一种DES算法的FPGA快速实现方法

该文阐述了密码算法FPGA快速实现的一种方法,利用Mentor Graphics的Catapult C综合工具,完成了DES算法从计算机仿真到硬件实现的全过程,代替了传统FPGA实现处理过程,可大大缩短项日的开发周期和降低作难度。     

EDA-VNS混合算法在求解同序Flow-shop问题中的应用

EDA（Estimation Distribution Algorithms,分布估计算法）是进化计算领域新兴起的一类随机优化算法,和遗传算法从微观上模拟生物进化的机理不同,EDA是从宏观上对生物群体整体分布的建模和模拟。由于EDA对微观概念上的搜索不太理想,文章将一种VNS（Variable Neighborhood Search,变邻域搜索）算法与EDA结合来完成对问题解的搜索。经过试验验证,EDA-VNS混合算法在求解同序Flow-shop问题比遗传算法有较好的性能。     

基于遗传算法的目标设定式电磁扫雷具结构研究

从考察目标设定式电磁扫雷具模拟受保护舰船磁场效果出发,利用遗传算法良好的全局搜索能力,得出现有目标设定式电磁扫雷具在模拟上有保护舰船磁场上的不足及原因,提出一种在不改变目标设定式电磁扫雷具单个磁体结构的基础上,通过改变磁体水中状态提高模拟效果的办法,通过仿真试验,结果表明该方法效果良好。     

基于Prony算法的感应电动机转子故障检测方法

分析Prony算法及其高分辨率谱估计的特点,在此基础上,提出一种基于Prony算法的感应电动机转子故障检测方法,仿真研究结果表明,在短数据情况下,相对FFT分析技术而言,该方法频率分辨率更高,故障检测更为准确,且计算量小,有利于电机故障实时状态监测。实验证明,将该方法应用于感应电机转子故障检测,可准确检测出转子故障时在定子电流中的故障特征成分。     

旋转惯导捷联算法

针对旋转惯导计算原理,分析了转台测角、测速误差对系统精度的影响。为确定其影响程度,将转台测角、测速误差等效为相应量值的陀螺仪漂移。理论计算和仿真结果表明,转台测量误差特别是测速误差对惯导系统造成较大的影响。提出改进的旋转惯导捷联算法,该算法在姿态更新和导航计算后引入转台转角信息而不涉及转速信息,从算法原理上消除了测速误差对系统精度的影响。     

两种目标定位方法的误差分析

单站球坐标定位法和梯度法是目标定位中经常使用的2种方法.针对2种目标定位方法的定位精度问题,分别介绍了2种方法的原理及误差处理过程.通过理论分析和仿真计算对上述方法的目标定位精度进行比较,结果表明,梯度法比单站球坐标定位法具有更高的目标定位精度.     

Reliable sensor deployment for network traffic surveillance

New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).     

Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

考虑最短距离作业的港口拖轮作业调度优化

为了合理调度拖轮作业,有效提高拖轮的利用率,减少船舶等待时间,针对拖轮作业特点,用多处理器任务调度理论分析了拖轮作业调度问题.在考虑拖轮最短距离作业的前提下,以最小化最大完工时间为优化目标建立了拖轮作业调度模型.采用演化策略算法对拖轮作业调度问题进行优化,提出了一种基于轮盘赌概率分配的编、解码方法,并设计了三点交叉互换的重组算子和个体基因交叉互换的变异算子.算例结果表明:用演化策略算法的优化结果优于仿真结果和粒子群算法的计算结果.采用实数编码时,通过优化算法得到的最大完工时间最小值为356 min,比仿真结果减少了117 min;而采用整数编码时,最大完工时间最小值为369 min,比仿真结果减少了104 min.验证了最短距离作业原则的有效性.