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This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

Nowadays amount of information on Internet is dramatically increasing. The ability of facilitating users to achieve useful information is more and more important for search field. CDSE, a model for the domain-based intelligent search engine is proposed in this paper. The model can help users to retrieve what they need by combining text classification with keywords extraction. Several algorithms that use key technologies are proposed, such as statistics, data mining and agents. Then a new criterion named ranking error is contributed to solve the problem of evaluation ranking inefficiency in traditional performance evaluation methodologies. The experimental results indicate that the proposed model can effectively improve retrieval precision and solve the problem of relevant document ranking behind in current search engine. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for nonnegative independent component analysis, which is based on minimizing the mutual information of the separated signals, and is truly insensitive to the particular underlying distribution of the source data. The unmixing system culminates to a novel neural network model. Compared with other algorithms for nonnegative ICA, the method proposed in this paper can work efficiently even in the case that the source signals are not well grounded, and that pre-whiting process is not needed. Finally, the experiments were performed on both simulating signals and mixtures of image data, the results indicate that the algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

We consider some problems about the computation time of evolutionary algorithms in this paper. First, some exact analytic expressions of the mean first hitting times of general evolutionary algorithms in finite search spaces are obtained theoretically by using the properties of Markov chain associated with evolutionary algorithms considered here. Then, by introducing drift analysis and applying Dynkin's Formula, the general upper and lower bounds of the mean first hitting times of evolutionary algorithms are estimated rigorously under some mild conditions listed in the paper. Those results in this paper are commonly useful. Also, the analytic techniques adopted in the paper are widely instructive for analyzing the computation time of evolutionary algorithms in a given search space as long as some specific mathematical arts are introduced accordingly. [ABSTRACT FROM AUTHOR]

This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profilesThis research was partially funded by the EC under the 6th FP IST IP VIKEF No. 507173, Priority 2.3.1.7 Semantic Based Knowledge Systems (www.vikef.net) and under the DELOS NoE on Digital Libraries, Priority: Technology-enhanced Learning and Access to Cultural Heritage - Contract n. G038-507618 (www.delos.info). The work was supported also by the research project "Modelli avanzati di personalizzazione per biblioteche elettroniche", year 2005, funded by the University of Bari.. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the "conference participant advisor" service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3). [ABSTRACT FROM AUTHOR]

The use of an ontology in order to provide a mechanism to enable machine reasoning has continuously increased during the last few years. This paper proposed an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontology-based document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web documents are classified in real time not with experimental data or a learning process, but by similarity calculations between the terminology information extracted from Web documents and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. [ABSTRACT FROM AUTHOR]

This chapter contains papers presented in the workshop on Text Mining and Applications (TeMA 2005), organized in the framework of the Portuguese Association for Artificial Intelligence conference (EPIA). This workshop is aimed at attracting quality papers and enhancing the knowledge in this area. 27 papers were submitted. From these, 9 papers were selected for publication in this Springer volume. These numbers show current importance of this field in AI and suggest that the organization of equivalent events in future EPIA editions should be pursued. First paper works on bilingual lexical acquisition from non parallel corpora, applicable in Machine Translation. Second paper describes work on Text Summarization. Third uses Transformation Based learning for NP Identification applied to Portuguese.Fourth uses linguistic knowledge for passage retrieval and question answering. Fifth describes the use of weakly supervised learning for extraction of semantic patterns. Sixth paper proposes a method for semantic indexing and evaluates it on traditional Information Retrieval tasks. Seventh works on unsupervised language independent extraction of multi-word terms, applicable in multiple domains and evaluates their results for Slovene and English. Eighth paper presents a variant of a known method for Anaphora resolution, adapted to Portuguese. Last paper proposes a stemmer for Brazilian Portuguese. [ABSTRACT FROM AUTHOR]

A Personal Assistant (PA) agent is a software agent capable of helping people to handle tasks in their workplace. The paper proposes a memory mechanism for personal assistant agents in order to enhance agent intelligence while working with the user or with other agents. Inspired by a case memory model in the domain of Case-Based Reasoning (CBR), this paper endows PA agents with a case memory mechanism, which results in improved PA agents: MemoPAs. We present the memory mechanism of MemoPA in detail, and report a first implementation of the method. Finally, future work is outlined for improving the memory mechanism. [ABSTRACT FROM AUTHOR]

