一种基于可传递置信模型的分布智能体决策融合方法*

在分析与研究对抗性多机器人系统决策问题的基础上,提出了一种基于可传递置信模型的多智能体决策融合方法;构建了决策融合体系架构,分别设计了基于证据推理的观测智能体模型,基于TBM的决策智能体模型以及决策融合中心模型,给出了相应的算法。通过在机器人足球中的应用及仿真实验,体现了本方法在对抗性多机器人系统中决策制定的良好性能及效果。     

Assessing sensor reliability for multisensor data fusion within the transferable belief model.

This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.     

Diagnostic reasoning under conflicting data: the application of the transferable belief model

One of the issues in diagnostic reasoning is inferring about the location of a fault in cases where process data carry inconsistent or even conflicting evidence. This problem is treated in a systematic way by making use of the transferable belief model (TBM), which represents an approximate reasoning scheme derived from the Dempster–Shafer theory of evidence. The key novelty of TBM concerns the paradigm of the open world, which turns out to lead to a new means of assigning beliefs to anticipated fault candidates. Thus, instead of being ignored, inconsistency of data is displayed in a portion of belief that cannot be allocated to any of the suspected faults but rather to an unknown origin. This item of belief is referred to as the strength of conflict (SC). It is shown in this paper that SC can be interpreted as a degree of confidence in the diagnostic results, which seems to bring a new feature to diagnostic practice. The basics of TBM are reviewed in the paper and the implementation of the underlying ideas in the diagnostic reasoning context is presented. An important contribution concerns the extension of basic TBM reasoning from single observations to a batch of observations by employing the idea of discounting of evidence. The application of TBM to fault isolation in a gas–liquid separation process clearly shows that extended TBM significantly improves the performance of the diagnostic system compared to ordinary TBM as well as classical Boolean framework, especially as regards diagnostic stability and reliability.     

A distributed controller approach for delay-independent stability of networked control systems

This article introduces a novel distributed controller approach for networked control systems (NCS) to achieve finite gain L₂ stability independent of constant time delay. The proposed approach represents a generalization of the well-known scattering transformation which applies for passive systems only. The main results of this article are (a) a sufficient stability condition for general multi-input-multi-output (MIMO) input-feedforward-output-feedback-passive (IF-OFP) nonlinear systems and (b) a necessary and sufficient stability condition for linear time-invariant (LTI) single-input-single-output (SISO) systems. The performance advantages of the proposed approach are reduced sensitivity to time delay and improved steady state error compared to alternative known delay-independent small gain type approaches. Simulations validate the proposed approach.     

Target identification based on the transferable belief model interpretation of dempster-shafer model

This paper explains how multisensor data fusion and target identification can be performed within the transferable belief model (TBM), a model for the representation of quantified uncertainty based on belief functions. We present the underlying theory, in particular the general Bayesian theorem needed to transform likelihoods into beliefs and the pignistic transformation needed to build the probability measure required for decision making. We present how this method applies in practice. We compare its solution with the classical one, illustrating it with an embarrassing example, where the TBM and the probability solutions completely disagree. Computational efficiency of the belief-function solution was supposedly proved in a study that we reproduce and we show that in fact the opposite conclusions hold. The results presented here can be extended directly to many problems of data fusion and diagnosis.     

一种分布式多媒体数据仓库模型

多媒体数据采掘是从大型多媒体数据库中提取高层的多媒体信息及知识。介绍一种分布式多媒体数据仓库模型──ＣＭＳ模型的体系结构及存储结构,并构造了具有综合处理各种媒体数据,能够对这些媒体数据进行修改、查询的军用多媒体数据仓库原型系统。     

Cyber-security in networked and distributed model predictive control

Distributed model predictive control (DMPC) schemes have become a popular choice for networked control problems. Under this approach, local controllers use a model to predict its subsystem behavior during a certain horizon so as to find the sequence of inputs that optimizes its evolution according to a given criterion. Some convenient features of this method are the explicit handling of constraints and the exchange of information between controllers to coordinate their actuation and minimize undesired mutual interactions. However, we find that schemes have been developed naively, presenting flaws and vulnerabilities that malicious entities can exploit to gain leverage in cyber-attacks. The goal of this work is to raise awareness about this issue by reviewing the vulnerabilities of DMPC methods and surveying defense mechanisms. Finally, several examples are given to indicate how these defense mechanisms can be implemented in DMPC controllers.     

