柴油机发电机组中盘片式联轴器弯曲刚度计算

为方便轴系振动计算和提高其计算结果的准确度。针对实际中常见的八孔圆环形盘片式,进行实例分析计算,提供了盘片式有关分析计算的基本方法。     

自升式海洋平台发电机组排烟管长距离布置研究

本文以一个已经投用圆柱桩腿自升式平台为例,针对以往平台柴油发电机组排烟管就近位置布置,占用平台有用空间的现状;通过对平台结构及空间的分析,对柴油发电机组排烟管进行长距离布置。通过理论计算,完全满足设备的使用要求下,且该布置方案运用于该自升式海洋平台,使用效果良好。该方案的运用,完全可以保证发电机组的可靠运行,能够确保整个平台的安全生产作业。     

基于PSCAD的船用柴油发电机组仿真分析

船舶电力系统容量小,在受到较大扰动时,频率和电压不再保持为恒定,而是一个动态变化的过程。为了对船舶电力系统的过渡过程进行分析,本文利用PSCAD／EMTDC软件建立了柴油机及其调速系统以及发电机励磁系统的数学仿真模型,并对其各组成部分进行详细分析。最后利用分级突加突卸负载的方式对机组模型的正确性进行验证,指出通过对船舶电站同步发电机的励磁控制和柴油机的转速控制可以提高船舶电力系统稳定性。     

基于粗糙集的铁路工程项目投标决策方法研究

铁建企业项目具有规模大、周期长、技术性强等特征,使工程投标项目评估更复杂、决策难度更大.本文将盈利与竞争力提升作为铁路建筑企业项目投标决策的直接影响因素,提出了基于波士顿矩阵原理的项目分类策略,从项目因素、环境因素、自身因素和业主因素等4个方面出发构建了项目类别判定的粗糙集模型,并利用SVM(支持向量机)对已建立的粗糙集模型进行改进,证明改进模型的计算结果更加准确.根据模型分析结果对项目投标提出了若干有意义的建议,为铁路建筑企业项目投标决策提供参考.     

Stochastic user equilibrium with equilibrated choice sets: Part I – Model formulations under alternative distributions and restrictions

The aim of this paper is to remove the known limitations of Deterministic and Stochastic User Equilibrium (DUE and SUE), namely that only routes with the minimum cost are used in DUE, and that all permitted routes are used in SUE regardless of their costs. We achieve this by combining the advantages of the two principles, namely the definition of unused routes in DUE and of mis-perception in SUE, such that the resulting choice sets of used routes are equilibrated. Two model families are formulated to address this issue: the first is a general version of SUE permitting bounded and discrete error distributions; the second is a Restricted SUE model with an additional constraint that must be satisfied for unused paths. The overall advantage of these model families consists in their ability to combine the unused routes with the use of random utility models for used routes, without the need to pre-specify the choice set. We present model specifications within these families, show illustrative examples, evaluate their relative merits, and identify key directions for further research.     

Driving analytics using smartphones: Algorithms,comparisons and challenges

The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and “free-driving” conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.     

舰船中压发电机组振动监测与故障诊断

中压发电机组是舰船的动力核心,中压发电机组在使用较长时间之后可能会出现振动异常的问题,通过对其进行振动检测,能够确定振动异常的位置和原因,并通过针对性的措施来解决问题,维护中压发电机的正常运行。本文以某舰船的中压发电机组故障问题为例,对中压发电机组振动监测与故障诊断进行了详细的分析。     

Optimal transit routing with partial online information

This paper studies the routing strategy in a transit network with partial online information at stops. By partial online information, we mean that the arrival time of the incoming transit vehicles is available for a subset of the lines serving a stop. To cope with the partial information assumption, a new routing strategy is proposed and closed form formulae for computing expected waiting times and line boarding probabilities are derived. The proposed strategy unifies existing hyperpath-based transit route choice models that assume either no information or full information. Like many existing models, it ensures optimality when all information is available or the headway is exponentially distributed. The problem of determining the attractive set is discussed for each of the three information cases. In particular, a new heuristic algorithm is developed to generate the attractive set in the partial information case, which will always yield a solution no worse than that obtained without any information. The paper also reveals that, when information is available, an optimal hyperpath may contain cycles. Accordingly, the cause of such cycles is analyzed, and a sufficient condition that excludes cycles from optimal hyperpaths is proposed. Finally, numerical experiments are conducted to illustrate the impact of information availability on expected travel times and transit line load distributions. Among other findings, the results suggest that it is more useful to have information on faster lines than on slower lines.     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

Meeting points in ridesharing: A privacy-preserving approach

Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data.In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.