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Multi-attribute decision problems where the performances of the alternatives are random variables are considered in this paper. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. [ABSTRACT FROM AUTHOR]

MOLAP is a important application on multidimensional data warehouse. We often execute range queries on aggregate cube computed by pre-aggregate technique in MOLAP. For the cube with d dimensions, it can generate 2d cuboids. But in a high-dimensional cube, it might not be practical to build all these cuboids. In this paper, we propose a multi-dimensional hierarchical fragmentation of the fact table based on multiple dimension attributes and their dimension hierarchical encoding. This method partition the high dimensional data cube into shell mini-cubes. The proposed data allocation and processing model also supports parallel I/O and parallel processing as well as load balancing for disks and processors. We have compared the methods of shell mini-cubes with the other existed ones such as partial cube and full cube by experiment. The results show that the algorithms of mini-cubes proposed in this paper are more efficient than the other existed ones. [ABSTRACT FROM AUTHOR]

This paper proposes computational intelligence for a perceptual system of a partner robot. The robot requires the capability of visual perception to interact with a human. Basically, a robot should perform moving object extraction, clustering, and classification for visual perception used in the interaction with human. In this paper, we propose a total system for human clustering for a partner robot by using long-term memory, k-means, self-organizing map and fuzzy controller is used for the motion output. The experimental results show that the partner robot can perform the human clustering. [ABSTRACT FROM AUTHOR]

In this paper, we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers, which are the target for recognition. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of individual codes. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm performs better for extraction and recognition of container identifiers compared to conventional algorithms. [ABSTRACT FROM AUTHOR]

Face recognition based only on the visual spectrum is not accurate or robust enough to be used in uncontrolled environments. This paper describes a fusion of visible and infrared (IR) imagery for face recognition. In this paper, a scheme based on membership function and fuzzy integral is proposed to fuse information from the two modalities. Recognition rate is used to evaluate the fusion scheme. Experimental results show the scheme improves recognition performance substantially. [ABSTRACT FROM AUTHOR]

Risk in technological innovation is one of the important factors that hold enterprises from launching technological innovation. What cause the technological innovation risks is very complicated, and traditional methods of risk discernment can only draw general estimate on the risks. But enterprises need to understand the concrete links that cause technological innovation risks. For this reason, this paper puts forward a novel method of risk discernment in technological innovation, combining technological path graph with variable weight fuzzy evaluation, in order to clearerly, more accurately find the positions, in which technological innovation risks may take place, and evaluate the risks. Finally, the paper has verified the dependability of this method experimentally. [ABSTRACT FROM AUTHOR]

This paper discusses the age-dependent replacement policy, in which the interarrival lifetimes of components are characterized as random fuzzy variables. A random fuzzy expected value model is presented and shown how it can be applied to reduce the loss of system failures. To solve the proposed model, a simultaneous perturbation stochastic approximation (SPSA) algorithm based on random fuzzy simulation is developed to search the optimal solution. At the end of this paper, a numerical example is enumerated. [ABSTRACT FROM AUTHOR]

In this paper, hybrid genetic-SPSA algorithm based on random fuzzy simulation is proposed for solving chance-constrained programming in random fuzzy decision-making systems by combining random fuzzy simulation, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA). In the provided algorithm, random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable, GA is employed to search for the optimal solution in the entire space, and SPSA is used to improve the new chromosomes obtained by crossover and mutation operations at each generation in GA. At the end of this paper, an example is given to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