A top-down experimental procedure for defect type recognition of epoxy-resin transformers by Partial Discharge (PD) is proposed. Most of the PD detection methods could be performed only at the shutdown period of equipments. By using Acoustic Emission (AE), the real-time and online detection could be reachable. Therefore, this paper conducted high voltage test of pre-faulty transformers and measured those PD signals for recognition needed. Afterward, the selected features that proposed in this paper can be extracted from these collected PD signals. According to these features, effective identification of their faulty types can be done using the proposed particle swarm optimization combined with neural network. Finally, with a view to apply in field, this research adds different noise levels to distort the original data. These distorted data are entered for subsequent testing. Research shows encouraging results that with 30% noise per discharge count, an 80% successful recognition rate can be achieved. [ABSTRACT FROM AUTHOR]

Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Because there is little research in solving the combinatorial optimization problem (COP) by EM, the paper attempts to employ the random-key concept enabling EM to solve COP in single machine scheduling problem. We present a hybrid algorithm that combines the EM methodology and genetic operators to obtain the best/optimal schedule for this single machine scheduling problem, which attempts to achieve convergence and diversity effect when they iteratively solve the problem. The objective in our problem is minimization of the sum of earliness and tardiness. This hybrid algorithm was tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm. [ABSTRACT FROM AUTHOR]

Upon close examination of a set of industrial data from a large scale power plant, time varying behavior are discovered. If a fixed model is applied to monitor this process, false alarms will be inevitable. This paper suggests the use of adaptive models to cope with such situation. A recently proposed technique, fast algorithm for Moving Window Principal Component Analysis (MWPCA) was employed because of its following strength: (i) the ability in adapting process changes, (ii) the conceptual simplicity, and (iii) its computational efficiency. Its advances in fault detection is demonstrated in the paper by comparing with the conventional PCA. In addition, this paper proposed to plot the scaled variables in conjunction with MWPCA for fault diagnosis, which is proved to be effective in this application. [ABSTRACT FROM AUTHOR]

PVM (Parallel virtual machine) library is a tool which used processes large amounts of data sets. This paper wants to achieve a high performance solution that exploits PVM library and parallel computers to solve ICA (Independent Component Analysis) problem. The paper presents parallel power ICA implementations to decomposition data sets. Power iteration (PI) is an algorithm for independent component analysis, which has some desired features. It has higher performance and data capacity than current sequential implementations. This paper, we show the power iteration algorithm which learning updating is in the form of matrix transformation . From power iteration algorithm, we develop parallel power iteration algorithm and implement parallel component decomposition solution. At last, experimental results, analysis and future plans are presented. [ABSTRACT FROM AUTHOR]

In this paper, global exponential stability of Cohen-Grossberg neural networks with reaction-diffusion and Dirichlet boundary conditions is considered by using an approach based on the delay differential inequality and the fixed-point theorem. Some sufficient conditions are obtained to guarantee that the reaction-diffusion Cohen-Grossberg neural networks are globally exponentially stable. The results presented in this paper are the improvement and extension of the existed ones in some existing works. [ABSTRACT FROM AUTHOR]

This paper analyzes global convergence and learning parameters of the back-propagation algorithm for quadratic functions. Some global convergence conditions of the steepest descent algorithm are obtained by directly analyzing the exact momentum equations for quadratic cost functions. In addition, in order to guarantee the convergence for a given learning task, the method is obtained to choose the proper learning parameters. The results presented in this paper are the improvement and extension of the existed ones in some existing works. [ABSTRACT FROM AUTHOR]

This article deals with the identification of hinging hyperplane models. This type of non-linear black-box models is relatively new, and its identification is not thoroughly examined and discussed so far. They can be an alternative to artificial neural nets but there is a clear need for an effective identification method. This paper presents a new identification technique for that purpose based on a fuzzy clustering technique called Fuzzy c-Regression Clustering. To use this clustering procedure for the identification of hinging hyperplanes there is a need to handle restrictions about the relative location of the hyperplanes: they should intersect each other in the operating regime covered by the data points. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning of the operating region of one local linear model resuling in a binary regression tree. Hence, this paper proposes a new algorithm for the identification of tree structured piecewise linear models, where the branches correspond to linear division of the operating regime based on the intersection of two local linear models. The effectiveness of the proposed model is demonstrated by a dynamic model identification example. [ABSTRACT FROM AUTHOR]

