南水北调东干渠工程安全监测数据分析与处理技术研究

东干渠工程是北京市南水北调配套工程之一,是中心城环线供水系统重要组成部分,承担第十水厂、通州水厂等东部区域供水任务.东干渠工程建设了安全监测系统,监测内容包括环境量、变形、渗流、应力、应变及专项监测等.但监测系统存在监测数据中断、仪器失效及缺少初值等问题.对某典型断面的应力、应变及温度等内观监测数据以及东五环段的沉降监测数据进行了梳理、对比验证分析,明确了监测数据的合理性,初步确定了沉降风险点,为东干渠工程安全监控提供技术支撑.     

水阳江、青弋江、漳河流域洪涝灾害分析及防御对策

2020年6—7月,水阳江、青弋江、漳河流域(以下简称三江流域)遭受梅雨期10轮强降雨影响,水阳江干流连续出现3次洪峰过程,全线发生超保证水位洪水、青弋江干流全线发生超警戒水位洪水,水阳江流域遭遇1999年以来最大洪水,造成宣城市直接经济损失29.57亿元。系统梳理了三江流域2020年暴雨洪水的科学应对举措,分析了三江流域上游、中游、下游存在的问题,并依据“上蓄、中疏、下排”的系统治理思路,提出了相应的治理对策与建议,以进一步提升三江流域防洪排涝能力。     

计算机便携式取证系统的设计与实现

目前,以计算机信．g-系统为犯罪对象和工具的新型犯罪活动越来越多,造成的危害也越来越大。侦破这些案件必须要用到计算机取证技术,而电子证据本身和取证过程有许多不同于传统物证和取证的特点,给司法工作和信息安全领域都提出了新的挑战。为此,文章设计开发了一款基于WindowsPE系统的便捷、实用、安全、有效的计算机便携式取证系统。电子证据的收集与处理是通过内置于u盘的WindowsPE系统来完成的;利用OpenSSL提供的编程接口和PKI相关技术实现对电子证据的保密、认证与保存等功能。最后通过测试与分析,表明该系统能实现对电子证据的收集、处理和电子证据的安全有效保存。     

城市轨道交通故障测距系统软件设计

为了对城市轨道交通牵引网短路故障点定位问题进行研究,满足牵引供电系统的可靠性要求,利用VS2003设计了城市轨道交通牵引网故障测距系统软件,接收从牵引变电所发送来的电压、电流信号,对牵引供电系统进行短路故障点定位,为快速修复线路和牵引供电系统继电保护提供依据,保证牵引供电系统的正常工作;软件基于CSerialPort类实现了牵引变电所与上位机之间的串口通讯,然后与ODBC数据库编程技术相结合,将串口数据实时保存在数据源文件中,实现了数据的实时采集和实时回放功能;最后进行联调的结果证明了该设计的有效性。     

计算机网络安全及防火墙技术分析

随着我国计算机技术的普及,网络的开放性虽然给人们带来方便,但是也给网络安全方面的问题带来了困惑。例如:计算机网络时常遭受病毒或黑客的袭击,使重要的文件和数据丢失等。为了能够更好的确保网络信息的安全,避免遭受破坏和侵犯,因此,作为保护网络安全的防火墙技术成为人们关注的焦点。该文通过简单介绍计算机网络安全及防火墙技术,对计算机网络安全及防火墙技术进行分析,并阐述了计算机网络安全中防火墙技术的运用。     

关于“军队院校教学工作评价管理系统”的质量控制

结合"军队院校教学工作评价管理系统"开发,从质量控制的角度出发,在指标体系构建、评价项目设置、评价数据提取、评价时限要求四个方面采取了一些控制措施,针对每一方面的特点和实际需要,就具体做法进行了介绍。     

基于人工神经网络的银行客户流失分析模型

针对目前中国建设银行存在的客户流失问题,利用BP人工神经网络网络稳定、学习能力强的特点,通过输入变量和输出变量之间的相关性分析,建立银行客户流失分析模型,以此获取即将流失的客户,以便银行做出经营决策,挽留有关用户,确保银行效益不受影响。实验证明,此模型能够很好的对银行客户流失进行预测分析。     

OMAPL138和FPGA的合并单元设计方案

提出了一种基于ARM9+DSP双核处理器OMAPL-138和FPGA的合并单元设计方案。给出了该方案的原理框图,以及具体的程序流程图,并在CCS开发环境下完成了软件编程,实现了合并单元按照IEC61850标准规定的格式进行数字量以太网传输。实验结果证明了该设计方案的可行性与正确性,为合并单元的设计提供了一种新的思路。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.