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Traditional indexing methods face the difficulty of"curse of dimensionality" at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval. [ABSTRACT FROM AUTHOR]

In this paper, we originally propose a music recommendation agent in a smart office to recommend music for users. Personal tastes are diverse, even the same person may have different preferences in different situations. Hence, our music recommendation is not only based on the user favorite genres but also the current mood of users. By collecting and analyzing the contextual information of users, the agent can automatically senses the mood of users and recommends music. [ABSTRACT FROM AUTHOR]

In this paper, we propose face feature selection and classifier selection method for face image group according illuminant. In knowledge based, we stored context and weight for feature points and selected classifier for context. This context is distinguished the face images having varying illumination. This context knowledge can be accumulated and used later. Therefore we designed the face recognition system by using evolution method and efficient face feature point selection. It can improve its performance incrementally using proposed algorithm. And we proposed efficient context modeling method by using SOM. For context awareness, we made artificial face images from FERET fa dataset and divided several group. Therefore we improved face recognition ratio using adaptable classifier, feature and weight for feature points. [ABSTRACT FROM AUTHOR]

A novel contourlet transform based fusion algorithm for nighttime driving image is proposed in this paper. Because of advantages of the contourlet transform in dealing with the two or higher dimensions singularity or the image salient features, such as line, curve, edge and etc., each of the accurately registered images is decomposed into a low frequency subband image and a sets of high frequency subband images with various multiscale, multidirectional local salient features. By using different fusion rules for the low frequency subband image and high frequency subband images, respectively, the fused coefficients are obtained. Then, the fused image is generated by the inverse contourlet transform. The simulation results indicate that the proposed method outperforms the traditional wavelet packet transform based image fusion method. [ABSTRACT FROM AUTHOR]

One of the most crucial and complex steps of object-oriented system design lies in the transition needed to go from system behavior (defined by means of scenario models) to component behavior (described by means of communicating hierarchical state machine models).This paper presents a re-verse approach for generating hierarchical UML statechart diagrams. Firstly, we put forward a generation algorithm for a flat statechart diagram based on the BK-algorithm, which is validated useful in our reverse engineering tool XDRE by generating UML statechart diagrams from a set of UML sequence diagrams. Secondly, according to UML composite state, an automatic approach of introducing hierarchy to the generated flat statechart diagrams is proposed and implemented. Finally, systematic experiment is conducted in the paper in order to verify the validity of this approach. [ABSTRACT FROM AUTHOR]

Rough set theory has been proposed by Pawlak as a tool for dealing with the vagueness and granularity in information systems. The core concepts of classical rough sets are lower and upper approximations based on equivalence relations. This paper studies arbitrary binary relation based generalized rough sets. In this setting, a binary relation can generate a lower approximation operation and an upper approximation operation. We prove that such a binary relation is unique, since two different binary relations will generate two different lower approximation operations and two different upper approximation operations. This paper also explores the relationships between the lower or upper approximation operation generated by the intersection of two binary relations and those generated by these two binary relations, respectively. Keyword: Rough set, Lower approximation, Upper approximation, Binary relation, Fuzzy set, Granular computing. [ABSTRACT FROM AUTHOR]

We propose a method of multiple context fusion based robust face detection scheme, multiple cascade and finally decision using correlation table. It takes advantage of multiple cascade face detector fusion by context. We propose the filtering classifier method for illumination face image. And then we constructed cascade classifier from applied different filtering method. The multiple cascade detectors made from six single context detectors. Six contexts are divided k-means algorithm, and classify illuminant. In this paper, we proposed the classifier fusion method by using correlation between face images. The proposed face detection achieves the capacity of the high level attentive process by taking advantage of the context-awareness using the information from illumination. We achieved very encouraging experimental results having varying illuminant. [ABSTRACT FROM AUTHOR]

Retrieving pertinent parts of a meeting or a conversation recording can help for automatic summarization or indexing of the document. In this paper, we deal with an original task, almost never presented in the literature, which consists in automatically extracting questions utterances from a recording. In a first step, we have tried to develop and evaluate a question extraction system which uses only acoustic parameters and does not need any textual information from a speech-to-text automatic recognition system (called ASR system for Automatic Speech Recognition in the speech processing domain) output. The parameters used are extracted from the intonation curve of the speech utterance and the classifier is a decision tree. Our first experiments on French meeting recordings lead to approximately 75% classification rate. An experiment in order to find the best set of acoustic parameters for this task is also presented in this paper. Finally, data analysis and experiments on another French dialog database show the need of using other cues like the lexical information from an ASR output, in order to improve question detection performance on spontaneous speech. [ABSTRACT FROM AUTHOR]

The objective of this paper is to describe the diagnosis, detection, and prediction of high risk patient status or level of risk, based on the hospital domain server with agent based intelligent home medical service system. We have proposed a Computer Aided Diagnosis and monitoring system framework for the high risk patient exploiting the ubiquitous artifacts available in the con-temporary medical environment. The proposed framework monitors patients using bio signal devices while they are staying at their homes. Furthermore, the agent based learning process is used to make intelligent inferences and adaptive learning of these private data using Computer Aided Diagnosis system. It can monitor acute COPD using integrated home medical server. The services include prevention, emergency call center and ambulance service in a common platform of ubiquitous environment. [ABSTRACT FROM AUTHOR]

The paper proposes and exemplifies how to use the fuzzy logic in the critical analysis of the faults including abnormal workings, for the most important elements in Power Systems. An original fuzzy logic-system enables us to analyze the qualitative evaluation of an Electric Transformer Protection System. A fuzzy event-tree allows the use of verbal statement for probabilities and consequences, such as very high, moderate and low probability. The technique is used for quantitative results computing, as "General Safety Degree" associated to all the paths in the tree. The paper focuses on the "General Safety Degree" algorithm. The application of fuzzy logic system is further demonstrated for a case study. A complex software tool named "Fuzzy Event Tree Analysis" had to be elaborated on this purpose. [ABSTRACT FROM AUTHOR]

In this paper, we propose uncertainty analysis using geometrical property between 2D-to-3D under affine reconstruction. In situations when are no missing data in an observation matrix, the accurate solution is known to be provided by Singular Value Decomposition (SVD). However, when converting image sequences to 3D, several entries of the matrix have not been observed and other entries have been perturbed by the influence of noise. In this case, there is no simple solution. In this paper, a new approach is applied for recovering missing data using geometrical properties between a 2D image plane and 3D shape and for estimating noise level in an observation matrix using ranks of SVD. This paper consists of four main phases: geometrical properties between 2D image plane and 3D error space, geometrical recovering of missing data, and noise level estimation in the observation matrix. [ABSTRACT FROM AUTHOR]

This paper proposes a new modeling approach for building TSK models for short-term load forecasting (STLF). The approach is a two-stage model building technique, where both premise and consequent identification are simultaneously performed. The fuzzy C-regression method (FCRM) is employed at stage-1 to identify the structure of the model. The resulting model is reduced in complexity by selection of the proper model inputs which are achieved using a Particle Swarm Optimization algorithm (PSO) based selection mechanism at stage-2. To obtain simple and efficient models we employ two descriptions for the load curves (LC's), namely, the feature description for the premise part and the cubic B-spline curve for the consequent part of the rules. The proposed model is tested using practical data, while load forecasts with satisfying accuracy are reported. [ABSTRACT FROM AUTHOR]

In this paper, we present the definition of λ-fuzzy signed measure, its generalized transform function, and the distribution properties of λ-fuzzy measures. Then investors' heterogeneity is revealed with a family of λ-fuzzy measures. The approach to asset pricing based on λ-fuzzy measures breaks through the traditional "identical rationality" assumption, and provides an useful analytical tool for recent researches of behavioral economics and finance. Keywords: fuzzy; fuzzy measures; heterogeneity; asset pricing; fuzzy options. [ABSTRACT FROM AUTHOR]

The issue of maintaining privacy in data mining has attracted considerable attention over the last few years. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. This paper addresses privacy preserving mining of association rules on distributed dataset. We present an algorithm, based on a probabilistic approach of distorting transactions in the dataset, which can provide high privacy of individual information and at the same time acquire a high level of accuracy in the mining result. Finally, we present experiment results that validate the algorithm. [ABSTRACT FROM AUTHOR]

It is difficult to understand a scene from visual information in uncertain real world. Since Bayesian network (BN) is known as good in this uncertainty, it has received significant attention in the area of vision-based scene understanding. However, BN-based modeling methods still have the difficulties in modeling complex relationships and combining several modules, as well as the high computational complexity of inference. To overcome them, this paper proposes a method to divide and select the BN modules for recognizing the objects in uncertain environments. The method utilizes the behavior selection network to select the most appropriate BN modules. Several experiments are performed to verify the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

