交通运输网络可靠性研究分析

重点分析连通可靠性、出行时间可靠性、能力可靠性、行为可靠性和潜在可靠性等评价道路网络性能的指标,在回顾和总结交通网络可靠性研究发展的基础上,针对国内外的研究现状,提出了目前研究中存在的问题,描述了该领域有待研究的方向。     

多媒体远程实时控制系统

本文给出了多媒体远程实时控制系统的分层结构模型，介绍了基于该模型，实现网络实时传输和远程监控的关键技术和实现方法。为实现实时通信，减轻网络负载，系统可以根据当前的网络状态选择合适的协议来传输通信数据，引入多线程控制和协调机制，以实现并发控制。     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

基于C/S+B/S混合模式的城市重大危险源管理系统的研究

分析城市重大危险源管理系统的功能结构,针对现阶段技术应用的局限性,笔者提出C/S+B/S混合模式的解决方案;对数据库系统、网络构架及安全性、兼容性和模型系统等关键技术进行研究;开发出基于C/S+B/S混合模式的城市重大危险源管理系统。该系统对解决城市重大危险源统一管理与布局分散的矛盾,实现重大危险源动态监管和重大事故应急指挥智能化,提高事故应急救援指挥和救援行为的效率有重要意义。     

Physical and Economic Sustainability of Water: New Approach Using the Case of the Big Lost River,Idaho

A new approach for evaluating water sustainability is introduced by comparing physical and economic sustainability. To achieve physical sustainability, water should be available in sufficient quantity and of good quality and used efficiently. The economic sustainability can be achieved by balancing between costs and values of water. The objectives of this study were to estimate the physical and economic sustainability of surface water in the Big Lost River, south–central Idaho. The study used a Bayesian network by building a graphical diagram of nodes representing all significant variables related with the sustainability, such as water demand, water quality, and the different costs and values of water. The study showed that the likelihood of the physical sustainability is less than that of the economic sustainability, which is attributed to the scarcity of water in the Big Lost River. Readers should send their comments on this paper to: BhaskarNath@aol.com within 3 months of publication of this issue.     

建筑物走廊型通道中火灾烟气流动特性的研究

应用网络模型方法,将建筑物走廊型通道合理地划分为多个节点,在火源为10kg木材的情况下,利用已开发成功的烟流性状预测软件对四川消防科研所的一座实体建筑物进行了模拟计算,获得了各节点烟流性状的发展变化规律。经比较,模型计算结果与实验数据十分符合。证明走廊型通道的多节点划分方法正确、预测软件实用可靠。根据烟气侵入走道中两节点的时间差及两节点间距,提出了烟气弥漫速度的计算方法。该算法与实测结果间的相对误差小于3%,实例应用及研究的成果,为建筑物火灾时安全疏散动态模拟研究提供了理论依据。     

基于小波神经网络的瓦斯涌出量预测研究

准确地预测瓦斯涌出量对于指导矿井设计和安全生产有重要意义,而瓦斯涌出量是一个与自然因素及开采技术等多因素有关的非线性建模问题。鉴于传统神经网络方法解决非线性问题收敛速度慢,易陷入局部最优解的缺陷,笔者提出一种既充分利用小波变换的时频局部化性质,又能结合神经网络的自学习能力的小波神经网络预测瓦斯涌出量的方法,并建立了预测模型。在此基础上,采用Delphi语言,设计了小波/BP神经网络仿真器。通过实例分析表明该方法较传统神经网络收敛迅速,预测精度高。     

基于小波神经网络的煤层底板突水非线性预测方法研究

针对煤层底板突水系统为一非线性动力学系统的特性,并在考察目前煤层底板突水预测方法的基础上,给出利用小波神经网络对煤层底板突水进行预测的可行性和优越性;阐述了小波神经网络的基本原理;提出和分析了基于小波神经网络的煤层底板突水预测模型及算法;并通过实例证明,应用小波神经网络解决煤层底板突水预测的可行性和优越性。研究及实践表明:小波神经网络的预测精度更高、更准确。     

基于贝叶斯网络的一种事故分析模型

贝叶斯网络被认为是人工智能研究中不确定性知识表示和推理的重要工具。当前在系统安全领域中已开始运用贝叶斯网络技术进行故障诊断分析，然而故障只是诱发事故的因素之一，无法系统的评价事故背后的隐患，对事故后果的预测也甚少涉及。笔者将贝叶斯网络作为一种事故分析手段，在事故致因理论的基础上提出了一种基于危险因素-事故-事故危害的三层贝叶斯网络拓扑模型；阐述了网络模型层次间的因果关联关系、各层次的构成、节点的描述方法以及网络模型的构建方法；最后通过一个天然气球罐的分析案例验证了该模型分析方法的可行性和有效性。     

MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P_(t), P_(t-1), P_(t-2), P_(t-3), antecedent runoff/lake stage R_(t-1) and air temperature T_(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P_(t)), P_(t-l), P_(t-2), P_(t-3), T_(t) and R_(t-l); Input II = Input-l less P_(t) P_(t-l), P_(t-2), P_(t-3); and Input III = Input-II less T_(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations.