Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.     

Online Prediction of Travel Time: Experience From a Pilot Trial

Abstract

This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.     

无线传感器网络三维覆盖策略研究

从覆盖面积和有效覆盖率2个方面评估了正三角形网格和正四边形网格的性能优劣.根据理论分析和公式推导,将三维区域完全覆盖问题转化为多个平面覆盖问题研究.采用性能较好的正三角形网格进行节点部署,从拓扑学的角度给出了一种三维区域完全覆盖的部署策略,提供了所需的最少节点数的数学表达式,进行了仿真验证.     

一种移动式港口起重机绞车松绳压绳器

多功能移动式港口起重机为处理集装箱、重装大件和散杂货物,通常在吊钩的基础上配备集装箱吊具、抓斗或其他起重设备,因此起升绞车的防乱绳装置尤为重要.针对钢丝绳在卷筒上出现松绳后发生乱绳和跳槽问题,通过计算分析,设计一种新型绞车松绳压绳器,可防止出现乱绳现象.     

基于学习矢量量化网络的人脸识别

提出利用主元分析(PCA)和学习矢量量化神经网络(LVQ)相结合的方法进行人脸识别。在ORL人脸库上的实验证明，这种方法的识别效率高，对表情和姿态的变化具有一定的鲁棒性，在识别性能上优于传统的Fisherfaces脸方法和eigenfaces方法。     

城域网以太网MAC-in-MAC技术研究与应用

以太网具有交换快速、配置简单的特点，所以越来越普遍地被应用在城域网和城域骨干网中。但广播域的分离、MAC地址表项过多及用户安全性等问题，是交换网络中的一些难以避免的问题。介绍了一种可较好地解决上述问题的新技术，即MAC-in-MAC技术，同时将它与已有的城域以太网技术VLAN技术和Q-in-Q技术进行了对比。最后，提出了2种实际网络中MAC-in-MAC技术的扩展应用。     

卫星网络中的拥塞控制算法分析

卫星网络因为延时长、误码率高、链路不对称等特点,导致网络的吞吐量等性能大大下降。近年来,针对卫星网络的劣势,国内外众多专家学者在卫星网络的拥塞控制领域开展了大量的研究工作。总结卫星网络中拥塞控制算法的研究现状,提出几点改进意见,并分析了进一步的研究方向。     

基于模糊神经网络的水下机器人实时状态监测模型

本文提出了基于人工智能的自主式水下机器人推进器及舵状态监测模块结构,基于模糊神经网络技术,以模糊自适应学习控制网络为核心构筑了状态监测模型,提出了基于最大权值矩阵的规则提取及基于浮点数编码的遗传算法参数调整方法,分析了推进器及舵的状态监测过程,并基于监测模型探讨了推进器及舵工作状态的评价方法.通过计算机仿真试验,验证了本文所提方法的有效性和监测模型的可行性.     

FPGA在车载3G通信系统中的应用

介绍了车载3G通信系统的原理、功能、组成及FPGA在此方面的应用．着重阐述了利用FPGA实现带信号灯的双口RAM和通用异步串行通信控制器功能的VHDL程序设计，并给出了相应的仿真波形。     

秦沈客运专线无线通信工程设计

秦沈客运专线无线系统包括无线列调系统及数字集群移动通信系统。无线列调系统实现列车无线调度功能。数字集群移动通信系统解决移动人员 (工务、电务、桥梁、车站、公安等 )的地面通话 ,取消通话柱。在车站 ,设置基站、车站无线列调电台 ;在区间点无人看守处 ,设置光纤射频直放终端设备。