An approach based on wavelet analysis and non-linear mapping is proposed in this paper. Using the non-linear mapping to decrease the dimensions of data, taking full advantage of wavelet analysis' superiority in local analysis, the approach is able to detect anomalies accurately. The experiments show that the approach is accurate and practical. [ABSTRACT FROM AUTHOR]

Interactive mining, which is the problem of mining frequent itemsets in a database under different thresholds, is becoming one of the interesting topics in frequent itemsets mining because of needs of practical application. In this paper, we propose a heuristic method for greatly decreasing the number of possible candidates in Poteriori, which is an algorithm based on Apriori for interactive mining. Fewer possible candidates make Poteriori more efficient. Analysis based on an example shows the advantage of our method. [ABSTRACT FROM AUTHOR]

Computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. Computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets. [ABSTRACT FROM AUTHOR]

This paper describes a novel adaptive learning approach for text categorization based on a Back-propagation neural network (BPNN). The BPNN has been widely used in classification and pattern recognition; however it has some generally acknowledged defects, which usually originate from some morbidity neurons. In this paper, we introduce an improved BPNN that can overcome these defects and rectify the morbidity neurons. We tested the improved model on the standard Reuter-21578, and the result shows that the proposed model is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

Both rough set theory and mathematical morphology originated from the classical set theory, and are characterized by using dual operators sharing similar properties. In this paper, a notion of morphological covering rough set is introduced by means of combining rough set with fundamental morphological operators. The focus of this paper is on constructing a covering and a pair of morphological rough approximation operators. The issue on reduction of a morphological covering is explored and the procedure for generating the reduction is presented. Necessary and sufficient conditions on characterization of the reduction are proved. Keywords: Rough set, morphological operators, morphological covering, adjunction, reduction. [ABSTRACT FROM AUTHOR]

This paper presents a new method of estimating fuzzy multivariable nonlinear regression model for fuzzy input and fuzzy output data. This estimation method is obtained by constructing a fuzzy linear regression based on least squares support vector machine(LS-SVM) in a high dimensional feature space for the data set with fuzzy inputs and fuzzy output. Experimental results are then presented which indicate the performance of this algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on the so called controller synthesis problem, which addresses the question of how to limit the internal behavior of a given system implementation to meet its specification, regardless of the behavior enforced by the environment. We consider this problem in the probabilistic setting, where the underlying model has both probabilism and nondeterminism and the nondeterministic choices in some states are assumed to be controllable while the others are under the control of an unpredictable environment. As for the specification, it is defined by probabilistic computation tree logic. We show that under the restriction that the controller exploits only Markovian randomized strategy, the existence of such a controller is decidable, which is done by a reduction to the decidability of first-order theory for reals. This also gives rise to an algorithm which can synthesize the controller if it does exist. [ABSTRACT FROM AUTHOR]

This paper considers the robust stability of uncertain T-S fuzzy system with time-varying delay. A new delay-dependent stability condition for the system is derived in terms of LMIs. The constraint on the time-varying delay function is removed, which means that a fast time-varying delay is allowed. Numerical example is given to illustrate the effectiveness and less conservativeness of the developed techniques. [ABSTRACT FROM AUTHOR]

For better solving some complicated problems in fuzzy automata hierarchy, simultaneously, in order to accomplish better task for fuzzy signal processing, this paper presents a kind of new automaton-fuzzy infinite-state automaton. The basic extracted frame of fuzzy infinite-state automaton is introduced by using neural networks. To the extracted fuzzy infinite-state automaton, this paper describes that it is equivalent to fuzzy finite-state automaton, and its convergence and stability on its hierarchy system will be discussed. Finally, the simulation is carried on and the simulation results show that the states of fuzzy infinite-state automaton converge to some stable states with extraction frame and training for weights what this paper provides at last. Finally, some problems of fuzzy infinite-state automaton and neural networks to be solved and development trends are discussed. These researches will not only extend further automata hierarchy, but also increase a new tool for application of fuzzy signal processing. It is an important base in the application of fuzzy automata theory. [ABSTRACT FROM AUTHOR]

This paper is devoted to the investigation of practical stability of a class of uncertain T-S fuzzy systems. The uncertainties satisfy the so-called "matching conditions" and the bound is known. In this paper, a sufficient condition for practical stability of dynamic systems is first presented. Then a controller design method for uncertain T-S fuzzy systems is derived, which is based on the above sufficient condition and the quadratic stability condition for T-S fuzzy systems. The advantage of our approach is that the controller we present contains a tuning parameter which can improve systems performance and the main computation process can be conveniently performed by LMI toolbox. Finally, an example is given to illustrate the application of the obtained approach. [ABSTRACT FROM AUTHOR]