Approximating probability distribution of circuit performance function for parametric yield estimation using transferable belief model

This paper applies the transferable belief model （TBM） interpretation of the Dempster-Shafer theory of evidence to approximate distribution of circuit performance function for parametric yield estimation. Treating input parameters of performance function as credal variables defined on a continuous frame of real numbers, the suggested approach constructs a random set-type evidence for these parameters. The corresponding random set of the function output is obtained by extension principle of random set. Within the TBM framework, the random set of the function output in the credal state can be transformed to a pignistic state where it is represented by the pignistic cumulative distribution. As an approximation to the actual cumulative distribution, it can be used to estimate yield according to circuit response specifications. The advantage of the proposed method over Monte Carlo （MC） methods lies in its ability to implement just once simulation process to obtain an available approximate value of yield which has a deterministic estimation error. Given the same error, the new method needs less number of calculations than MC methods. A track circuit of high-speed railway and a numerical eight-dimensional quadratic function examples are included to demonstrate the efficiency of this technique.     

Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks

With the rapid development of applications for wireless sensor networks, efficient data aggregation methods are becoming increasingly emphasized. Many researchers have studied the problem of reporting data with minimum energy cost when data is allowed to be aggregated many times. However, some aggregation functions used to aggregate multiple data into one packet are unrepeatable; that is, every data is aggregated only at most once. This problem motivated us to study reporting data with minimum energy cost subject to that a fixed number of data are allowed to be aggregated into one packet and every data is aggregated at most once. In this paper, we propose novel data aggregation and routing structures for reporting generated data. With the structures, we study the problem of scheduling data to nodes in the networks for data aggregation such that the energy cost of reporting data is minimized, termed MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING. In addition, we show that MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING is NP-complete. Furthermore, a distributed data scheduling algorithm is proposed accordingly. Simulations show that the proposed algorithm provides a good solution for MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING.     

基于小波变换的分布式WSN数据融合模型研究

针对无线传感器网络节点硬件、能量受限问题,分析现有数据融合方法,提出一种基于小波变换的分布式WSN数据融合模型,该模型对现有环结构模型进行改进,并加入数据验证环节,策略性地减少传输的数据量,并利用小波提升方案对数据进行融合处理,降低数据表示和传输的复杂度。通过仿真实验,证明了DDA-WT算法能有效降低网络节点的能耗,延长整体网络的生存期。     

A review of distributed architectures for networked virtual reality

The aims of this paper are twofold. First, it identifies the general requirements of future large-scale distributed virtual reality (VR) systems based on an analysis of current VR systems, of more general distributed systems platforms and a consideration of the key issues of scale and heterogeneity. These requirements subsequently inform the development of a general VR reference architecture; and a framework which identifies the key software components which will comprise future distributed VR systems. Second, it uses this reference architecture as a vehicle for conducting a broad review of current distributed VR products and research prototypes. The review covers twelve well known VR systems and intended as a general resource for researchers entering the field. These systems are: AVIARY, BrickNet, DIVE, dVS, MASSIVE, the MR Toolkit, NPSNET, Superscape, VEOS, VUE, WAVES and World Toolkit. The review also identifies relevant standards in the areas of computer graphics and distributed systems. The paper finishes by drawing out a number of more general conclusions from the review including the urgent need to conduct research into the subjects of security and resource discovery for distributed VR systems.     

