基于统计理论的多传感器信息融合方法

多传感器信息融合技术引起了工业测量系统的改变 ,是智能仪表的发展方向。针对工业中干扰的特点 ,提出一种基于统计理论的信息融合方法 ,它采用高等数学的方法求解具有多变量约束条件的极值问题 ,从而得到最小方差的信息融合结果。以此方法可以提高系统的抗干扰能力和测量准确度 ,并通过实验测量进行了仿真以表明其正确、有效。     

基于多传感器信息融合的控制系统研究

本文研究了多传感器组合系统的智能化数据融合方法。从数据处理算法、故障检测和系统可靠性等方面探讨 了基于多传感器的控制系统,并提出了一种实用的数据处理算法和故障检测算法。     

基于信息融合的入侵检测模型与方法

研究了入侵检测系统(IDS)研究现状，针对当前IDS系统误报率高和对时间及空间上分散的协同攻击无法有效检测的缺陷，引入信息融合和多传感器集成的观点，提出了一个多层次的IDS推理框架和原型系统．该原型系统采用贝叶斯网络作为多传感器融合的工具，用目标树的方法来分析协同攻击的攻击企图，并最终量化系统的受威胁程度．相比现有的IDS，该原型的结构更加完整，能够更容易发现协同攻击并有效的降低误报．     

多传感器信息融合方法综述

本文概述了在智能机器与系统领域多传感器信息融合的主要方法加权平均法,卡尔曼滤波法,贝叶斯估计,证据理论,产生式规则,模糊理论,神经网络及粗糙集理论,并指出了它们的不足及发展方向.     

基于信息融合的入侵检测系统研究

研究在入侵检测中,采用信息融合的方法,试图解决当前入侵检测系统中存在的问题。提出了用于入侵检测的信息融合模型,并应用贝叶斯网络的多书传播算法给出了信息融合的方法。采用和挑选DARPA2000中的数据作为样本,通过实验验证,基于信息融合技术的入侵检测方法能够提高检测度,降低误报率。     

基于随机自适应方法的多传感器融合算法

针对基于熵理论的贝叶斯信息融合技术需要进行无穷区间的积分运算,容易出现数值不稳定的问题,提出一种基于随机自适应方法的多传感器融合算法。利用传感器测量值之间的差值自适应地建立传感器的后验概率分布模型;结合互信息的理论实时识别和剔除伪测量值,避免求熵时的积分计算;将该方法分别应用于集中式融合方案和分布式融合方案中得到了两种新的数据融合方法。仿真实验结果表明,在存在伪测量值的情况下,该算法性能明显优于一般的贝叶斯融合方法。     

基于多传感器信息融合的控制系统研究

本文研究了多传感器组合系统的智能化数据融合方法。从数据处理算法、故障检测和系统可靠性等方面探讨了基于多传感器的控制系统,并提出了一种实用的数据处理算法和故障检测算法。     

基于信息融合的计算机网络信息发现

引入信息融合技术,利用多个探测工具收集网络信息,并将来自不同类型网络探测工具的信息在不同的层次上进行融合。在数据层融合中,使用模糊逻辑的统计方法识别系统类别、区分网络设备;在决策层融合中,通过系统知识库的支持,获得最可信的网络信息。     

基于EEMD的多传感器信息融合降噪方法

由于传感器被动采集所得信号没有太多先验信息,在后续应用过程中因噪声的存在受到限制,所以,提出一种基于总体经验模式分解（EEMD）和时延估计的多传感器信息融合降噪方法.首先将多传感器采集所得信号进行EEMD,将所得对应IMF分量进行互相关,求取时延估计值,依据时延矢量封闭准则（TDVCR）获得相应IMF分量的时延矢量误差.最后,根据多传感器综合支持度确定相应权重,对IMF分量进行重构,得到降噪后的信号.实验结果表明：该方法在EEMD的基础上有效利用了多传感器的时延估计特性,在没有任何先验信息的条件下降低了信号噪声,取得满意的效果.     

Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods.     

