The link between economic and environmental performance of the top 10 U.S. ports

Ports currently face increasing demands to address a variety of environmental issues and achieve sustainability objectives. Using insights from the resource-based view of firms, this study examines the link between economic performance (EP) and environmental performance (ENP) of the top 10 U.S. seaports. Geospatial modeling is used to capture pollution incidents that can be geographically dispersed; in addition, both a bootstrap data envelopment analysis (DEA) approach and an undesirable DEA model are used to measure port ENP and EP. Our findings are in general consistent with assertions that positive EP can be attained in conjunction with good environmental practices. Ports’ capabilities of strategic capital investment in physical assets, collaborative interorganizational processes, and performance monitoring are essential, while they pursue both economic and environmental goals simultaneously.     

采用双互易边界元法进行三维声场特征值分析的近似函数研究

该文对应用双互易边界元法进行三维声场特征值分析时所用的近似函数进行了研究和讨论.分别以RBF函数、TPS函数和扩展的RBF和TPS函数作为近似函数对一长方体封闭空间声特征值进行了计算.计算结果表明:TPS函数得出的结果与其它函数相比是比较精确的,且收敛速度较快,但高次的TPS函数并没有显示出优势;TPS函数、扩展的TPS函数和RBF函数对于域内点的选取不太敏感.     

A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network

Most previous work in addressing the adaptive routing problem in stochastic and time-dependent (STD) network has been focusing on developing parametric models to reflect the network dynamics and designing efficient algorithms to solve these models. However, strong assumptions need to be made in the models and some algorithms also suffer from the curse of dimensionality. In this paper, we examine the application of Reinforcement Learning as a non-parametric model-free method to solve the problem. Both the online Q learning method for discrete state space and the offline fitted Q iteration algorithm for continuous state space are discussed. With a small case study on a mid-sized network, we demonstrate the significant advantages of using Reinforcement Learning to solve for the optimal routing policy over traditional stochastic dynamic programming method. And the fitted Q iteration algorithm combined with tree-based function approximation is shown to outperform other methods especially during peak demand periods.     

支持向量回归在轮轨Hertz弹性接触计算中的应用

空间轮轨接触是铁道车辆系统动力学仿真对象模型中的重要柔性环节，基于材料线弹性假设的Hertz理论，可以给出两弹性体一般接触的复杂的隐式非线性关系.支持向量回归是统计学习理论中提出的新一代学习算法．轮轨接触计算可以使用它离线学习到一个显式的非线性关系．其逼近精度和表示复杂度可得到很好的折中．有利于铁道车辆系统动力学仿真的在线计算，     

舰船雷达散射截面预估

介绍了舰船雷达散射截面（RCS）预估的方法及舰船RCS预估的理论基础——电磁场高频方法，并给出了用3D建模软件构建一些舰船几何模型与RCS计算结果。     

基于神经网络的水深插值研究

提出了利用神经网络技术，对离散的、分布不规则的海图水深数据进行插值处理得到连续水深数值的方法，研究了应用Levenberg-Marquardt反向学习网络进行函数逼近和径向基函数网络进行函数插值二种算法，用函数曲面和实际水域数值对算法进行了测试和误差分析，并对实际应用中区域分块、规一化和等深线数据处理等问题作了说明。     

基于SVR近似模型的潜水器外形优化

流体仿真软件在船舶与海洋结构物概念设计阶段应用广泛。针对潜器外形设计过程中,仿真分析往往需要耗费大量的时间成本,无法直接与优化器结合的问题,本文研究基于支持向量回归机（Support Vector Regres-sion, SVR）的潜器外形优化方法,包括拉丁超立方试验设计选取样本点、基于 ICEM的潜器参数化建模和网格自动划分、基于 Fluent的阻力计算及 SVR模型的构造。采用改进的粒子群算法求解潜器外形优化设计问题,得到了阻力性能优良的潜器外形。     

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实时交通信号控制是城市交通控制系统的重要组成部分,建立在前人研究工作的基础上,本文尝试采用多智能体的分布式控制技术来解决复杂的交通信号控制问题,构造了多智能体的城市交通控制系统控制流程,提出基于同时扰动随机逼近算法/人工神经网络的改进的交通控制模型,模型通过采用同时扰动随机逼近算法来更新神经网络的权重,这种方法克服了现有控制方法需要大量的数据传输、准确的数学模型等缺陷。最后作者应用微观交通仿真系统对模型的有效性在较为复杂的交通网络中进行了测试,仿真结果表明了该方法的有效性。     

Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five‐point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL‐based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision‐making of traffic flow prediction. Copyright © 2012 John Wiley & Sons, Ltd.     

白车身轻量化的一种混合优化策略

以某轿车白车身为研究对象,综合考虑弯曲、扭转刚度和模态指标的性能要求,提出一种基于近似模型寻优和灵敏度分析统筹协调变量的混合优化策略.算法同时考虑构造近似模型与灵敏度,对模型进行全局最优化搜索及仿真验证,并根据灵敏度分析所得贡献率,适当调整变量取值,最终使白车身结构的轻量化达到7.51％.结果表明,混合优化策略能够明显提高白车身轻量化设计的效率.