一种基于证据理论的数据融合算法

针对传感器测量值存在系统误差的情况,基于证据理论的思想,提出一种新的数据融合算法。该算法首先将所有测量值根据其与真值的偏差进行分组,并分配不同的基本信任;然后将其构成的集合视为辨识框架,进而将各个测量值转换为相应的证据并进行证据组合,所得合成证据的Mass函数即为各个测量值的权值分配函数;最后对所得分组融合测量值进行加权求和,即得融合结果。仿真结果验证了该算法的有效性。     

A peer-to-peer collaboration framework for multi-sensor data fusion

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.     

A novel leader following consensus approach for multi-agent systems with data loss

International Journal of Control, Automation and Systems - The objective of this paper is to develop a novel consensus algorithm or protocol for multi-agent systems in the event of communication...     

A novel hybrid algorithm for feature selection

Feature selection is an important filtering method for data analysis, pattern classification, data mining, and so on. Feature selection reduces the number of features by removing irrelevant and redundant data. In this paper, we propose a hybrid filter–wrapper feature subset selection algorithm called the maximum Spearman minimum covariance cuckoo search (MSMCCS). First, based on Spearman and covariance, a filter algorithm is proposed called maximum Spearman minimum covariance (MSMC). Second, three parameters are proposed in MSMC to adjust the weights of the correlation and redundancy, improve the relevance of feature subsets, and reduce the redundancy. Third, in the improved cuckoo search algorithm, a weighted combination strategy is used to select candidate feature subsets, a crossover mutation concept is used to adjust the candidate feature subsets, and finally, the filtered features are selected into optimal feature subsets. Therefore, the MSMCCS combines the efficiency of filters with the greater accuracy of wrappers. Experimental results on eight common data sets from the University of California at Irvine Machine Learning Repository showed that the MSMCCS algorithm had better classification accuracy than the seven wrapper methods, the one filter method, and the two hybrid methods. Furthermore, the proposed algorithm achieved preferable performance on the Wilcoxon signed-rank test and the sensitivity–specificity test.     

基于互联网络的决策支持系统模型

基于广域互联网络的决策支持系统取代传统的Ｃ／Ｓ模式的局域决策支持是决策支持系统得以进一步发展的必然。文章讨论实施基于Ｉｎｔｅｒｎｅｔ的决策支持系统的优点和关键技术,给出基于广域互联网络决策支持系统的模型。     

A geometric framework for data fusion in information retrieval

Data fusion in information retrieval has been investigated by many researchers and a number of data fusion methods have been proposed. However, problems such as why data fusion can increase effectiveness and favorable conditions for the use of data fusion methods are poorly resolved at best. In this paper, we formally describe data fusion under a geometric framework, in which each component result returned from an information retrieval system for a given query is represented as a point in a multi-dimensional space. The Euclidean distance is the measure by which the effectiveness and similarity of search results are judged. This allows us to explain all component results and fused results using geometrical principles. In such a framework, score-based data fusion becomes a deterministic problem. Several interesting features of the centroid-based data fusion method and the linear combination method are discussed. Nevertheless, in retrieval evaluation, ranking-based measures are the most popular. Therefore, this paper investigates the relation and correlation between the Euclidean distance and several typical ranking-based measures. We indeed find that a very strong correlation exists between these. It means that the theorems and observations obtained using the Euclidean distance remain valid when ranking-based measures are used. The proposed framework enables us to have a better understanding of score-based data fusion and use score-based data fusion methods more precisely and effectively in various ways.     

A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding

The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.     

A software architecture and framework for Web-based distributed Decision Support Systems

This paper presents an integrated method to help design and implement a Web-based Decision Support Systems (DSS) in a distributed environment. First, a layered software architecture is presented to assist in the design of a Web-based DSS. The layered software architecture can provide a formal and hierarchical view of the Web-based DSS at the design stage. Next, a component-based framework is presented to implement the Web-based DSS in a distributed environment. Finally, an instance of the layered software architecture and 3CoFramework applied to the Web-based National Agricultural Decision Support System (NADSS) is presented.     

数据仓库在森林资源决策支持系统中的应用

数据仓库技术是目前数据库技术研究的热点问题之一。介绍了数据仓库的设计和联机分析处理，提出了建立全国性的分布式森林资源数据仓库的方案。讨论了如何在Microsoft SQL Server 2000中开发基于数据仓库的森林资源决策支持系统。     

数据仓库在森林资源决策支持系统中的应用

数据仓库技术是目前数据库技术研究的热点问题之一。介绍了数据仓库的设计和联机分析处理,提出了建立全国性的分布式森林资源数据仓库的方案。讨论了如何在Microsoft SQL Server 2000中开发基于数据仓库的森林资源决策支持系统。     

A hybrid fusion framework to multi-modal bio metric identification

Multimedia Tools and Applications - In the recent decade, comprehensive research efforts have been carried out as the promising modality of bio metrics on humans’ physical features for person...     