The derivative problems from the classical shortest path problem (SPP) are becoming more and more important in real life[1]. The dissimilar shortest paths problem is a typical derivative problem. In Vehicles Navigation System(VNS),it is necessary to provide drivers alternative paths to select. Usually, the path selected is a dissimilar path to the jammed path. In fact, "dissimilar" is fuzzy. Considering traffic and transportation networks in this paper, we put forward to the definition of dissimilar paths measure that takes into account the decision maker's preference on both the road sections and the intersections. The minimum model is formulated in which not only the length of paths but also the paths dissimilar measure is considered. And a genetic algorithm also is designed. Finally, we calculate and analyze the dissimilar paths in the traffic network of the middle and east districts of Lanzhou city in P.R. of China by the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Based on the Fuzzy mathematical principle, a fuzzy integral model on forecasting the cancerational tendency of hydatidiform mole is created. In this paper, attaching function, quantum standard, weight value of each factor, which causes disease, and the threshold value of fuzzy integral value are determined under condition that medical experts take part in. The detailed measures in this paper are taken as follows: First, each medical expert gives the score of the sub-factors of each factor based on their clinic experience and professional knowledge. Second, based on analyzing the feature of the scores given by medical experts, attaching functions are established using K power parabola larger type. Third, weight values are determined using method by the analytic hierarchy process[AHP] method. Finally, the relative information is obtained from the case histories of hydatidiform mole cases. Fuzzy integral value of each case is calculated and its threshold value is finally determined. Accurate rate of the fuzzy integral model(FIM) is greater than that of the maximum likelihood method (MLM) via diagnosing the history cases and for new cases, the diagnosis results of the FIM is in accordance with those of the medical experts. [ABSTRACT FROM AUTHOR]

Associative classification is a well-known technique which uses association rules to predict the class label for new data object. This model has been recently reported to achieve higher accuracy than traditional classification approaches like C4.5. In this paper, we propose a novel associative classification algorithm based on correlation analysis, ACBCA, which aims at extracting the k-best strong correlated positive and negative association rules directly from training set for classification, avoiding to appoint complex support and confidence threshold. ACBCA integrates the advantages of the previously proposed effective strategies as well as the new strategies presented in this paper. An extensive performance study reveals that the improvement of ACBCA outperform other associative classification approaches on accuracy. [ABSTRACT FROM AUTHOR]

Rough set theory (RST) can mine useful information from a large number of data and generate decision rules without prior knowledge. Support vector machines (SVMs) have good classification performances and good capabilities of fault-tolerance and generalization. To inherit the merits of both RST and SVMs, a hybrid classifier called rough set support vector machines (RS-SVMs) is proposed to recognize radar emitter signals in this paper. RST is used as preprocessing step to improve the performances of SVMs. A large number of experimental results show that RS-SVMs achieve lower recognition error rates than SVMs and RS-SVMs have stronger capabilities of classification and generalization than SVMs, especially when the number of training samples is small. RS-SVMs are superior to SVMs greatly. [ABSTRACT FROM AUTHOR]

A relatively new area of research in centering on the phonetic encoding of information. This paper deals with the possible computer applications of the Sound Approach © English phonetic alphabet. The authors review some preliminary research into a few of the more promising approaches to the application of the processes of machine learning to this phonetic alphabet for computer spell-checking, computer speech recognition etc. Applying mathematical approaches to the development of a data-based phonetic spelling recognizer, and speech recognition technology used for language pronunciation training in which the speech recognizer allows a large margin of pronunciation accuracy, the authors delineate the parameters of the current research, and point the direction of both the continuation of the current project and future studies. [ABSTRACT FROM AUTHOR]

In this paper, a method to construct of a stable dual-mode predictive controller of unknown nonlinear system using the fuzzy system as a predictive model is proposed. The dual-mode controller is designed to ensure the stability in this region. In the neighborhood of the origin, a linear feedback controller designed for the linearized system generates the control action. Outside this neighborhood, predictive controller based on the fuzzy model is applied to the real nonlinear system. This method yields a stable closed-loop system when is applied to nonlinear systems under some conditions. [ABSTRACT FROM AUTHOR]

This paper firstly compares rough sets theory with grey system theory, then the concept of grey sets is proposed and the grey degree of grey sets and the basic relationships and operations of grey sets are defined. Next, we set up the models of grey rough sets and wide grey rough sets as well as study their properties. Moreover, the definition of rough grey sets is given and their basic characters are investigated. Furthermore, the relations between rough grey sets and grey rough sets are researched. Finally, we come to the conclusion that it is possibly more effective to deal with some uncertain problems if the theories and methods of rough sets and grey systems are combined. [ABSTRACT FROM AUTHOR]

Rough set (RS) theory is a mathematics tool for handling uncertain problem. It is helpful for KDD, but expensive consumption of time and unclear expression of result are the main problem in practical application. The extended concept lattice (ECL) jis a new form of concept lattice which is gotten by introducing equivalence intension into Galois concept lattice (GCL). The ECL is an efficient tool for data analysis and knowledge discovery in database (KDD). Both ECL and RS are based on equivalence class, so the relative between them exists. This paper describes the ECL first, then discusses the relation between the ECL and RS, and describes the implementation of rough set based on ECL. Keywords: KDD, Concept Lattice, Rule, Rough set. [ABSTRACT FROM AUTHOR]

In the axiomatic approach of fuzzy rough set theory, fuzzy rough approximation operators are characterized by a set of axioms that guarantees the existence of certain types of fuzzy binary relations reproducing the operators. Thus axiomatic characterization of fuzzy rough approximation operators is an important aspect in the study of rough set theory. In this paper, the independence of axioms of generalized fuzzy rough approximation operators is investigated, and their minimal sets of axioms are presented. [ABSTRACT FROM AUTHOR]

Attribute reduction is one of the basic contents in decision table. And it has been proved that computing the optimal attribute reduction is NP-complete. A lot of algorithms for the optimal attribute reduction were proposed in consistent decision table. But most decision tables are inconsistent in fact. In this paper, the judgment theorem with respect to maximum distribution reduction is obtained and the significance of attributes is defined in decision table, from which a polynomial heuristic algorithm for the optimal maximum distribution reduction is proposed. Finally, the experimental results show that this algorithm is effective and efficient. [ABSTRACT FROM AUTHOR]

This paper is devoted to the discussion of the relationship among some reduction approaches of information systems. It is proved that the distribution reduction and the entropy reduction are equivalent, and each distribute reduction is a d reduction. Furthermore, for consistent information systems, the distribution reduction, entropy reduction, maximum distribution reduction, distribute reduction, approximate reduction and d reduction are all equivalent. [ABSTRACT FROM AUTHOR]

The problem of real value attribute discretization can be converted into the reduct problem in the Rough Set Theory, which is NP-hard and can be solved by some heuristic algorithms. In this paper we show that the straightforward conversion is not scalable and propose a divide-and-conquer algorithm. This algorithm is fully scalable and can reduce the time complexity dramatically especially while integrated with the tournament discretization algorithm. Parallel versions of this algorithm can be easily written, and their complexity depends on the number of objects in each subtable rather than the number of objects in the initial decision table. There is a tradeoff between the time complexity and the quality of the discretization scheme obtained, and this tradeoff can be made through adjusting the number of subtables, or equivalently, the number of objects in each subtable. Experimental results confirm our analysis and indicate appropriate parameter setting. [ABSTRACT FROM AUTHOR]

At present, much more research in the field of soft sensor modeling is concerned. In the process of establishing soft sensor models, how to select the secondary variables is still an unresolved question. In this paper, rough set theory is used to select the secondary variables from the initial sample data. This method is used to build the soft sensor model to estimate the oxygen concentration in a regeneration tower and the good result is obtained. [ABSTRACT FROM AUTHOR]

An efficient design method to improve the control performance of rough controller is presented in this paper. As the input-output data of the history process operation may not be enough informative, extra testing signals are used to excite the process to acquire sufficient data reflecting the control laws of the operator or the existing controller. Using data from the successive exciting tests or excellent operation by operators, the rules can be updated and enriched, which is helpful to improve the performance of the rough controller. The effectiveness of the proposed method is demonstrated through two simulation examples emulating PID control and Bang-Bang control, respectively. [ABSTRACT FROM AUTHOR]

