小波融合图像效果的因子分析评价方法

因子分析是一种有效的评价融合图像质量的方法。文中采用了小波sym5进行了多尺度图像融合的研究,并且通过主成分分析方法提取了细节信息因子和光谱信息流失因子等参数,作为图像的客观质量评价标准。实验证明,对于小波变换多尺度融合后的图像,该方法能够客观有效地评价其反映细节信息的参数（如信息熵和清晰度）以及反映光谱信息的参数（如扭曲程度和偏差指数）的变化趋势。理论分析结果,因子综合得分和观众评价得分结果具有良好的一致性。     

A probabilistic reasoning model: Formulation and control strategy

It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.     

Conflicting information integration for decision support

The integration of information from multiple sources is a topic of increasing importance to the design of advanced decision support systems for a variety of situation assessment tasks. The integration of information is especially difficult when the multiple sources provide conflicting information. In general, approaches to this problem have used either probability-based techniques or techniques based on one of the newer theories of uncertainty. This paper surveys these current approaches to the conflicting information problem and then describes a new approach based on information theory.     

Nonparametric time series forecasting with dynamic updating

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques. When data in the most recent year are partially observed, we improve point forecast accuracy by using dynamic updating methods. We also introduce a nonparametric approach to construct prediction intervals of updated forecasts, and compare the empirical coverage probability with an existing parametric method. Our approaches are data-driven and computationally fast, and hence they are feasible to be applied in real time high frequency dynamic updating. The methods are demonstrated using monthly sea surface temperatures from 1950 to 2008.     

All linear methods are equal—and extendible to (some) nonlinearities

Several linear methods of residual generation for fault detection and diagnosis are reviewed. The parity relation approach is introduced in some detail, for both additive and parametric faults. The Chow–Willsky scheme, various diagnostic observers and principal component analysis are compared to the additive version. The ‘local approach’ and the least‐squares estimation of parameter changes are shown to be related to the parametric variant. Nonlinear extensions are demonstrated for all the techniques under additive faults. Copyright © 2002 John Wiley & Sons, Ltd.     

Hierarchical fuzzy rule based systems using an information theoretic approach

This paper proposes a novel anytime algorithm for the construction of a Hierarchical Fuzzy Rule Based System using an information theoretic approach to specialise rules that do not effectively model the decision space. The amount of uncertainty tolerated within the decision provides a single tuneable parameter to control the trade off between accuracy and interpretability. The algorithm is empirically compared with existing methods of function approximation and is demonstrated on a mobile robot application in simulation.     

稀疏低秩双线性判别模型及其应用

"高维度小样本"问题是模式识别应用中的主要障碍之一。跨越这一障碍的有效方法之一是采用参数矩阵的低秩逼近,目的是控制模型复杂度。常用的低秩逼近方法需要预先指定目标矩阵秩的大小(如主成分分析)。提出了一种新的基于稀疏约束的低秩判别模型,此模型通过对目标参数进行矩阵分解,然后分别对子成分施加低秩(稀疏)约束,从而达到低秩逼近的目的。进一步将这一思想嵌入一个双边判别模型,并用坐标下降法对目标函数进行优化,使得算法在低秩逼近的同时还有效利用了输入数据的空间特性,从而得到更好的推广性能。其有效性在一个安全生物识别应用上得到了验证。     

基于决策图的Web数据库访问优化策略

文章提出了一种用来解决Web数据库访问优化问题的决策方法的新应用。这种基于图形结构的优化方法借助了香农信息论手段,能够减少数据访问的时间,实现对大型数据库的快速查询,降低数据库的设计成本。     

Subtyping and polymorphism in object-role modelling

Although Entity-Relationship (ER) modelling techniques are commonly used for information modelling, Object-Role Modelling (ORM) techniques are becoming increasingly popular, partly because they include detailed design procedures providing guidelines for the modeller. As with the ER approach, a number of different ORM techniques exist. In this paper, we propose an integration of two theoretically well founded ORM techniques: FORM and PSM. Our main focus is on a common terminological framework, and on the notion of subtyping. Subtyping has long been an important feature of semantic approaches to conceptual schema design. It is also the concept in which FORM and PSM differ the most in their formalization. The subtyping issue is discussed from three different viewpoints covering syntactical, identification, and population issues. Finally, a wider comparison of approaches to subtyping is made, which encompasses other ER-based and ORM-based information modelling techniques, and highlights how formal subtype definitions facilitate a comprehensive specification of subtype constraints.     

Comparative study of the performance of a non-parametric self-consistent estimator with that of maximum likelihood estimator under random censorship of a bivariate survival function

Traditional approaches are dealt with for data analysis in bioreliability based on parametric or non-parametric statistical techniques. Parametric techniques for censored data lead to cumbersome results and too many complications; the non-parametric techniques also (which used to be employed as an alternative to parametric procedures), according to Leurgans et al. (1984), do not converge in probability to the true values of the parametric. Therefore, it was necessary to re-examine the issue, namely how a comparison between parametric and non-parametric techniques (with known values of the parameter) stands. The investigation is based on the same approach.     