This paper is the first in a series which address the development of a temporal planner using timed Petri net (TPN) models of concurrent temporal plans. Unlike previous work which uses Petri net unfoldings to develop partially ordered plans (in the untimed case), the approach presented here is purely algebraic, and is based on a (min,max,+) discrete event dynamical system description of a TPN. This paper focuses primarily on policy evaluation, with subsequent work detailing how to use this approach for efficient optimal temporal planning. [ABSTRACT FROM AUTHOR]

In this paper, we utilize a combination of SWEBOK and text categorization to categorize software engineering knowledge. SWEBOK serves as a backbone taxonomy while text categorization provides a collection of algorithms including knowledge representation, feature enrichment and machine learning. Firstly, fundamental knowledge types in software engineering are carefully analyzed as well as their characteristics. Then, incorporated with SWEBOK, we propose a knowledge categorization methodology as well as its implementing algorithms. Finally, we conduct experiments to evaluate the proposed method. The experimental results demonstrate that our methodology can serve as an effective solution for the categorization of software engineering knowledge. [ABSTRACT FROM AUTHOR]

Transposition tables have long been a viable tool in the pruning mechanisms of game-tree search algorithms. In such applications, a transposition table can reduce a game-tree to a game-graph with unique board positions at the nodes. This paper proposes a transposition table extension, called a template matching table, where templates that prove winning positions are used to map features of board positions to board values. This paper demonstrates that a game-tree search for the game of Hex can have a more effective pruning mechanism using a template matching table than it does using a transposition table. [ABSTRACT FROM AUTHOR]

A dual-population genetic algorithm (DPGA) is a new multi-population genetic algorithm that solves problems using two populations with different evolutionary objectives. The main population is similar to that of an ordinary genetic algorithm, and it evolves in order to obtain suitable solutions. The reserve population evolves to maintain and offer diversity to the main population. The two populations exchange genetic materials using interpopulation crossbreeding. This paper proposes a new fitness function of the reserve population based on the distance to the main populations. The experimental results have shown that the performance of DPGA is highly related to the distance between the populations and that the best distance differs for each problem. Generally, it is difficult to decide the best distance between the populations without prior knowledge about the problem. Therefore, this paper also proposes a method to dynamically adjust the distance between the populations using the distance between good parents, i.e., the parents that generated good offspring. [ABSTRACT FROM AUTHOR]

There are numerous families of Neural Networks (NN) used in the study and development of the field of Artificial Intelligence (AI). One of the more recent NNs involves the Bursting neuron, pioneered by Rulkov. The latter has the desirable property that it can also be used to model a system (for example, the "brain") which switches between modes in which the activity is excessive ("bursty"), to the alternate case when the system is "dormant". This paper, which we believe is a of pioneering sort, derives some of the analytic properties of the Bursting neuron, and the associated NN. To be more specific, the model proposed by Rulkov [11] explains the so-called "bursting" phenomenon in the system (brain), in which a low frequency pulse output serves as an envelope of high frequency spikes. Although various models for bursting have been proposed, Rulkov's model seems to be the one that is both analytically tractable and experimentally meaningful. In this paper, we show that a "small" scale network consisting of Bursting neurons rapidly converges to a synchronized behavior implying that increasing the number of neurons does not contribute significantly to the synchronization of the individual Bursting neurons. The consequences of such a conclusion lead to a phenomenon that we call "behavioral synchronization". [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) has come under the spotlight as technology supporting ubiquitous society. But now, we face several security problems and challenges in RFID systems. Recent papers have reported that RFID systems have to achieve the following requirements: (1) Indistinguishability, (2) Forward Security, (3) Replay Attack resistance, (4) Tag Killing resistance, and (5) Ownership Transferability. We have to design RFID system that achieves the above-mentioned requirements. The previous security methods achieve only some of them individually, and no RFID system has been constructed that achieves all requirements. In this paper, we propose an RFID security method that achieves all requirements based on a hash function and a symmetric key cryptosystem. In addition, our proposed method provides not only high-security but also high-efficiency. [ABSTRACT FROM AUTHOR]

Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. However, there are only limited papers studying on the mechanism of EO applied to the numerical optimization problems so far. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Lévy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good choice to deal with the numerical constrained optimization problems. [ABSTRACT FROM AUTHOR]

This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general square matrices are given. Then, some useful properties of homogeneous Markov chains with finite states are investigated. Finally, the geometric convergence rates of the transition operators, which is determined by the revised spectral of the corresponding transition matrix of a Markov chain associated with the EA considered here, are estimated by combining the acquired results in this paper. [ABSTRACT FROM AUTHOR]

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods. [ABSTRACT FROM AUTHOR]

This paper reports a snapshot of our on-going experiments in which a common target we call a-tiny-island-in-a-huge-lake is explored with different methods ranging from a data-mining technique to an artificial immune system. Our implicit interest is a network intrusion detection, and we assume data floating in the huge lake are normal while ones found on the tiny island are abnormal. Our goal here is twofold. One is to know (i) whether or not it is possible to train a system using just normal data alone. The other is to study (ii) a limit of the size of the detectable area, when we decrease the size of the island eventually shrinking to zero, equivalently so-called a-needle-in-a-haystack which is still an open and worth while tuckling problem. To learn these two issues, a fuzzy rule extraction system - one with fixed triangle/trapezoid membership functions for our first goal, and for the second goal with Gaussian membership functions whose shape is adaptively determined for the second goal, are exploited in this paper. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster. [ABSTRACT FROM AUTHOR]

Gene libraries are a biological mechanism for generating combinatorial diversity in the immune system. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding lifetime learning mechanisms. In this paper we examine the implications of this view, by examining coverage, avoidance of self, clustering and diversity. We show how gene libraries may improve both computational expense and performance, and present an analysis which suggests how they might do it. We suggest that gene libraries: provide combinatorial efficiency; improve coverage; reduce the cost of negative selection; and allow targeting of fixed antigen populations. Keywords: gene libraries, artificial immune systems, antibodies, diversity, Baldwin effect, lifetime learning. [ABSTRACT FROM AUTHOR]

This paper deals with the application of segment procedure neural networks to predict harm status of horsetail-pine worm. A novel procedure neural networks is proposed to solve those problems which are related to certain distinct segments of procedure. It is indicated that this model is a generalized form of the known procedure neural networks, and it owns all properties of the known model. This paper also presents learning algorithms for the segment procedure neural networks. Horsetail-pine worm forecast is a hard work for forest experts, but it is a typical segment procedure problem. In this paper a segment procedure neural networks is applied to deal with this issue, and some simulation experiment results are presented. Keywords: Neural networks, topological structure, procedure neural networks, segment, algorithm, learning, prediction. [ABSTRACT FROM AUTHOR]

The classical rough set theory based on complete information systems stems from the observation that objects with the same characteristics are indiscernible according to available information. With respect to upper-approximation and lower-approximation defined on an indiscernibility relation it classifies objects into different equivalent classes. But in some cases such a rigid indiscernibility relation is far from applications in the real world. Therefore, several generalizations of the rough set theory have been proposed some of which extend the indiscernibility relation using more general similarity or tolerance relations. For example, Kryszkiewicz [4] studied a tolerance relation, and Stefanowski [7] explored a non-symmetric, similarity relation and valued tolerance relation. Unfortunately, All the extensions mentioned above have their inherent limitations. In this paper, after discussing several extension models based on rough sets for incomplete information, a concept of constrained dissymmetrical similarity relation is introduced as a new extension of the rough set theory, the upper-approximation and the lower-approximation defined on constrained similarity relation are proposed as well. Furthermore, we present the comparison between the performance of these extended relations. Analysis of results shows that this relation works effectively in incomplete information and generates rational object classification. Keywords: Rough sets, incomplete information, constrained dissymmetrical similarity relation. [ABSTRACT FROM AUTHOR]

