多跳水下传感器网络中的功率分配算法

水下无线传感器网络的研究越来越受到学术界的重视,其典型特点是能量和带宽有限.多数水下无线传感器网络的应用都可以归结为参数估计问题.本文针对多跳水下无线传感器网络,对递进式的分布式参数估计算法进行了性能分析.在此基础上,利用交替最小化方法得到功率分配方案,目标是在给定的均方误差性能要求下,使得网络消耗的总功率最小.     

舰船电力电子设备在线阻抗建模方法

从二端口网络分析出发,设计一种基于差分方式的在线阻抗测试方法。运用人工神经网络的优化方法,提出一种阻抗等效电路参数的提取算法,由此建立在线阻抗等效电路模型。应用该方法实现了电力电子设备阻抗的在线工程测量,实测与仿真结果表明基于该建模方法所获得的阻抗等效电路能准确反映设备在线运行的阻抗特征。     

海洋工程用锚类型及其发展综述

海洋浮式装置锚泊定位系统的锚是使装置保持船位的关键设备。随着海洋工程的发展,已涌现出各种类型的海洋工程装置用锚,在锚的研究、设计和海上安装中大量采用先进的技术理论、试验方法和工艺技术,对各种海洋工程锚的特性、适用范围、国内外研究和发展情况作全面分析与阐述。     

基于事件触发的无线传感器网络动态分簇

针对由目标事件激发的无线传感器网络动态分簇问题,提出了一种动态分簇协议。着重研究了簇头竞选,成员招募和簇重组等关键环节,在簇头竞选机制中,提出了一种综合考虑了节点接收信号强度,剩余能量以及与汇聚节点的距离等因素的竞选方案。在簇成员招募阶段,提出了一种基于任务需求的自适应簇范围划分方法,约束了簇的规模,降低了簇内通信的能耗。仿真结果表明：该协议能够有效地节省节点能量,延长网络寿命,尤其适用于汇聚节点距离网络较远的情况。     

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.     

手机社交娱乐操作对驾驶人视觉参数影响研究

为探究手机社交娱乐操作对驾驶人视觉参数的影响，基于模拟驾驶器和SmartEye 眼动仪设计模拟驾驶实验，采集驾驶人正常驾驶、拨打电话、收发信息等5种操作下视觉特征数据，应用拉依达准则和联合频数分布直方图进行数据归约.针对不同操作下视觉参数，应用箱形图和Kruskal-Wallis显著性检验，验证视觉参数评价指标的有效性.结果表明：随手机操作类型的不同，驾驶人水平和垂直视角标准差、视线离开道路时间百分比、扫视平均速度和眨眼持续时间呈显著性变化，可作为视觉参数影响的评价指标；观看视频和刷朋友圈操作对驾驶人视觉影响程度明显高于拨打电话和收发短信操作.     

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.