A self-tuning adaptive distributed observer approach to the cooperative output regulation problem for networked multi-agent systems

In this paper, we first propose a self-tuning distributed observer for a multi-agent system, which is capable of providing the estimation of the leader's signal to various followers without assuming all the followers know the system matrix of the leader. We then further develop a novel adaptive distributed control law to solve the cooperative output regulation problem for linear heterogeneous multi-agent systems. This control law offers two advantages in that it makes less use of the information of the network as well as the information of the leader and external disturbances, and it avoids calculating the observer gain.     

A Shortest-route approach for optimum aggregation of data with an application to income data

This paper addresses the problem of grouping a large volume of discrete data into a given number of groups. The algorithm is based on the well-known shortest path algorithm. It handles different criteria for minimizing the loss of information which might result during the process of grouping. The given problem is transformed into a directed graph and it then looks for a shortest path with a given number of edges. The algorithm is illustrated through an example of five data items. It has also been coded in Pascal and some test results are presented.     

无线传感网中一种改进的分布式数据聚集调度算法

针对无线传感器网络中实时数据收集具有较高的延时问题, 提出了一种改进的无通信冲突的分布式数据聚集调度近似算法。该算法首先在最大独立集的基础上建立一棵根在sink的数据聚集树, 然后各个节点按数据聚集树分层进行数据调度。在数据聚集树的构造过程中, 对于两个相距两跳的支配点, 它们共同的、相距两跳的支配点, 通过距sink最近的支配点加入数据聚集树; 而在数据调度过程中, 采用一种新的选择标准从竞争集中选择节点进行数据调度。通过这两方面的改进, 有效地降低了数据的聚集延时。理论分析表明, 该算法的延时上界为14R+Δ; 仿真模拟的结果表明, 该算法产生的数据聚集延时远低于现有算法。     

基于分布式认证的完整性保护数据融合方案

针对无线传感器网络(WSN)数据融合中存在的数据完整性保护问题,在双簇头安全融合（DCSA）方案的基础上,提出一个安全高效的数据融合方案,通过在节点间加入对称密钥,采用分布式验证方法,使节点认证与融合同时进行,在向上融合的过程中立即证实子节点融合数据的完整性。同时,利用红黑簇头的监督特性,能够定位恶意节点,增强抗共谋攻击能力。实验结果表明,所提方案在保证与DCSA相同安全等级的前提下,能够及早检测并丢弃错误数据,提高了完整性检测机制的效率,且具有更低的网络能耗。     

A belief classification rule for imprecise data

The classification of imprecise data is a difficult task in general because the different classes can partially overlap. Moreover, the available attributes used for the classification are often insufficient to make a precise discrimination of the objects in the overlapping zones. A credal partition (classification) based on belief functions has already been proposed in the literature for data clustering. It allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. In this paper, we propose a new belief classification rule (BCR) for the credal classification of uncertain and imprecise data. This new BCR approach reduces the misclassification errors of the objects difficult to classify by the conventional methods thanks to the introduction of the meta-classes. The objects too far from the others are considered as outliers. The basic belief assignment (bba) of an object is computed from the Mahalanobis distance between the object and the center of each specific class. The credal classification of the object is finally obtained by the combination of these bba’s associated with the different classes. This approach offers a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this BCR method with respect to other classification methods.     

A stabilizing model predictive control for networked control system with data packet dropout

A constrained model predictive control （MPC） algorithm for networked control system with data packet dropout is proposed in this paper. A buffer is designed to store the predicted control sequence between controller and actuator. It is shown that if the control horizon of MPC is not less than the number of data packets lost continuously, feasibility of MPC at initial time implies asymptotical stability of the closed-loop system. A simulation example illustrates the effectiveness of the proposed approach.     

面向分布式在线学习的共享数据方法

分布式数据流已成为现代数据驱动应用产生数据的主要形式,而局部节点的数据虽然独立存储,但彼此之间是相互关联的,因此如何高效地共享局部节点数据来构建全局学习器是分布式在线学习的关键问题.针对此问题,提出一种分布式在线学习的数据共享解决方案,包括基于指数损失的半监督聚类方法和基于协方差矩阵与均值向量的数据共享方法,并证明重构...