In this paper, a system based on Artificial Neural Network (ANN) for classifying automobile parts with different shapes is presented. The system gets the original information from an image sensor, classifies two sorts of automobile parts with different shapes after processing these images. The classifier designed in this paper adopts the ANN with an improved BP algorithm. The perimeter, acreage and the degree of the decentralization of the automobile parts in the intensity image are taken as the input feature vectors. For the No.1 part and No.2 part, the experimental result indicates that the recognition accuracy rate can reach up to 99% in this system and the productivity will be improved obviously comparing with the checking result in the off-line system manually. [ABSTRACT FROM AUTHOR]

A novel Fuzzy PID controller is proposed in this paper, which is used for treating the control of pulp level and brightness in flotation de-inking column. The essential part of fuzzy PID controller is the fuzzy logic control, which is a multi-input-multi-output controller. A design method of the fuzzy PID controller is presented. The simulation results show that the proposed Fuzzy PID controller has preferable performance and significant advantages over the traditional PID controller. [ABSTRACT FROM AUTHOR]

The speed and convenience of the Internet facilitated the development of electronic commerce (e-commerce). E-commerce research and technologies have always drawn the attention of researchers. Among them, the application of mobile agent on e-commerce has drawn much attention in recent years. Mobile agents can roam freely over different execution environments to execute tasks assigned to them. However, a mobile agent may be attacked when it requests services from other servers or when comes in contact with and exchange information with another agents while roaming on the internet. Hence, a mobile agent user may be concerned that his mobile agent could be corrupted or private information tapped and pirated by other agents. To ensure the security of mobile agents in public network environment, this paper proposes a security scheme that is suitable for mobile agents. The scheme includes access control and key management; it is also an improvement on the key management and access control for mobile agent scheme of Volker and Mehrdad. The proposed scheme corrects the drawback in Volker and Mehrdad's scheme which is the need of a large amount of storage for storing the secret keys. Security and performance analysis of our scheme proves the proposed scheme to be more efficient and secure. [ABSTRACT FROM AUTHOR]

Space applications' users have been relying for the past decades on custom developed software tools capable of addressing short term necessities during critical Spacecraft control periods. Advances in computing power and storage solutions have made possible the development of innovative decision support systems. These systems are capable of providing high quality integrated data to both near real time and historical data analysis applications. This paper describes the implementation of a new approach for a distributed and loosely coupled data extraction and transformation solution capable of extracting, transforming and perform loading of relevant real-time and historical Space Weather and Spacecraft data from semi-structured text files into an integrated space-domain decision support system. The described solution takes advantage of XML and Web Service technologies and is currently working under operational environment at the European Space Agency as part of the Space Environment Information System for Mission Control Purposes (SEIS) project. [ABSTRACT FROM AUTHOR]

Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals. [ABSTRACT FROM AUTHOR]

This paper presents a Genetic algorithm (GA) based solution to co-optimize test scheduling and wrapper design for core based SOCs. Core testing solutions are generated as a set of wrapper configurations, represented as rectangles with width equal to the number of TAM (Test Access Mechanism) channels and height equal to the corresponding testing time. A locally optimal best-fit heuristic based bin packing algorithm has been used to determine placement of rectangles minimizing the overall test times, whereas, GA has been utilized to generate the sequence of rectangles to be considered for placement. Experimental result on ITC'02 benchmark SOCs shows that the proposed method provides better solutions compared to the recent works reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a novel fault detection and section estimation method for unbalanced underground distribution systems (UDS). The method proposed is based on artificial neural networks (ANNs) and wavelet transforms (WTs). The majority of UDS are characterized by having several single/double phase laterals and non-symmetrical lines. Also, Digital Fourier Transforms (DFT), used in the majority of traditional protection relays, supplies a low level of robustness to the fault diagnosis process due to its inversely proportional time-frequency characteristic. These characteristics compromise the traditional fault diagnosis methods performance. ANNs are capable of learning and generalizing, whereas WTs are robust tools capable of evaluating a signal's frequency range that can characterize the fault phenomenon. This paper describes the proposed diagnosis method and discusses the results obtained from simulated implementation. The obtained results demonstrate the capability and robustness of the technique indicating its potential for on-line applications. [ABSTRACT FROM AUTHOR]