Security Attack Testing (SAT)—testing the security of information systems at design time

For the last few years a considerable number of efforts have been devoted into integrating security issues into information systems development practices. This has led to a number of languages, methods, methodologies and techniques for considering security issues during the developmental stages of an information system. However, these approaches mainly focus on security requirements elicitation, analysis and design issues and neglect testing. This paper presents the Security Attack Testing (SAT) approach, a novel scenario-based approach that tests the security of an information system at the design time. The approach is illustrated with the aid of a real-life case study involving the development of a health and social care information system.     

Return Value Prediction meets Information Theory

Accurate return value prediction is a key tool for enabling effective speculative method-level parallelism, which will be a standard feature in the next generation of chip-multiprocessor architectures. In this paper we give some information theoretic measures that indicate intrinsic predictability of method return values. This is in stark contrast to the current ad-hoc heuristic measures imposed by specific prediction techniques. Our hope is that the application of information theoretic principles to the field of return value prediction should result in new kinds of predictors, and better deployment of existing prediction techniques. The two main contributions of this work are: (i) to show that there is some correlation between information theoretic measures and return value predictor performance; (ii) to highlight some major issues that need to be resolved before information theory can be adopted practically by the return value prediction community.     

Towards A Game Theoretic Understanding of Ad-Hoc Routing

In this paper we present a new application of game theory, in which game theoretic techniques are used to provide a rigorous underpinning to the analysis of ad-hoc routing protocols. The explosion of interest in ad-hoc networks over the last few years has resulted in a very large number of routing protocols being proposed. Despite this, the science of analysing routing protocols is still relatively immature, and the question that remains is how to decide “how good” a given protocol is. We propose a game theoretic approach as a potentially effective means of answering this question. The conceptual mapping of routing into a game is, we believe, natural and simple. Furthermore, game theory provides an extensive repertoire of tools to analyse key properties. The paper describes how routing techniques can be modelled as games and presents some analytical results.     

Characterization of a complementary sensitivity property in feedback control: An information theoretic approach

This paper addresses a characterization of a complementary sensitivity property in feedback control using an information theoretic approach. We derive an integral-type constraint of the complementary sensitivity function with respect to the unstable zeros of the open-loop transfer function. It is an analogue of Bode’s integral formula for the sensitivity gain. To show the constraint, we first show a conservation law of the entropy and mutual information of signals in the feedback system. Then, we clarify the relationship between the mutual information of control signals and the unstable zeros of the open-loop transfer function.     

Using component metadata to regression test component‐based software

Increasingly, modern‐day software systems are being built by combining externally‐developed software components with application‐specific code. For such systems, existing program‐analysis‐based software engineering techniques may not directly apply, due to lack of information about components. To address this problem, the use of component metadata has been proposed. Component metadata are metadata and metamethods provided with components, that retrieve or calculate information about those components. In particular, two component‐metadata‐based approaches for regression test selection are described: one using code‐based component metadata and the other using specification‐based component metadata. The results of empirical studies that illustrate the potential of these techniques to provide savings in re‐testing effort are provided. Copyright © 2006 John Wiley & Sons, Ltd.     

一种基于对等计算的构件模型研究

基于构件的软件开发,是软件工程领域一个重要的方法,其中构件模型是该方法的理论基础.目前,适用于基于对等结构的分布式应用软件开发的构件模型尚不多见.针对对等应用软件的特点,首先在给出一个开发框架的前提下,对对等应用软件进行了构件特征分析,并将基于该框架开发的对等应用软件系统所涉及到的构件形态,归结为可迁移构件、轻量级构件和耦合容器构件3类,然后在此基础上提出一个支持对等应用的构件模型,并对构件模型中语义和语法进行了表述,最后介绍了该构件模型的生成与部署、合成与组装、查找与匹配、通讯与协作、以及相关对比等方面的研究工作.     

Unsupervised detection of acoustic events using information bottleneck principle

An unsupervised approach based on Information Bottleneck (IB) principle is proposed for detecting acoustic events from audio streams. In this paper, the IB principle is first concisely presented, and then the practical issues related to the application of IB principle to acoustic event detection are described in detail, including definitions of various variables, criterion for determining the number of acoustic events, tradeoff between amount of information preserved and compression of the initial representation, and detection steps. Further, we compare the proposed approach with both unsupervised and supervised approaches on four different types of audio files. Experimental results show that the proposed approach obtains lower detection errors and higher running speed compared to two state-of-the-art unsupervised approaches, and is little inferior to the state-of-the-art supervised approach in terms of both detection errors and runtime. The advantage of the proposed unsupervised approach over the supervised approach is that it does not need to pre-train classifiers and pre-know any prior information about audio streams.