Various approaches are presented to solve the growing spam problem. However, most of these approaches are inflexible to adapt to spam dynamically. This paper proposes a novel approach to counter spam based on spam behavior recognition using Decision Tree learned from data maintained during transfer sessions. A classification is set up according to email transfer patterns enabling normal servers to detect malicious connections before mail body delivered, which contributes much to save network bandwidth wasted by spams. An integrated Anti-Spam framework is founded combining the Behavior Classification with a Bayesian classification. Experiments show that the Behavior Classification has high precision rate with acceptable recall rate considering its bandwidth saving feature. The integrated filter has a higher recall rate than either of the sub-modules, and the precision rate remains quite close to the Bayesian Classification. [ABSTRACT FROM AUTHOR]

This paper introduces the palmprint classification and recognition method based on PCA (Principal Components Analysis) using the Palmprint Acquisition Device. And the 75dpi palmprint image which is obtained by the palmprint acquisition device is used for the effectual palmprint recognition system. PCA have been previously applied to other biometric authentication and classification tasks, but not to palmprint images. We describe how the extraction of feature in the palmprint surface using Optimized PCA. The proposed system consists of the palmprint acquisition device and the palmprint authentication algorithm. [ABSTRACT FROM AUTHOR]

Bayesian technique is a popular tool for object detection due to its high efficiency. As it compares pixel by pixel, it takes a lot of execution time. This paper addresses a novel framework for head detection with minimum time and high accuracy. To detect head from motion pictures, motion segmentation algorithm is employed. The novelty of this paper carried out with the following steps: frame differencing, preprocessing, detecting edge lines and restoration, finding the head area and cutting the head candidate. Moreover, nested K-means algorithm is adopted to find head location and statistical modeling is employed to determine face or non-face class, while Bayesian Discriminating Features (BDF) method is employed to verify the faces. Finally, the proposed system is carried out with a lot of experiments and a recognizable success is notified. [ABSTRACT FROM AUTHOR]

Optical camera based detection method is a popular system to fulfill pedestrian detection; however, it is difficult to be used to detect pedestrians in complicated environment (e.g. rainy or snowy weather conditions). The difficulties mainly include: (1) The light is much weaker than in sunny days, therefore it is more difficult to design an efficient classification mechanism; (2) Since a pedestrian always be partly covered, only using its global features (e.g. appearance or motion) may be mis-detected; (3) The mirror images on wet road will cause a lot of false alarms. In this paper, based on our pervious work, we introduce a new system for pedestrian detection in rainy or snowy weather. Firstly, we propose a cascaded classification mechanism; and then, in order to improve detection rate, we adopt local appearance features of head, body and leg as well as global features. Besides that, a specific classifier is designed to detect mirror images in order to reduce false positive rate. The experiments in a single optical camera based pedestrian detection system show the effeteness of the proposed system. [ABSTRACT FROM AUTHOR]

SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications. [ABSTRACT FROM AUTHOR]

A new stocks analysis method based on clustering is presented in this paper, in which, six-dimension feature space is constructed according to the data structure of stock chief-index, and the constructed feature space is analyzed with a new fuzzy kern clustering algorithm. We use the Shanghai and Shenzhen's stock index since 1997 to test our presented method. The results show that the method could intelligently recognizes some rules of essence trends of the stock markets and forecasts essence direction of the stock markets not only in short-term but also in long-term. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an algorithm based on energy information obtained from Wavelet Transform for classification of medical images according to imaging modalities and body parts. Various medical image retrieval systems are available today that classify images according to imaging modalities, orientations, body parts or diseases. Generally these are limited to either some specific body part or some specific disease. Further, almost all of them deal with the DICOM imaging format. Our technique, on the other hand, can be applied to any of the imaging formats. The results are shown for JPEG images in addition to DICOM imaging format. We have used two types of wavelets and we have shown that energy obtained in either case is quite distinct for each of the body part. [ABSTRACT FROM AUTHOR]