Multiple classifier systems have become a popular classification paradigm for strong generalization performance. Diversity measures play an important role in constructing and explaining multiple classifier systems. A diversity measure based on relation entropy is proposed in this paper. The entropy will increase with diversity in ensembles. We introduce a technique to build rough decision forests, which selectively combine some decision trees trained with multiple reducts of the original data based on the simple genetic algorithm. Experiments show that selective multiple classifier systems with genetic algorithms get greater entropy than those of the top-classifier systems. Accordingly, good performance is consistently derived from the GA based multiple classifier systems although accuracies of individuals are weak relative to top-classifier systems, which shows the proposed relation entropy is a consistent diversity measure for multiple classifier systems. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) and semantic-based image retrieval (SBIR) have attracted great research attentions. However, they all have disadvantages. This paper proposes a retrieval method trying to overcome them. To achieve this we first introduce an approach of rough set-based low-level features selection. We propose an approach of feedback-based semantic-level features annotation. We also introduce a corresponding computing technology of similarity. We then build a model of combination of these two kinds of methods. Experimental results show that our method is more user-adaptive, and can achieve better performance compared with another retrieval method which is only based on low-level features. [ABSTRACT FROM AUTHOR]

Image segmentation is an important research topic in image processing and computer vision community. In this paper, a new unsupervised method for MR brain image segmentation is proposed based on fuzzy c-means (FCM) and fuzzy connectedness. FCM is a widely used unsupervised clustering algorithm for pattern recognition and image processing problems. However, FCM does not consider the spatial coherence of images and is sensitive to noise. On the other hand, fuzzy connectedness method has achieved good performance for medical image segmentation. However, in the computation of fuzzy connectedness, one needs to select seeds manually which is elaborative and time-consuming. Our new method used FCM as the first step to select salient seeded points and then applied fuzzy connectedness algorithm based on those seeds. Thus our method achieved unsupervised automatic segmentation for brain MR images. Experiments on simulated and real data sets proved it is effective and robust to noise. [ABSTRACT FROM AUTHOR]

Distributed Data Mining (DDM) generally deals with the mining of data within a distributed framework such as local area and wide area networks. One strong case for DDM systems is the need to mine for patterns in very large databases. This requires mandatory partitioning or splitting of databases into smaller sets which can be mined locally over distributed hosts. Data Distribution implies communication costs associated with the need to combine the results from processing local databases. This paper considers the development of a DDM system on a cluster. In specific we approach the problem of data partitioning for data mining. We present a prototype system for DDM using a data partitioning mechanism based on Bayesian mixture modeling. Results from comparison with standard techniques show plausible support for our system and its applicability. [ABSTRACT FROM AUTHOR]

A popular solution to improving the speed and scalability of association rule mining is to do the algorithm on a random sample instead of the entire database. But it is at the expense of the accuracy of answers. In this paper, we present a sampling ensemble approach to improve the accuracy for a given sample size. Then, using Monte Carlo theory, we give an explanation for a sampling ensemble and obtain the theoretically low bound of sample size to ensure the feasibility and validity of an ensemble. And for learning the origination of the sample error and therefore giving theoretical guidance for obtaining more accurate answers, bias-variance decomposition is used in analyzing the sample error of an ensemble. According to theoretical analysis and real experiments, we conclude that sampling ensemble method can not only significantly improve the accuracy of answers, but also be a new means to solve the difficulty of determining appropriate sample size needed. [ABSTRACT FROM AUTHOR]

