Energy-aware load-balaning fault-tolerant routing scheme for ad hoc sensor networks

Most routing protocols for sensor networks try to extend network lifetime by minimizing the energy consumption, but have not taken the network reliability into account. An energy-aware, load-balancing and fault-tolerant routing scheme, termed as ELFR was propsed to adapt to the harsh environment. First a network robustness model was presented. Based on this model, the route discovery phase was designed to make the sensors to construct into a hop-leveled network which is mesh structure. A cross-layer design was adopted to measure the transmission delay so as to detect the failed nodes. The routing scheme works with acknowledge （ACK） feedback mechanism to transfer control messages to avoid producing extra control overhead messages. When nodes fail, the new healthy paths will be selected locally without rerouting. Simulation results show that our scheme is much robust, and it achieves better energy efficiency, load balancing and maintains good end-to-end delay.     

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鉴于模糊神经网络具有良好的非线性特性、学习能力、自适应能力和抗干扰能力,本文将模糊神经网络技术引入到高速公路入口匝道控制中。提出一种基于GA和BP算法的模糊神经网络控制器,并对控制器进行了详细设计。设计过程主要分为三部分：输入输出参数的选择、模糊神经网络的结构设计以及基于GA-BP的学习算法设计。最后,使用MATLAB软件对其进行了仿真。仿真结果表明,本文提出的方法是有效的,较之基于BP的模糊神经网络控制和ALINEA控制,能更好地稳定主线交通流密度。     

船体曲率板感应加热成形工艺研究

在船体曲面板的冷成形过程中,回弹是影响成形精度的主要因素,为提高板条成形质量,需研究回弹预测方法以获得合适的回弹控制方式,进而指导模具设计。

基于全卷积神经网络（FCN）对回弹图片进行像素级计算和回归计算,从而实现对每个成形位置的回弹量预测。首先,利用ABAQUS 2019建立有限元模型,并通过实验结果进行准确性的对比验证;然后,采用验证后的有限元方法计算获取神经网络训练样本集,将板条几何信息作为神经网络的输入,并基于不同卷积层结构采用TensorFlow深度学习框架来搭建全卷积网络模型;最后,对比分析不同神经网络的优劣,并将最优网络应用于模具设计。

算例分析结果显示：FCN模型预测回弹量的最大误差为8.49%,具有较高的准确度,其中FCN32的精度最高;FCN模型可以实现模具形状的一次性设计,计算时间仅为0.5 s,最大误差仅为1.00%,显著提高了计算效率。

全卷积神经网络算法提供了一种快速高效的板条回弹预测方法,以及快速设计模具形状的新思路。

     

基于改进 BiLSTM 网络的地铁车轮磨耗预测模型

针对地铁车轮磨耗数据时间跨度较长引起的长期依赖问题,为了进一步提升预测精度,提出一种将麻雀搜索算法(sparrow search algorithm,SSA)优化双向长短期记忆网络(bidirectional long short term memory,BiLSTM)的改进 BiLSTM(SSA-BiLSTM)网络模型,用于地铁车轮磨耗预测。首先,利用麻雀搜索算法对双向长短期记忆网络算法的神经元个数、迭代次数、输入批量和学习率等超参数在给定范围内进行寻优,得到参数最优值;然后,以参数最优值来构建改进 BiLSTM 网络模型,对车轮磨耗进行预测分析;最后,以车轮踏面磨耗和轮缘磨耗作为研究对象,将某地铁 1 车厢 1 号车轮的现场实测历史磨耗数据作为输入,对该模型进行训练及验证分析,并与多层感知机(multilayer perceptron,MLP)、LSTM、BiLSTM 以及 SSA-LSTM 模型的预测结果进行对比。研究结果表明：SSA-Bi-LSTM 模型的车轮磨耗预测精度更高,与 LSTM、BiLSTM 以及 SSA-LSTM 网络模型相比,踏面磨耗的平均绝对百分误差(mean absolute percentage error,MAPE)分别降低了 13.28%、10.32%、1.47%,轮缘磨耗分别降低了 9.5%、0.46%、0.02%;分别对同一地铁 2 号、4 号车厢的 1 号位置车轮磨耗进行预测,并与磨耗实测数据进行对比,踏面磨耗的平均绝对百分比误差分别为 1.34%、1.42%,轮缘磨耗的平均绝对百分比误差分别为 0.18%、0.19%,验证了本文所提模型具有良好的泛化性,为地铁轮对智能化管理提供理论支持,延长车轮使用寿命。     

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。     

基于神经网络和遗传算法结合的桥梁结构损伤诊断

利用神经网络对有限元计算的样本数据建立起桥梁结构的损伤参数——损伤位置、损伤程度（输入）以及位移差——损伤结构与完好结构的位移差（输出）的全局性映射关系，以获得遗传算法求解桥梁结构损伤问题所需的目标函数值，从而识别桥梁的损伤参数．某连续梁桥算例表明，此方法可以在较少的有限元分析次数下获得较好的损伤诊断结果．     

How the environmental impact affects the design of logistics networks based on cost minimization

The degree of centralization of the logistics network is one of the important strategic decisions to take. This issue has often been studied but mainly from a cost minimization focus. This research studies the influence of incorporating the environmental impact in logistics network design models, comparing the results with those obtained considering cost minimization only. A fuzzy biobjective optimization model is used to carry out a number of experiments. By weighting the relative importance of the two objectives, the differences with respect to the base cost minimization solution can be ascertained. Our results show that in general more material is moved through warehouses when the environmental impact is considered and more decentralized facilities are opened.     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

The use of demand correlation in the modeling of air carrier departure delays as first‐order autoregressive random processes

A method for modeling air carrier departure delays at commercial‐service airports as autoregressive random processes is presented. This method employs the correlation of a priori demand data to significantly reduce prediction error in the optimal least‐squares estimator for additive white noise. The reduction factor of the prediction error is demonstrated to be on the order of 10² over that of the unbiased estimator. Copyright © 2011 John Wiley & Sons, Ltd.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.