基于置信度理论的网络知识融合系统和应用

由于网络知识资源的异构性,知识融合系统需要对多源数据、信息进行集成与整合并消除歧义.在知识融合过程中,由于知识科学的复杂性和模糊性,需要对多源知识的不一致、不完整等不确定性问题进行处理.基于此,提出了一种基于置信度理论的网络知识融合系统模型,弥补了传统知识融合系统在不确定性处理上的不足,并使用反馈自适应机制自动校正置信...     

多传感器冲突信息的加权融合算法

针对在多传感器目标识别系统中,DS规则对高冲突信息融合结果不合理的问题,提出了一种新的加权融合算法.在多源证据信息融合时,首先根据两证据距离大小来确定其相互支持度,将证据支持度矩阵模最大特征值对应的特征向量作为证据的权重向量,然后确定各证据的相对折扣因子,并修正证据信息,最后用DS规则融合.通过实验仿真对比分析了多种方法的融合效果,表明了新方法可以较好的解决高冲突信息融合的问题.     

Shape matching using relaxation techniques

The problem of finding approximate matches of pieces of shapes to parts of larger shapes is investigated. The shapes are represented by polygonal approximations. Initially, figures of merit are assigned to the matches between pairs of angles on the two shapes. Relaxation methods are then used to find acceptable combinations of these matches. This approach was tested on a data base consisting of digitized coastlines in various map projections. In nearly all cases, all matches except the correct one were eliminated by the relaxation processes.     

Application of fuzzy Naive Bayes and a real-valued genetic algorithm in identification of fuzzy model

We present a method to identify a fuzzy model from data by using the fuzzy Naive Bayes and a real-valued genetic algorithm. The identification of a fuzzy model is comprised of the extraction of “if–then” rules that is followed by the estimation of their parameters. The involved parameters include those which determine the membership function of fuzzy sets and the certainty factors of fuzzy if–then rules. In our method, as long as the fuzzy partition in the input–output space is given, the certainty factor of each rule is computed with the fuzzy conditional probability of the consequent conditioned on the antecedent by using the fuzzy Naive Bayes, which is a generalization of Naive Bayes. The fuzzy model involves the rules characterized by the highest values of certainty factors. The certainty factor of each rule is the fuzzy conditional probability, and it reflects the inner relationship between the antecedent and the consequent. In order to improve the accuracy of the fuzzy model, the real-valued genetic algorithm is incorporated into our identification process. This process concerns the optimization of the membership functions occurring in the rules. We just involve the parameters of membership function of the fuzzy sets into the real-valued genetic algorithm, since the certainty factor of each rule can be computed automatically. The performance of the model is shown for the backing-truck problem and the prediction of Mackey–Glass time series.     

Dual effect, certainty equivalence, and separation in stochastic control

In this paper the various policies in fixed end-time stochastic control are discussed first. The emphasis is on the difference between the feedback and closed-loop policies. It is shown how the closed-loop policy has the important property that it can be actively adaptive, while the feedback policy can only be passively adaptive. The feature of being actively adaptive is possible when the control has a dual effect, i.e., in addition to its effect on the state it affects the state uncertainty. The intimate connection between the neutrality (lack of dual effect) and certainty equivalence properties for a class of problems is proved. This new result is then used to widen the class of problems for which it was previously known that the certainty equivalence property holds.     

基于规则的边缘连接算法在路面病害检测中的应用

本文提出了一种基于规则的边缘连接算法,此算法对通过边缘检测提取的病害边缘块进行启发式搜索,采用基于规则的方法计算边缘连接的可信度作为连接质量的度量。通过在江苏省高速公路路面病害自动检测系统中的应用,表明此方法有较高的连接效率,同时能有效地抑制噪声。     

An algorithmic approach to combining belief functions

Methods of combination are used to synthesize pieces of evidence of equal standing that represent different aspects of a specific system about which a diagnosis is to be made. Combination is distinct from consensus, when complete diagnoses rendered by different knowledge sources require synthesis, and conditionalization, where pieces of evidence to be synthesized have dissymmetric relationships to each other. The Dempster-Shafer Rule is the quintessential combination method. However, it has been criticized for its inability to handle inconsistent pieces of evidence and for the way it focuses the weight of evidence. This article presents an alternative combination method that is capable of handling inconsistent evidence and relates evidence focusing to the amount of information resident in pieces of evidence. The method is capable of combining belief functions. Future research should address extending the method to the combination of a broad class of imprecise probability functions. © 1996 John Wiley & Sons, Inc.     