This paper begins with a new algorithm for computing time sequence data expansion distance on the time domain that, with a time complexity of O(n×m), solves the problem of retained similarity after the shifting and scaling of time sequence on the Y axis. After this, another algorithm is proposed for computing time sequence data expansion distance on frequency domain and searching similar subsequence in long time sequence, with a time complexity of merely O(n×fc), suitable for online implementation for its high efficiency, and adaptable to the extended definition of time sequence data expansion distance. An incremental DFT algorithm is also provided for time sequence data and linear weighted time sequence data, which allows dimension reduction on each window of a long sequence, simplifying the traditional O(n×m×fc) to O(n×fc). [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scan. A frequent itemset is a set of common items that are included in at least as many transactions as a given minimum support. While scanning the database of transactions, our algorithm generates a table having 1-frequent items and a list of transactions per each 1-frequent item, and generates 2-frequent itemsets by using a hash technique. k(k≥3)-frequent itemsets can be simply found by checking whether for all (k-1)-frequent itemsets used to generate a k-candidate itemset, the number of common transactions in their lists is greater than or equal to the minimum support. The experimental analysis of our algorithm has shown that it can generate frequent itemsets more efficiently than FP-growth algorithm. [ABSTRACT FROM AUTHOR]

Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research criticized that discovering subsequence patterns in time series using clustering approaches is meaningless. It is due to the presence of trivial matched subsequences in the formation of the time series subsequences using sliding window method. The objective of this paper is to propose a threshold-free approach to improve the method for segmenting long stock time series into subsequences using sliding window. The proposed approach filters the trivial matched subsequences by changing Perceptually Important Point (PIP) detection and reduced the dimension by PIP identification. [ABSTRACT FROM AUTHOR]

The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. Furthermore, the observations in the sound environment are often contain fuzziness due to several causes. In this paper, a method for estimating the specific signal for acoustic environment systems with unknown structure and fuzzy observation is proposed by introducing a fuzzy probability theory and a system model of conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem of psychological evaluation for the sound environment. [ABSTRACT FROM AUTHOR]

This paper presents a clustering algorithm named HYBRID. HYBRID has two phases: in the first phase, a set of spherical atom-clusters with same size is generated, and in the second phase these atom-clusters are merged into a set of molecule-clusters. In the first phase, an incremental clustering method is applied to generate atom-clusters according to memory resources. In the second phase, using an edge expanding process, HYBRID can discover molecule-clusters with arbitrary size and shape. During the edge expanding process, HYBRID considers not only the distance between two atom-clusters, but also the closeness of their densities. Therefore HYBRID can eliminate the impact of outliers while discovering more isomorphic molecule-clusters. HYBRID has the following advantages: low time and space complexity, no requirement of users' involvement to guide the clustering procedure, handling clusters with arbitrary size and shape, and the powerful ability to eliminate outliers. [ABSTRACT FROM AUTHOR]

This paper discusses a surface reconstruction method using the Delaunay triangulation algorithm. Surface reconstruction is used in various engineering applications to generate CAD model in reverse engineering, STL files for rapid prototyping and NC codes for CAM system from physical objects. The suggested method has two other components in addition to the triangulation: the weighted alpha shapes algorithm and the peel-off algorithm. The weighted alpha shapes algorithm is applied to restrict the growth of tetrahedra, where the weight is calculated based on the density of points. The peel-off algorithm is employed to enhance the reconstruction in detail. The results show that the increase in execution time due to the two additional processes is very small compared to the ordinary triangulation, which demonstrates that the proposed surface reconstruction method has great advantage in execution time for a large set of points. [ABSTRACT FROM AUTHOR]

The IP address lookup problem is to find the longest matching IP prefix from a routing table for a given IP address. In this paper we implemented and extended the results of [3] by incorporating the access frequencies of the target IP addresses. Experimental results showed that the number of memory access is reduced significantly. [ABSTRACT FROM AUTHOR]

The development of CAM system for design and manufacturing of end mills becomes a key approach to save the time and reduce cost for end mills manufacturing. This paper presents the calculation and simulation of CNC machining end mill tools using on 5-axes CNC grinding machine tool. In this study the process of generation and simulation of grinding point data between the tool and the grinding wheels through the machined time are describes. Using input data of end mill geometry, wheels geometry, wheel setting, machine setting the end mill configuration and NC code for machining will be generated and visualized in 3 dimension before machining. The 3D visualizations of end mill manufacturing was generated by using OpenGL in C++. [ABSTRACT FROM AUTHOR]

