相关条件下基于N-P准则的分布式检测理论研究

本文针对并行网络、二元局部判决的情况,研究了在相关条件下基于N-P准则的分布式检测融合算法,给出了在联合概率密度已知和未知两种情况下的最优融合规则的理论推导及相应的解决方案,并在实验仿真的基础上,得到了部分有益的结论。     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

江阴长江公路大桥钢箱梁体受力分析

介绍了对大桥的风向、风力和交通流量的测试依据和内容，分析了风向、风力的测试结果以及梁体在风力及交通流量等作用下的受力情况，指出钢箱梁体主要受到的力是风力。     

网络安全事件的关联分析方法的比较研究

随着当前攻击手段和技术的日益复杂化,一次入侵事件往往需要多个步骤才能完成,这些步骤都是彼此相关的。但是传统的入侵检测集中于检测底层的入侵或异常,所检测到的结果也仅仅是一次完整入侵的一部分,所以不能将不同的报警结合起来以发现入侵的逻辑步骤或者入侵背后的攻击策略。关联分析技术将不同分析器上产生的报警进行融合与关联分析,极大地减少了报警的数量,降低了入侵检测的误报率,并且适当的减少了入侵检测的漏报率。文中在对网络安全事件关联分析方法的系统结构进行分析后,着重介绍了当前比较流行的几种网络安全事件关联分析方法,最后对各种方法进行了比较研究。     

人工免疫原理在网络入侵检测系统中的研究

文章分析了人工免疫系统的原理,简单建立了一种基于否定选择算法的网络入侵检测系统,引入了一种用于异常事件识别的r-字符块匹配规则,并对规则存在的漏洞进行了分析。     

基于循环谱理论的BPSK直扩信号检测

研究了扩频信号的周期平稳性及其循环谱密度函数;在推导出BPSK扩频信号的循环谱密度函数的基础上,提出利用循环谱理论对BPSK直扩信号进行检测,并用MATLAB进行仿真验证。     

一种变步长D-Rake多用户检测器

在时变多径衰落信道下,接收到的CDMA信号功率变化较大,此时D-Rake盲自适应多用户检测器性能显著下降,将变步长LMS算法与基于主分量的相干合并引入到D-Rake(DecorrelatingRake)检测器中,构成一种变步长D-Rake,称之为VD-Rake(Variablestep-sizeDecorrelatingRake)检测器。该检测器能克服原D-Rake检测器对信号功率变化较敏感等缺点,有效地改善了D-Rake检测器的性能。     

测向定位技术准确度及误差校正

测向定位技术是无线电管理的一个重要课题。测向定位过程中的误差的种类和产生的原因很多。选择适当的方法对误差进行校正，可以有效的减小测向误差带来的损失，提高无线电管理的效率。     

基于均匀圆阵的信号二维方向角互相关估计

针对复值乘性和加性噪声同时存在的情况,提出一种基于均匀圆阵的信号二维方向角互相关估计方法.本文推导出互相关估计公式,分析了估计的统计性能,给出了最优参数选择原则.在不改变均匀圆阵阵列流形的情况下,此方法抑制了信号中的乘性和加性噪声,且计算量小.计算机仿真证明了此方法的有效性.     

Automatic fog detection and estimation of visibility distance through use of an onboard camera

In this paper, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and patented. The approach consists of dynamically implementing Koschmieder's law. Our method enables computing the meteorological visibility distance, a measure defined by the International Commission on Illumination (CIE) as the distance beyond which a black object of an appropriate dimension is perceived with a contrast of less than 5%. Our proposed solution is an original one, featuring the advantage of utilizing a single camera and necessitating the presence of just the road and sky in the scene. As opposed to other methods that require the explicit extraction of the road, this method offers fewer constraints by virtue of being applicable with no more than the extraction of a homogeneous surface containing a portion of the road and sky within the image. This image preprocessing also serves to identify the level of compatibility of the processed image with the set of Koschmieder's model hypotheses. Nicolas Hautiére graduated from the École Nationale des Travaux Publics de l'État, France (2002). He received his M.S. and Ph.D. degree in computer vision, respectively, in 2002 and 2005 from Saint-Étienne University (France). From 2002, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France. His research interests include trafic engineering, computer vision, and pattern recognition. Jean-Philippe Tarel graduated from the École Nationale des Ponts et Chaussées, Paris, France (1991). He received his Ph.D. degree in Applied Mathematics from Paris IX-Dauphine University in 1996 and he was with the Institut National de Recherche en Informatique et Automatique (INRIA) from 1991 to 1996. From 1997 to 1998, he was a research associate at Brown University, USA. From 1999, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France, and from 2001 to 2003 in the INRIA. His research interests include computer vision, pattern recognition, and shape modeling. Jean Lavenant graduated from the École Nationale des Travaux Publics de l'État, Lyon, France (2001). He received the M.S. degree in Computer Vision from Jean Monnet university of Saint-Étienne in 2001. In 2001, he was a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC). In 2002, he was a system engineer in Chicago (USA). He is currently an engineer for the french ministry of transports. Didier Aubert received the M.S. and Ph.D. degree, respectively, in 1985 and 1989 from the National Polytechnical Institut of Grenoble (INPG). From 1989--1990, he worked as a research scientist on the development of an automatic road following system for the NAVLAB at Carnegie Mellon University. From 1990–1994, he worked in the research department of a private company (ITMI). During this period he was the project leader of several projects dealing with computer vision. He is currently a researcher at INRETS since 1995 and works on Road traffic measurements, crowd monitoring, automated highway systems, and driving assistance systems for vehicles. He is an image processing expert for several companies, teaches at Universities (Paris VI, Paris XI, ENPC, ENST) and is at the editorial board of RTS (Research - Transport - Safety).