A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity.      

CERTAINTY FACTOR THEORY AND ITS IMPLEMENTATION IN AN INFORMATION SYSTEM-ORIENTED EXPERT SYSTEM SHELL

This article discusses some topics related to the MYCIN model for uncertainty handling in expert systems. Some previously identified problems in this model should be considered as evidence to support the point of view that the probabilistic interpretation for measure of belief (MB) and measure of disbelief (MD) given by Shortliffe is inappropriate. In connection lo this, some results achieved by Heckerman are also reviewed and one of his arguments is questioned. According to this argument, the original interpretation for certainty factors implies a noncommutative method for evidence combination, and therefore, this interpretation should be abandoned. Problems similar to those identified by Adams in the original interpretation for certainty factors exist also in the interpretation recommended by Heckerman. Then, in the context of previous results stating that there is an isomorphic mapping from the evidence combination scheme for the likelihood ratio to the evidence combination function adopted in EMYCIN and its successors, we point out that the evidence combination scheme for the likelihood ratio suffers from a strong restriction. We remark upon the complaint that the MYCIN model cannot always function as well as one has expected. Implementation of the certainty factor mechanism in an information system-oriented expert system shell with some substantial changes is described     

矩形件排样优化的一种近似算法

本文对理论上属于ＮＰ－完备问题的二维矩形件优化排样问题，构造了一个效率高、速度快、可令人满意的一种近似算法，该算法的主要思想是在排样过程中根据一种局部最优原则不断地动态产生一些较小的矩形，然后对这些小矩形区域排样，同时也消去一些已排过的矩形区域，直至所有的矩形件被排完，根据本文算法我们开发了一个矩形件排样系统。     

基于D-S信息融合的水下机器人地图构建

针对未知动态环境中自治水下机器人(Autonomous Underwater Vehicle,AUV)的路径规划问题,给出一种基 于D-S (Shafer-Dempster)信息融合的水下栅格地图构建算法.首先通过建立一个声纳传感器模型,将声纳数据转换成栅格的信度函数分配值;接着应用D-S证据理论信息融合算法更新地图数据,从而构建出水下动态栅格地图;最后通过真实地图与融合构建地图比较,说明D-S融合算法在地图构建中的可行性.     

Judgmental forecasting with interactive forecasting support systems

With an increasing use of DSS/EIS, managers are often required to process information coming from a variety of sources in making a final decision. However, we have little understanding of the efficiency with which people select and use the multiple pieces of information. This issue was examined under various conditions using a DSS in a forecasting task where multiple items of information were displayed on request in an interactive manner. Results indicate that overall people underacquired information. Moreover, people often selected less-reliable information. This sub-optimal behaviour did not diminish over time (it became worse). But an aggregation DSS was helpful at the task. This suggests that people seemed to have a problem in aggregating multiple pieces of information. It was also found that the independent preparation of an initial forecast improved forecast accuracy significantly. Perhaps, forecasters may prepare the initial forecast independently and use decision aids for the subsequent tasks of the forecasting process.     

Certainty aggregation and the certainty fuzzy measures

Aggregation functions are mostly used in decision‐making situations that require information fusion in a meaningful manner. The main purpose of aggregation is to turn a group of input data into a single and comprehensive one. However, in real decision‐making and system evaluation problems, the decision maker may exhibit only some amount of certainty in her decision inputs. In this study, we show how to aggregate these certainty degrees assigned to a group of inputs in an intuitive and reasonable manner. One of the interesting aspects of the problem is that the value aggregation is independent of their certainty degrees while the certainty aggregation essentially depends on both the input values and the value aggregation function. The construction of the aggregation function gives rise to a fuzzy measure that satisfies some very interesting properties. The technique presented here has wide range of applications.     

A concurrent dynamic logic of knowledge,belief and certainty for multi-agent systems

This paper extends the logic of knowledge, belief and certainty from one agent to multi-agent systems, and gives a good combination between logic of knowledge, belief, certainty in multi-agent systems and actions that have concurrent and dynamic properties. Based on it, we present a concurrent dynamic logic of knowledge, belief and certainty for MAS, which is called CDKBC logic. Furthermore, a CDKBC model is given for interpreting this logic. We construct a CDKBC proof system for the logic and show that the proof system is sound and complete, and prove that the validity problem for the system is EXPTIME-complete.     