In this paper a study of similarity based classifier with generalized mean and ideal class vector approach is carried out. Before this ideal class vectors in the classifier has been very little investigated area and here focus is changed to study truly 'ideal' vectors to represent class and similarity measure with its power parameters has been taken from best results in our previous studies. To find correct ideal vectors a search using differential evolution algorithm is carried out. [ABSTRACT FROM AUTHOR]

A fuzzy random chance-constrained programming method for quantifying transmission reliability margin is presented in this paper. Uncertainty problem of power systems is modeled as a fuzzy stochastic optimization problem, solved the hybrid intelligence algorithm based on chance constrained programming model. The simulation results of IEEE30 system demonstrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

As the World Wide Web becomes a large source of digital music, the music recommendation system has got a great demand. There are several music recommendation systems for both commercial and academic areas, which deal with the user preference as fixed. However, since the music preferred by a user may change depending on the contexts, the conventional systems have inherent problems. This paper proposes a context-aware music recommendation system (CA-MRS) that exploits the fuzzy system, Bayesian networks and the utility theory in order to recommend appropriate music with respect to the context. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system. Keywords: context-awareness, music recommendation system, fuzzy system, Bayesian networks, utility theory. [ABSTRACT FROM AUTHOR]

Based on long-term industrial practice, our research team addressed economic view to complement the existing CIM system architecture, which is used to measure the relevant performance and has become the annex C of ISO 15704. However the work on economic view issued before mainly focused on the performance framework, in this paper a fuzzy performance modeling process and method is proposed as the further research. Considering the numerous dynamic and subjective indicators of performance, the related fuzzy method that the research largely relies on here is the best choice for its natural advantages in this area. Before the method for fuzzy performance modeling is put forward, the fuzzy performance modeling framework aligned with process and organization model is discussed firstly. Afterwards, a theorem is presented for conflict and redundancy validation of fuzzy rules that is necessary for performance modeling. Finally the fuzzy performance modeling and measurement method are suggested. [ABSTRACT FROM AUTHOR]

Multi-level thresholding is a method that is widely used in image segmentation. However, most of the existing methods are not suited to be directly used in applicable fields, and moreover they are not extended into a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first, we classify pixels of each color channel to two clusters by using EWFCM algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method, as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and an existing method and much better segmentation results are obtained by the proposed post-processing method. [ABSTRACT FROM AUTHOR]

Most conflicts in collaborative design are categorized as the problem of fuzzy multiple attributive group decision-making (FMAGDM). Both fuzzy assessments and the aggregation of multiple experts' opinions should be considered in the conflict resolution process. This paper presents a new approach for the problem, where cooperation degree (CD) and reliability degree (RD) are introduced for aggregating the vague experts' opinions. Furthermore, a fuzzy multiple attributive group decision-making expert system (FMAGDMES) is proposed to provide an interactive way to solve conflicts in collaborative environment. It is an intelligent integrated system because it combines fuzzy set theory with the method of group opinion aggregation. The vehicle performance evaluation as a real case is used to validate the efficiency of the proposed expert system, which is implemented by using c++. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy edge-direction detector is introduced firstly, and then a fuzzy edge-sensitive noise reduction filter is presented in the basis of edge-sensitive noise reduction algorithm proposed by Adelmann (1999). The experimental results show that the method can obtain better results than Adelmann's and other schemes, such as Russo's FIRE filter (1997), in respect of smoothing impulse, Gaussian and mixed noise and preserving edges. [ABSTRACT FROM AUTHOR]

Image resolution enhancement is receiving a great deal of attention in the wide increasing use of digital imaging technologies recently. This paper presents a feature-oriented fuzzy shock-diffusion equation, where the shock term is used to sharpen edges along the normal direction to the isophote line (edge), while the diffusion term is used to remove artifacts ("jaggies") along the tangent direction. A fuzzy decision mechanism is used to preserve image features such as edge, texture and fine part. Finally, a shock capturing scheme with a special limiter function is developed to speed the process with numerical stability. Experimental results on real images demonstrate that our algorithm substantially improves the subjective quality of the enhanced images over conventional interpolations and some related equations. [ABSTRACT FROM AUTHOR]

An approach to perceptual segmentation of textured images by fuzzy clustering of spatial patterns is proposed in this paper. The dissimilarity between a texture feature, which is modeled as a spatial pattern, and each cluster is calculated as a combination of the Euclidean distance in the feature space and the spatial dissimilarity, which reflects how much of the pattern's neighborhood is occupied by other clusters. The proposed algorithm has been applied to the segmentation of texture mosaics. The results of comparative experiments demonstrate that the proposed approach can segment textured images more effectively and provide more robust segmentations. [ABSTRACT FROM AUTHOR]