This paper proposed a novel residual capacity estimation method for lead-acid battery. Generally, the battery residual capacity is related to open-circuit-voltage (OCV) and inner resistance. Due to complexities, delay, coupling and nonlinearity on the relation described above, the residual capacity estimation is difficult to be obtained by the voltage or inner resistance measurement only. In this paper, by observing and recording the constant current discharging process, we generated the residual capacity pattern for different capacity levels and built a CMAC (Cerebellar Model Articulation Controller) neural network to estimate the lead-acid battery residual capacity. The characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN estimation scheme enables powerful, straightforward, and efficient battery residual capacity estimation. With application of this scheme to the experimental data test, the estimation results demonstrate the new scheme with high accuracy and high noise rejection ability. [ABSTRACT FROM AUTHOR]

Bearing fault detection with the aid of the vibration signals is presented. In this paper, time-domain features are extracted to indicate bearing fault, which collected from tri-axial vibration signal. Decision tree is chosen as an effective diagnostic tool to obtain bearing status. The paper also introduces principal component analysis (PCA) algorithm to reduce training data dimension and remove irrelevant data. Both original data and PCA-based data are used to train C4.5 decision tree models. Then, the result of PCA-based decision tree is compared with normal decision tree to get the best performance of classification process. [ABSTRACT FROM AUTHOR]

Kernel methods like support vector machine, kernel principal component analysis and kernel fisher discriminant analysis have recently been successfully applied to solve pattern recognition problems such as face recognition. However, most of the papers present the results without giving kernel parameters, or giving parameters without any explains. In this paper, we present an experiments based approach to optimize the performance of a Gabor feature and kernel method based face recognition system. During the process of parameter tuning, the robustness of the system against variations of kernel function, kernel parameters and Gabor features are extensively tested. The results suggest that the kernel method based approach, with tuned parameters, achieves significantly better results than other algorithms available in literature. [ABSTRACT FROM AUTHOR]

Gyro survey technique has applied and played an important role in many areas, such as offshore oil drilling, directional drilling and so on. Considering the influence of the compensation for the large surveying azimuth error, in this paper, the principle of the gyro survey system is described, and the ANFIS architecture is employed to model the survey azimuth error, predict a chaotic current, all yielding remarkable results according to the gyro survey principle and the data sampled from the two-axis turntable. From the simulation and the testing result, we can see that ANFIS is an effective and feasible way to model and compensate the azimuth error, and the precision of the ANFIS method is higher than the methods of the bilinear interpolation and the radius basis function (RBF), so it is available and advisable in engineering. [ABSTRACT FROM AUTHOR]

Understanding and adapting to changes of customer behavior is an important aspect of surviving in a continuously changing market environment for a modern company. The concept of customer change model mining is introduced and its process is analyzed in this paper. A customer change model mining method based on swarm intelligence is presented, and the strategies of pheromone updating and items searching are given. Finally, an examination on two customer datasets of a telecom company illuminates that this method can achieve customer change model efficiently. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for data analysis based on the "key points" in data distribution is proposed. Here, the key points contain three types of data points: bridge points, border points, and skeleton points, where our main contribution is the bridge points. For each type of key points, we have developed the corresponding detection algorithm and tested its effectiveness with several synthetic data sets. Meanwhile, we further developed a new hierarchical clustering algorithm SPHC (Skeleton Point based Hierarchical Clustering) to demonstrate the possible applications of the key points acquired. Based on some real-world data sets, we experimentally show that SPHC performs better compared with several classical clustering algorithms including Complete-Link Hierarchical Clustering, Single-Link Hierarchical Clustering, KMeans, Ncut, and DBSCAN. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel approach to perform kernel parameter selection for Kernel Fisher discriminant analysis (KFDA) based on the viewpoint that optimal kernel parameter is associated with the maximum linear separability of samples in the feature space. This makes our approach for selecting kernel parameter of KFDA completely comply with the essence of KFDA. Indeed, this paper is the first paper to determine the kernel parameter of KFDA using a search algorithm. Our approach proposed in this paper firstly constructs an objective function whose minimum is exactly equivalent to the maximum of linear separability. Then the approach exploits a minimum search algorithm to determine the optimal kernel parameter of KFDA. The convergence properties of the search algorithm allow our approach to work well. The algorithm is also simple and not computationally complex. Experimental results illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The problem of mean square exponential stability of uncertain stochastic Hopfield neural networks with interval time-varying delays is investigated in this paper. The delay factor is assumed to be time-varying and belongs to a given interval, which means that the derivative of the delay function can exceed one. The uncertainties considered in this paper are norm-bounded and possibly time-varying. By Lyapunov-Krasovskii functional approach and stochastic analysis approach, a new delay-dependent stability criteria for the exponential stability of stochastic Hopfield neural networks is derived in terms of linear matrix inequalities(LMIs). A simulation example is given to demonstrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

