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。     

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。     

Impact of phytoplankton community size on a linked global ocean optical and ecosystem model

We isolated the effect phytoplankton cell size has on varying remote sensing reflectance spectra (R_rs(λ)) in the presence of optically active constituents by using optical and radiative transfer models linked in an offline diagnostic calculation to a global biogeochemical/ecosystem/circulation model with explicit phytoplankton size classes. Two case studies were carried out, each with several scenarios to isolate the effects of chlorophyll concentration, phytoplankton cell size, and size-varying phytoplankton absorption on R_rs(λ). The goal of the study was to determine the relative contribution of phytoplankton cell size and chlorophyll to overall R_rs(λ) and to understand where a standard band ratio algorithm (OC4) may under/overestimate chlorophyll due to R_rs(λ) being significantly affected by phytoplankton size. Phytoplankton cell size was found to contribute secondarily to R_rs(λ) variability and to amplify or dampen the seasonal cycle in R_rs(λ), driven by chlorophyll. Size and chlorophyll were found to change in phase at low to mid-latitudes, but were anti-correlated or poorly correlated at high latitudes. Phytoplankton size effects increased model calculated R_rs(443) in the subtropical ocean during local spring through early fall months in both hemispheres and decreased R_rs(443) in the Northern Hemisphere high latitude regions during local summer to fall months. This study attempts to tease apart when/where variability about the OC4 relationship may be associated with cell size variability. The OC4 algorithm may underestimate [Chl] when the fraction of microplankton is elevated, which occurs in the model simulations during local spring/summer months at high latitudes in both hemispheres.     

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.     

集通铁路长大隧道GSM-R冗余方案研究

针对集通铁路的长大隧道,论述分布式基站和数字光纤直放站的两种冗余方案,通过分析比较两种方案的单网交织以及同站址双网冗余覆盖方式,推荐采用分布式基站单网交织冗余方案。     

一种基于语义分割结果的车道线检测拟合方法

车道线等地面标志物的检测是自动驾驶车辆环境感知的重要内容,能够为车辆提供可行驶区域的信息。文章提出一种基于语义分割结果的车道线检测拟合方法。使用车载单目相机获取车辆行驶过程中采集的道路图像,送入卷积神经网络进行车道线语义分割。将分割得到的仅含车道线的二值图像进行透视变换得到鸟瞰图,筛选有效车道线像素点,对有效车道线点使用最小二乘法进行多项式拟合,输出左右车道线多项式拟合系数,能够有效解决传统车道线检测算法的环境适应性差,鲁棒性不强,对弯道车道线检测信息不够准确等问题。     

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.