动态生理信息融合在人体健康评价系统的应用

针对人体健康状况实时评价问题,将生理医学理论与信息融合技术相结合,设计了一种基于动态生理信息融合的健康评价系统。利用扩展的卡尔曼滤波辅助方法进行预处理及特征提取,将模糊逻辑引进神经网络,推进了模型一致性推理过程,选取基于数值优化改进的BP算法。仿真结果及健康增进型运动平台的实际应用表明该系统能够快速、准确完成人体健康状况的评价。     

基于模糊理论的多传感器信息融合算法

D-S证据理论作为一种重要的不确定性推理理论,为处理传感器信息的模糊性及不确定性提供了很好的解决方法。但各个证据中的基本概率分配函数（mass函数）如何生成,仍是人们需要解决的问题。针对这一问题,提出了一种基于模糊理论中的高斯隶属度函数来得到传感器提供信息的可信度,计算了各个传感器之间的相互支持度;将各传感器的可信度和支持度转化成mass函数;利用证据理论对多传感器信息进行融合。仿真试验表明该方法能够有效提高识别的准确性和可靠性。     

Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network's probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Brian J. Ross is a professor of computer science at Brock University, where he has worked since 1992. He obtained his BCSc at the University of Manitoba, Canada, in 1984, his M.Sc. at the University of British Columbia, Canada, in 1988, and his Ph.D. at the University of Edinburgh, Scotland, in 1992. His research interests include evolutionary computation, language induction, concurrency, and logic programming. He is also interested in computer applications in the fine arts. Eduardo Zuviria received a BS degree in Computer Science from Brock University in 2004 and a MS degree in Computer Science from Queen's University in 2006 where he held jobs as teacher and research assistant. Currently, he is attending a Ph.D. program at the University of Montreal. He holds a diploma in electronics from a technical college and has worked for eight years in the computer industry as a software developer and systems administrator. He has received several scholarships including the Ontario Graduate Scholarship, Queen's Graduate Scholarship and a NSERC- USRA scholarship.     

多传感器分布式信息融合粒子滤波器

针对非线性非Gaussian系统的状态估计问题,提出一种基于信息融合的多传感器分布式粒子滤波算法。该算法首先利用粒子滤波方法分别计算局部传感器的状态估值,再应用分布式标量加权融合准则对状态估值进行信息融合。仿真结果表明和单传感器情形相比可提高滤波的精度。     

A driver fatigue recognition model based on information fusion and dynamic Bayesian network

We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.     

动静态信息融合及动态贝叶斯网络的步态识别

步态是远距离视频监控领域最具潜力的生物特征。目前对步态的识别研究大都是考虑单一条件下步态的识别率,但在穿外套、背包等混合条件下识别率较低,通过分析人体行走时步态的时序特征,提出一种基于动静态信息相结合的多信息融合的动态贝叶斯网络(DSIF-DBN)。模型含有3层状态,模型中每个时间片都为静态信息和动态信息的融合。此模型能很好地表达步态的时序特性,即步态行走时人体姿态,运动幅度等特征的节奏性变化。实验结果表明该方法有较高的识别率,能有机地融合步态的静态信息及动态信息,并且在有噪声及信息缺失的情况下有较好的鲁棒性,大大降低了外套及背包对步态识别的影响。     

基于耦合HMM的多传感异步信息融合分类策略 *

针对多传感异步信息融合分类问题,提出了一种新颖的基于耦合隐马尔可夫模型（CHMM）结构的中期 融合分类策略,该策略既考虑到了多传感信息在时间上的相关性,又解决了信息流之间的异步问题;其次,通过 限制信息流的状态数量和限制信息流之间的异步程度,简化了模型结构;此外,为解决CHMM的算法实现问题, 提出了一种CHMM与双流HMM的等效变换方法,从而利用经典的HMM算法解决了CHMM的模型实现。最后 在唇读语音双模态数据库上的实验证明,该异步信息融合策略实现了比早期同步融合更理想的识别结果,证明 了该     

基于RBF网络的信息融合在机器人足球中的应用

机器人足球系统是综合性的人工智能研究平台。决策在机器人足球比赛中起着至关重要的作用。通过对机器人足球系统的分析,论证了信息融合应用于机器人足球系统的可行性。针对机器人足球比赛决策中的实际问题,提出了基于径向基函数(RBF)神经网络的信息融合方法,并设计了足球机器人射门实验。实验结果证明该方法有助于提高整个系统决策的准确性。