic modeling

Transportation research is a key area in both science and engineering. In this paper, we present an empirical analysis of 17,163 articles published in 22 leading transportation journals from 1990 to 2015. We apply a latent Dirichlet allocation (LDA) model on article abstracts to infer 50 key topics. We show that those characterized topics are both representative and meaningful, mostly corresponding to established sub-fields in transportation research. These identified fields reveal a research landscape for transportation. Based on the results of LDA, we quantify the similarity of journals and countries/regions in terms of their aggregated topic distributions. By measuring the variation of topic distributions over time, we find some general research trends, such as topics on sustainability, travel behavior and non-motorized mobility are becoming increasingly popular over time. We also carry out this temporal analysis for each journal, observing a high degree of consistency for most journals. However, some interesting anomaly, such as special issues on particular topics, are detected from temporal variation as well. By quantifying the temporal trends at the country/region level, we find that countries/regions display clearly distinguishable patterns, suggesting that research communities in different regions tend to focus on different sub-fields. Our results could benefit different parties in the academic community—including researchers, journal editors and funding agencies—in terms of identifying promising research topics/projects, seeking for candidate journals for a submission, and realigning focus for journal development.