舰船电力系统可靠性研究初探

本文讨论了舰船电力系统可靠性研究的需求和必要性,讨论了国内外的研究现状;根据陆地电力系统可靠性研究的经验和内容,在分析舰船电力系统的区别和特殊需求后,探讨了舰船电力系统可靠性的研究内容以及研究思路和步骤;以船舶电网的拓扑结构设计中的可靠性研究为例,介绍了各种可靠性指标的计算方法;最后对未来的研究提出方向。     

蛋白组演化网络的度分布研究

对已有的蛋白组增长模型作出进一步的探索和拓展,提出了一个新的蛋白组演化网络模型,应用马氏链理论证明了其极限度分布的存在性,进而给出了它的明显表达式.结果表明:所建立的蛋白组演化网络度分布服从幂律分布,从而也是一个无标度网络.该结果对马氏链在蛋白质相互作用网络中的应用具有一定的参考价值.     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Transport resilience and vulnerability: The role of connectivity

This paper aims to adopt a critical stance on the relevance and interpretation of the recently emerging concepts of resilience and vulnerability in transportation studies. It makes a clear distinction between engineering and ecological interpretations of these concepts and offers a systematic typology of various studies in this field. A core element in the study is the linkage between the aforementioned concepts and connectivity/accessibility in transport networks. The methodological findings in the study are put in perspective by addressing also such concepts as robustness, reliability and friability of transport systems.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.     

一类大规模运输网络多阶段随机规划配流模型

利用随机规划理论，分析大规模运输网络的基本特性及优化，是近几年交通运输领域发展起来的一种新的研究方向，已广泛地应用于交通运输管理的各个部门。本文在分析了一类大规模运输网络在市场经济模式下的基本特性的基础上，提出了一类适用范围更广、更复杂的多阶段和广义多阶段随机规划配流模型。     

WGS-84至工程坐标系的转换参数获取

随着直接由GPS相对定位获取高精度的工程坐标成为可能 ,由GPS测定WGS 84坐标系的坐标 (或坐标增量 )至用户定义的工程坐标系坐标 (或坐标增量 )所需的转换参数的获取方法以及精度日益为人们所重视 .提出了研究这一问题的必要性 .在给出不同条件下坐标转换参数的获取技术的基础上 ,对转换参数的精度的影响因素进行了分析 ,对转换网的要求提出了定性的观点     

A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

A hybrid algorithm to design the multi-layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial im-munology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore im-proves the probabiligy of training algorithm to converge to global optima. The applications of the neural network in the modulation-style recognition of analog modulated rader signals demonstrate the good performance of the network.     

主动磁轴承的神经网络辨识研究

简述了主动磁轴承的工作原理及数学模型,在传统系统辨识的基础上提出了基于BP神经网络的非线性辨识方案.通过闭环控制使转子稳定悬浮,并在控制器的输出信号中加入随机噪声,以使系统被充分激励.按照10 kHz的采样频率对磁轴承的I/O数据进行采样,以用于磁轴承特性的辨识.对某单自由度磁轴承的仿真表明,神经网络模型能够以很高的精度逼近磁轴承的输入输出数据,且对不同的输入信号有较好的泛化能力,验证了所提方案的有效性.