A novel biclustering of gene expression data based on hybrid BAFS-BSA algorithm

Multimedia Tools and Applications - As one of usual concepts, co-expressed genes can represent co-regulated genes in gene expression data. This strategy can be refined further because co-expression...     

A framework for simulating real-time multi-agent systems

In this paper, we describe an implementation of use in demonstrating the effectiveness of architectures for real-time multi-agent systems. The implementation provides a simulation of a simplified RoboCup Search and Rescue environment, with unexpected events, and includes a simulator for both a real-time operating system and a CPU. We present experimental evidence to demonstrate the benefit of the implementation in the context of a particular hybrid architecture for multi-agent systems that allows certain agents to remain fully autonomous, while others are fully controlled by a coordinating agent. In addition, we discuss the value of the implementation for testing any models for the construction of real-time multi-agent systems and include a comparison to related work.

采购决策支持系统数据仓库技术的研究

通过对决策支持系统和数据仓库技术的研究,提出了基于数据仓库的采购决策支持系统,并针对医院药品流通领域的特点设计了采购决策支持系统数据仓库模型。主要研究了数据仓库中的数据组织和设计数据仓库的关键技术,最后给出了数据仓库模型的设计方案     

基于Multi-Agent的装备保障决策支持系统

现代战争条件下,装备综合保障目标和约束的复杂度急剧增加,采用传统方法所建立的问题模型往往求解困难,而且不易于理解和维护.提出了一个基于Multi-Agent的装备保障决策支持系统,可实现复杂保障问题的规约、求解、组合、划分、反馈和动态控制,并给出了一个应用该系统进行保障规划问题求解的实例.系统采用基于Agent构件的分布式体系结构,高层Agent可由低层Agent组装而成.系统顶层使用了7类Agent,其中Decision-Maker和Solver进一步采用了基于Multi-Agent的A-Team框架来实现保障任务控制和问题求解,有效地提高了问题求解的效率和可靠性.     

A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion

Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.     

一种求解多目标优化问题的进化算法混合框架

为了提高多目标优化算法的求解性能,提出一种启发式的基于种群的全局搜索与局部搜索相结合的多目标进化算法混合框架.该框架采用模块化、系统化的设计思想,不同模块可以采用不同策略构成不同的算法.采用经典的改进非支配排序遗传算法(NSGA-II)和基于分解的多目标进化算法(MOEA/D)作为进化算法的模块算法来验证所提混合框架的有效性.数值实验表明,所提混合框架具有良好性能,可以兼顾算法求解的多样性和收敛性,有效提升现有多目标进化算法的求解性能.     

一种数据融合算法评估平台

传感器的多样性、战场环境的复杂性给数据融合算法的选择带来了困难,针对这种情况,介绍了一种多传感器数据融合算法测试平台,对融合算法进行了定量分析评估,为融合算法的选择使用提供了一种参考。     

A hierarchical methodology framework for multisource data fusion in vegetation classification

This Letter presents a new methodological framework for a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery. The uniqueness of the approach is that the overall structure of the fusion system is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors. The framework consists of two key components: an automated multisource image registration system and a hierarchical model for multi-sensor and multi-temporal data fusion.     

Actor-Critic框架下一种基于改进DDPG的多智能体强化学习算法

现实世界的人工智能应用通常需要多个agent协同工作,人工agent之间有效的沟通和协调是迈向通用人工智能不可或缺的一步.以自主开发的警员训练虚拟环境为测试场景,设定任务需要多个不同兵种agent小队互相协作或对抗完成.为保证沟通方式有效且可扩展,提出一种混合DDPG(Mi-DDPG)算法.首先,在Actor网络加入双向循环神经网络(BRNN)作为同兵种agent信息交流层;然后,在Critic网络加入其他兵种agent信息来学习多agent协同策略.另外,为了缓解训练压力,采用集中训练,分散执行的框架,同时对Critic网络里的Q函数进行模块化处理.实验中,在不同的场景下用Mi-DDPG算法与其他算法进行对比,Mi-DDPG在收敛速度和任务完成度方面有明显提高,具有在现实世界应用的潜在价值.