基于证据可信度的D-S理论协作频谱感知方法

D-S证据理论频谱感知算法中,针对当协作用户数增加时所引起的报告数据量迅速增大、带宽开销增加问题,将本地测量统计量中不确定度分配到确定信息中,减少了向融合中心发送的数据量,有效降低了带宽开销。其次,针对高冲突数据对D-S证据理论融合结果影响大的问题,通过评估每个证据的可信度,将可信度作为权重来计算加权平均证据,降低了高冲突证据对融合结果的影响。仿真结果表明,所提方法在有效降低了报告带宽开销的同时,能够减少高冲突证据对融合结果的影响。     

Fisher Linear Discriminant Analysis for text-image combination in multimedia information retrieval

With multimedia information retrieval, combining different modalities – text, image, audio or video provides additional information and generally improves the overall system performance. For this purpose, the linear combination method is presented as simple, flexible and effective. However, it requires to choose the weight assigned to each modality. This issue is still an open problem and is addressed in this paper.     

可保留细节信息的全方位多级组合滤波法的研究

传统全方位多级组合滤波法在抑制噪声的同时会严重模糊图像，通过分析该问题产生的原因，提出了一种改进的滤波方法，该方法先对图像象素进行是否为边缘点、孤立噪声点或平滑区域点的预判断，然后再根据判断结果分别进行处理。实验结果表明，该方法能很好地保留图像边缘细节信息，取得了较好的处理效果。     

A fuzzy MCDM method and an application to solid transportation problem with mode preference

In this paper we propose a computationally efficient fuzzy multi-criteria decision making (FMCDM) method. For this purpose we define a ranking function based on credibility measure to rank a fuzzy number over another fuzzy number. A comparative result of our proposed ranking method with the other well known methods is provided. The proposed FMCDM method is successfully applied to find most preferred transportation mode among available modes with respect to some evaluation criteria for a solid transportation problem (STP). Here the evaluation ratings of the alternatives and criteria weights are presented in terms of linguistic variables. The importance weights of the available transportation modes as obtained by this method are then assigned to the STP. Numerical example is provided to illustrate the proposed method and problem.     

Relational database schema design for uncertain data

Driven by the dominance of the relational model, we investigate how the requirements of applications on the certainty of functional dependencies can improve the outcomes of relational database schema design. For that purpose, we assume that tuples are assigned a degree of possibility with which they occur in a relation, and that functional dependencies are assigned a dual degree of certainty which says to which tuples they apply. A design theory is developed for functional dependencies with degrees of certainty, including efficient axiomatic and algorithmic characterizations of their implication problem. Naturally, the possibility degrees of tuples bring forward different degrees of data redundancy, caused by functional dependencies with the dual degree of certainty. Variants of the classical syntactic Boyce–Codd and Third Normal Forms are established. They are justified semantically in terms of eliminating data redundancy and update anomalies of given degrees, and minimizing data redundancy of given degrees across all dependency-preserving decompositions, respectively. As a practical outcome of our results, designers can simply fix the degree of certainty they target, and then apply classical decomposition and synthesis to the set of functional dependencies whose associated degree of certainty meets the target. Hence, by fixing the certainty degree a designer controls which integrity requirements will be enforced for the application and which data will be processed by the application. The choice of the certainty degree also balances the classical trade-off between query and update efficiency on future database instances. Our experiments confirm the effectiveness of our control parameter, and provide original insight into classical normalization strategies and their implementations.     

基于证据推理规则的信息融合故障诊断方法

本文针对不确定性故障特征信息的融合决策问题,给出基于证据推理(evidence reasoning,ER)规则的故障诊断方法.首先基于故障特征样本似然函数归一化的方法求取各传感器(信息源)提供的诊断证据;从传感器误差以及故障特征对各故障类型辨别能力的差异出发,给出获取诊断证据可靠性因子的方法;给出双目标优化模型训练得到诊断证据的重要性权重,最后利用ER规则融合经可靠性因子和重要性权重修正后的诊断证据,利用融合结果进行故障决策.该方法继承了Dempster-Shafer证据理论处理不确定性信息融合问题的优点,同时克服了它在实际应用中无法区分证据可靠性和重要性的不足,使得所获诊断证据更为客观、可信.最后,通过在多功能电机转子试验台上的故障诊断实验,验证了所提方法的有效性.