This paper investigates the tracking problem of nonholonomic mobile robots. A control structure combining a kinematic controller and a dynamic controller based on nonlinear feedback control plus fuzzy compensator is presented. The fuzzy compensator, whose parameters are tuned on-line, is employed to approximate the total uncertainty including the structured and unstructured uncertainties due to the universal approximation property of fuzzy logic systems. The stability of the proposed approach is guaranteed by the Lyapunov theory. Simulation results show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "Avoidance" of multiple autonomous mobile robots are represented by a small number of fuzzy rules. Fuzzy rules in Sugeno type and their related parameters are automatically generated from clustering input-output data obtained from the algorithms for the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots. [ABSTRACT FROM AUTHOR]

In this paper, a classifier ensemble method based on fuzzy integral for fuzzy classifiers is proposed. The object of this method is to reduce subjective factor in building a fuzzy classifier, and to improve the classification recognition rate and stability for classification system. For this object, a method of determining fuzzy integral density based on membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced. The method of selecting classifier sets is also presented. The proposed method is evaluated by the comparison of experiments with standard data sets and the existed classifier ensemble methods. [ABSTRACT FROM AUTHOR]

Wigner-Ville distribution (WVD) is a joint time-frequency analysis for non-stationary signals. The main difficulty with the WVD is its bilinear characteristic which leads to cross terms in the time-frequency domain. Recently the technique of empirical mode decomposition (EMD) has been proposed as a novel tool for the analysis of nonlinear and non-stationary data. In this paper, key elements of the numerical procedure and principles of EMD are introduced. Wigner-Ville distribution based on EMD is applied in the research of the faults diagnosis of the bearing. Firstly, the original time series data is decomposed in intrinsic mode functions (IMFs) using the empirical mode decomposition. Then, the Wigner-Ville distribution for selected IMF is calculated. The signal simulation and experimental results show that Wigner-Ville distribution based on EMD can not only successfully eliminate the cross terms but also effectively diagnosis the faults of the bearing. [ABSTRACT FROM AUTHOR]

The wireless sensor network is rapidly emerging as an important tool for various applications. In this network, where a large subset of the network applications monitors and protects critical infrastructures, security is a particularly important issue. However, ensuring the security in the sensor network is complicated due to limited energy resources. In this paper, a fuzzy logic based anomaly detection scheme which provides the security to the directed diffusion protocol is proposed. The scheme is effective in preventing Denial-of-Service type attacks, which drain the energy resources within the nodes. Fuzzy logic is exploited in order to obtain a high detection rate by considering factors such as the node energy level, neighbor nodes list, message transmission rate, and error rate in the transmission. [ABSTRACT FROM AUTHOR]

Given the fact that the current Internet is getting more difficult in handling the traffic congestion control, new techniques are required. This paper proposes a multi-model fuzzy controller for AQM routers which consolidates the advantages of FLC and PID controller. Simulation results show that the proposed AQM scheme does improve the end-to-end performance. [ABSTRACT FROM AUTHOR]

Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and stability routing in mobile ad hoc network (MANET) is a challenging task. This paper presents an Entropy-based Stability QoS Routing with Priority scheduler in MANET using fuzzy controllers (ESQRP). The key idea of ESQRP algorithm is to construct the new metric-entropy and select the stability path with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network. The simulation results shows that the ESQRP approach provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks. [ABSTRACT FROM AUTHOR]

Ontology formal model and ontology checking recently are still under hot discussion. In this paper, an extended ontology model is constructed using Description Logics. Based on the extended model, the issue on ontology checking is studied with the conclusion that the four kinds of term checking, including term satisfiability checking, term subsumption checking, term equivalence checking and term disjointness checking, can be reduced to the satisfiability checking, and satisfiability checking can be transformed into instantiation consistence checking. [ABSTRACT FROM AUTHOR]

It is the most important issue that the restrictive security policy and correct concurrency control is guaranteed. This paper considers the security of heterogeneous system with replicated data. The existed read-from relationship in the existed serializability is improper. So, we define secure read-from relationship and propose secure one-copy quasi-serializability by utilizing this relationship and display some examples. The proposed secure one-copy quasi-serializability is very proper for global transactions in that this serializability doesn't violate security autonomy and prevents covert channel between global transactions. [ABSTRACT FROM AUTHOR]