Reliability-based design optimization of problems with correlated input variables using a Gaussian Copula

The reliability-based design optimization (RBDO) using performance measure approach for problems with correlated input variables requires a transformation from the correlated input random variables into independent standard normal variables. For the transformation with correlated input variables, the two most representative transformations, the Rosenblatt and Nataf transformations, are investigated. The Rosenblatt transformation requires a joint cumulative distribution function (CDF). Thus, the Rosenblatt transformation can be used only if the joint CDF is given or input variables are independent. In the Nataf transformation, the joint CDF is approximated using the Gaussian copula, marginal CDFs, and covariance of the input correlated variables. Using the generated CDF, the correlated input variables are transformed into correlated normal variables and then the correlated normal variables are transformed into independent standard normal variables through a linear transformation. Thus, the Nataf transformation can accurately estimates joint normal and some lognormal CDFs of the input variable that cover broad engineering applications. This paper develops a PMA-based RBDO method for problems with correlated random input variables using the Gaussian copula. Several numerical examples show that the correlated random input variables significantly affect RBDO results.     

Large-scale network intrusion detection based on distributed learning algorithm

As network traffic bandwidth is increasing at an exponential rate, it’s impossible to keep up with the speed of networks by just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the pressure on network intrusion detection (NIDS) platforms, so the continuous increasing speed and throughput of network poses new challenges to NIDS. To make NIDS usable in Gigabit Ethernet, the ideal policy is using a load balancer to split the traffic data and forward those to different detection sensors, which can analyze the splitting data in parallel. In order to make each slice contains all the evidence necessary to detect a specific attack, the load balancer design must be complicated and it becomes a new bottleneck of NIDS. To simplify the load balancer this paper put forward a distributed neural network learning algorithm (DNNL). Using DNNL a large data set can be split randomly and each slice of data is presented to an independent neural network; these networks can be trained in distribution and each one in parallel. Completeness analysis shows that DNNL’s learning algorithm is equivalent to training by one neural network which uses the technique of regularization. The experiments to check the completeness and efficiency of DNNL are performed on the KDD’99 Data Set which is a standard intrusion detection benchmark. Compared with other approaches on the same benchmark, DNNL achieves a high detection rate and low false alarm rate.     

Extraction of genomic DNA and detection of single nucleotide polymorphism genotyping utilizing an integrated magnetic bead-based microfluidic platform

This study presents a new magnetic bead-based microfluidic platform, which integrates three major modules for rapid leukocytes purification, genomic DNA (gDNA) extraction and fast analysis of genetic gene. By utilizing microfluidic technologies and magnetic beads conjugated with CD_15/45 antibodies, leukocytes in a human whole blood sample can be first purified and concentrated, followed by extraction of gDNA utilizing surface-charge switchable, DNA-specific, magnetic beads in the lysis solution. Then, specific genes associated with genetic diseases can be amplified by an on-chip polymerase chain reaction (PCR) process automatically. The whole pretreatment process including the leukocytes purification and gDNA extraction can be performed in an automatic fashion with the incorporation of the built bio-separators consisting of microcoils array within less than 20 min. The detection of single nucleotide polymorphism (SNP) genotyping of methylenetetra-hydrofolate reductase (MTHFR) C677T region associated with an increased risk of genetic diseases was further performed to demonstrate the capability of the proposed system. The extracted gDNA can be transported into a micro PCR chamber for on-chip fast nucleic acid amplification of detection genes with minimum human intervention. Hence, the developed system may provide a powerful automated platform for pretreatment of human leukocytes, gDNA extraction and fast analysis of genetic gene.     

地磁场在缺陷微磁检测中的作用分析

地磁场在缺陷微磁检测中的作用是实现微磁检测定量化的理论基础;通过微磁学理论分析地磁场反复磁化铁磁性物质的物理过程,由于磁畴壁的移动,在缺陷区产生固定磁畴结点,固定磁畴结点内的磁场在缺陷处发生泄漏,形成可识别的缺陷信号,固定磁畴结点一旦产生其磁场强度远大于地磁场,地磁场对缺陷微磁检测的影响只会改变检测信号的幅值,不会改变缺陷信号的特征,揭示了缺陷磁场失而复得的原因,同时实验验证了地磁场在应力致磁效应中起到偏置磁场的作用,为在役设备微磁检测奠定了理论基础。     

空时互反馈人脸检测与跟踪系统的算法与实现

针对视频序列人脸检测与跟踪算法复杂、实时性要求较强这一相互矛盾的特点,设计了基于数字媒体处理器DM642的空时互反馈人脸检测与跟踪系统;在空域,利用主成分分析和最优阈值检测人脸;在时域,根据前后帧概率密度函数的相对距离确定跟踪目标,利用卡尔曼滤波器对检测出人脸的位置进行预测,形成了空时互反馈闭环算法;实践表明,该系统运行稳定可靠,能满足实时视频监控系统的要求。     

一种时频域联合捕捉主动声纳信号的方法

介绍了一种可应用于被动声纳的时频域联合捕捉主动声纳信号的方法及其工作流程。仿真相同噪声环境下使用该方法的发现距离与主动声纳探测距离,仿真结果表明使用该方法的发现距离远超过主动声纳探测距离,使用该方法可实现对目标的远程发现。     

一种基于多Agent的分布式入侵检测系统设计

在分析现有基于Agent的入侵检测系统的基础上,提出了一种基于多Agent分布式入侵检测系统模型。该模型采用了分布检测、分布响应的模式,各Agent之间具有良好的相对独立性。通过多Agent技术的思想建立系统总体结构,给出了模型的各个组成部分,并对结构中各种Agent与中心控制台的功能设计进行了分析。同时对涉及到特征匹配算法、动态选举算法、协同算法进行了初步的设计与分析。系统可充分利用各Agent的协同完成入侵检测任务,实时响应,可有效地改进传统IDS。     

实时嵌入式信息系统的网格体系结构研究

网络作为实时嵌入式信息系统集成和应用的基础设施,已经成为实时嵌入式信息系统建设的关键。为了解决复杂背景下各系统间的互操作和集成问题,分析了目前主流的商业网格体系结构,指出基于Web Service的网格技术在该背景下应用所存在问题,提出了一种将网格、面向服务架构、智能代理技术结合的混合网格体系结构。     

SJA1000内寄存器的VHDL语言读写设计与应用

SJA1000是一种独立的CAN总线控制器,主要应用于工业环境中的区域网络控制。随着FPGA的广泛应用,CAN总线与FPGA的应用日益加强。文章从状态机的角度,介绍SJA1000内部寄存器的硬件语言读写时序的实现和应用,详细描述状态机的工作原理和相应的VHDL代码。     

不停车收费车道控制系统的优化设计及实现

车道控制系统作为负责收费车道数据采集、交通控制以及与上级系统通信的前端系统,是ETC系统能否正常有效运行的关键。文章详细阐述了ETC车道控制系统的主要功能及设计原则,给出了ETC车道构成、车道控制系统的模块结构及工作流程。实践表明,该系统功能完善、稳定可靠、符合国情,具有广泛的应用前景。