In analyzing gene expression data from micro-array, a major challenge is the definition of a feature selection criterion to judge the goodness of a subset of features with respect to a particular classification model. This paper presents a cost-sensitive approach feature selection that focuses on two fundamental requirements: (1) the quality of the features in order to promote the classifier accuracy and (2) the cost of computation due to the complexity that occurs during training and testing the classifier. The paper describes the approach in detail and includes a case study for a publicly available micro-array dataset. Results show that the proposed process yields state-of-art performance and uses only a small fraction of features that are generally used in competitive approaches on the same dataset. [ABSTRACT FROM AUTHOR]

This paper presents a computability theorem for fixed points of multi-valued functions defined on multilattices, which is later used in order to obtain conditions which ensure that the immediate consequence operator computes minimal models of multilattice-based logic programs in at most ω iterations. [ABSTRACT FROM AUTHOR]

The paper aimed to establish a framework for handling the relationships between different agents in mobile environments. In such environments, objects are vague due to incompleteness and local nature of sensed data. The lack of relevant data stimulated us to use intuitionistic fuzzy (IF) logic for modeling spatial relationships between them. In this paper uncertainty modeling of spatial relationships are analyzed from the view point of intuitionistic fuzzy (IF) logic and in order to provide a paradigm that treats with uncertain topological relationships in mobile GIS environments, a logical framework is presented in which the concept of spatial influenceability is combined with the IF logic. [ABSTRACT FROM AUTHOR]

This paper is a continuation of our previous papers on entropy of the Atanassov intuitionistic fuzzy sets (A-IFSs, for short). We discuss the necessity of taking into account all three functions (membership, non-membership and hesitation margin) describing A-IFSs while considering the entropy. [ABSTRACT FROM AUTHOR]

The paper shows methods that could be suitably applied to manage uncertainties in space systems design and modeling. Attention is focused on the Interval Analysis, as one of the most promising methods. In this research two issues typical faced in space-related researchs have been treated: optimization and ordinary differential equations (ODEs) solving taking into account possible uncertainties included in the models; the first is generally related to space system design phases while the second deals with systems dynamics analysis. Because of lack of space this paper presents results from the first area only: more specifically, the optimization of a control system design to accomplish robust reconfiguration manoeuvre is achieved taking into account uncertainties on the technological parameters of the propulsion unit to obtain a robust design from the beginning of the study. Results turned out that the Interval Analysis is effective and efficient to handle these classes of uncertainties. [ABSTRACT FROM AUTHOR]

Different clustering algorithms are based on different similarity or distance measures (e.g. Euclidian distance, Minkowsky distance, Jackard coefficient, etc.). Jarvis-Patrick clustering method utilizes the number of the common neighbors of the k-nearest neighbors of objects to disclose the clusters. The main drawback of this algorithm is that its parameters determine a too crisp cutting criterion, hence it is difficult to determine a good parameter set. In this paper we give an extension of the similarity measure of the Jarvis-Patrick algorithm. This extension is carried out in the following two ways: (i) fuzzyfication of one of the parameters, and (ii) spreading of the scope of the other parameter. The suggested fuzzy similarity measure can be applied in various forms, in different clustering and visualization techniques (e.g. hierarchical clustering, MDS, VAT). In this paper we give some application examples to illustrate the efficiency of the use of the proposed fuzzy similarity measure in clustering. These examples show that the proposed fuzzy similarity measure based clustering techniques are able to detect clusters with different sizes, shapes and densities. It is also shown that the outliers are also detectable by the proposed measure. [ABSTRACT FROM AUTHOR]