计算机局域网络保密措施

随着计算机的普及 ,局域网络越来越广为利用 ,但也出现了相应的失密问题。本文从信息及实体两个方面 ,论述了局域网络的保密措施。     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

隧洞施工通风系统事故树分析

在采矿、铁路、公路及水利水电工程中，常常采用长距离隧洞或隧道来穿越高山。而隧洞的施工以其施工技术复杂、难度大、风险高，经常成为控制工程工期的主要环节，隧洞施工中的通风换气系统，关系到施工能否顺利进行和施工作业人员的人身安全，根据隧洞施工通风系统的运行特点，采用安全系统分析中的事故树（FTA）方法，对隧洞施工中通风系统的安全性进行定性分析，找出影响通风安全的主要因素，并提出了通风安全管理对策，可以保证施工作业的顺利进行和人身安全，提高整个施工作业的安全度。     

计算流体力学(CFD)中的迭代法及其并行计算方法

应用计算流体力学 (CFD)方法分析事故原因已被广泛采用 ,笔者针对事故理论分析和流体计算过程中 ,运用CFD方法所存在计算量大的问题 ,分析和讨论了几种古典迭代法及其并行计算方法     

关于重大隐患监控GIS模式的研究

系统地分析了重大危险源、事故隐患的地域监管体系以及隐患监控的新技术应用基础 ;提出了职业安全卫生监管体系中的危险源和隐患监控 ,应该分为过程安全监控和安全目标监控两个层次。隐患监管功能的有效实现 :隐患监控的技术载体则越来越依托信息网络的发展 ;重大隐患监管体系特别适合采用地理信息系统     

提高交通安全——实现智能汽车的研究

主要介绍了通过智能汽车的研究 ,提高城市交通安全的必要性 ;综述了发达国家关于智能汽车研究的现状与趋势 ,并通过国外智能汽车发展的趋势 ;进一步论证智能汽车的研究是汽车工业发展的方向 ,也是提高交通安全的重要手段之一。此外 ,论文提出利用模式识别技术的智能汽车的结构框架 ;指出神经网络方法应用与模式识别技术结合的优势。最后 ,论文在阐述我国需要发展智能汽车的同时 ,提出应结合中国国情相应研究相关理论 ,为今后的实施奠定基础的近期目标 ,并提出展望     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

ADJUSTMENT OF STREAM CHANNEL CAPACITY FOLLOWING DAM CLOSURE,YEGUA CREEK,TEXAS1

ABSTRACT: In Yegua Creek, a principal tributary of the Brazos River in Texas, surveys of a 19 km channel reach downstream of Somerville Dam show that channel capacity decreased by an average of 65 percent in a 34 year period following dam closure. The decrease corresponds with an approximately 85 percent reduction in annual flood peaks. Channel depth has changed the most, decreasing by an average of 61 percent. Channel width remained stable with an average decrease of only 9 percent, reflecting cohesive bank materials along with the growth of riparian vegetation resulting from increased low flows during dry summer months. Although large changes in stream channel geometry are not uncommon downstream of dams, such pronounced reductions in channel capacity could have long‐term implications for sediment delivery through the system.     

垃圾场渗出污水厌氧处理的可行性研究

采用血清瓶静态试验法研究了垃圾渗出污水的厌氧处理的可行性。试验表明:垃圾渗出污水对厌氧发酵微生物无抑制作用,有良好的厌氧降解性,产气率为0.321/g·COD,污水中的大部份有机物都能被厌氧消化,COD_cr去除率为78.2%,发酵过程中还能获得能源——沼气。具有良好的应用前景。