In this paper, an Adaptive and Active Computing Paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user-centered, push-based high quality information service timely in a proper way, the motivation of which is generalized as R4 Service: the Right information at the Right time in the Right way to the Right person, upon which formalized algorithms of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push-based, event-driven, interest-related, adaptive and active information service mode, which is useful and promising for long-term user to gain fresh information instead of polling from kinds of information sources. Performance evaluations based on the AACP retrieval system that we have fully implemented manifest the proposed schema is effective, stable, feasible for adaptive and active information service in distributed heterogeneous environment. [ABSTRACT FROM AUTHOR]

To search a pattern P in a text, index data structures such as suffix trees and suffix arrays are widely used. It is known that searching with suffix trees is faster than with suffix arrays in the aspect of time complexity. But recently, a few linear-time search algorithms for constant-size alphabet in suffix arrays have been suggested. One of such algorithms proposed by Sim et al. uses Burrows-Wheeler transform and takes $O(P\log\Sigma)$ time. But this algorithm needs too much space compared to Abouelhoda et al.'s algorithm to search a pattern. In this paper we present an improved version for Sim et al.'s algorithm. It needs only 2n bytes at most if a given alphabet is sufficiently small. [ABSTRACT FROM AUTHOR]

A MDO framework is a collaborative distributed computing environment that facilitates the integration of multi-disciplinary design efforts to achieve the global optimum result among local mutually-conflicting optimum results on heterogeneous platforms throughout the entire design process. The challenge for the MDO framework is to support the integration of legacy software and data, workflow management, heterogeneous computing, parallel computing and fault tolerance at the same time. In this paper, we present a Linda tuple space-based distributed computing framework optimized for MDO which is called MEDIC. In the design of MEDIC, we classify required technologies and propose an architecture in which those technologies can be independently implememnted at different layers. The Linda tuple space allows us to make the MEDIC architecture simple because it provides a flexible computing platform where various distributed and parallel computing models are easily implemented in the same way, multi-agents are easily supported, and effective fault tolerance techniques are available. A prototype system of MEDIC has been developed and applied for building an integrated design environment for super-high temperature vacuum furnaces called iFUD. [ABSTRACT FROM AUTHOR]

For a given product or object, predicting human emotions is very important in many business, scientific and engineering applications. There has been a significant amount of research work on the image-based analysis of human emotions in a number of research areas because human emotions are usually dependent on human vision. However, there has been little research on the computer image processing-based prediction, although such approach is naturally very appealing. In this paper, we discuss challenging issues in how to index images based on human emotions and present a heuristic approach to emotion-based image indexing. The effectiveness of image features such as colors, textures, and objects (or shapes) varies significantly depending on the types of emotion or image data. Therefore, we propose adaptive and selective techniques. With respect to six adverse pairs of emotions such as weak-strong, we evaluated the effectiveness of those techniques by applying them to the set of about 160 images in a commercial curtain pattern book obtained from the Dongdaemoon textile shopping mall in Seoul. Our preliminary experimental results showed that the proposed adaptive and selective strategies are effective and improve the accuracy of indexing significantly depending on the type of emotion. [ABSTRACT FROM AUTHOR]

In the Knowledge Economy era, trying to promote the whole competition advantage, the electronic businesses utilize information technology and internet to integrate the various kinds of application systems, database, and platform. It becomes common practice to construct an Enterprise Digital Content Portal (EDCP). To vary from minute to minute, coming to the problem of business environment is so difficult to integrate the complicated and huge amount information. By the way of collaboration of EDCP and the information of trade partners, it can provide the customers the real-time information that appearing with dynamic various timing. Through the combine of the information and procedure that between the enterprise and it's business partner, it can use the assisting of information technology, improve the enterprise's internal and external operational procedures, raise the transparency of information in the value chain, achieve the purpose that sharing information with different platform and language. In order to combine the different kinds of platform's information between the different enterprises, we use Java technologies for web services in the construction and development of EDCP, use the Extended Markup Language, and the Web-based communication protocol, it communicates with other software system [1], accomplish the framework of the knowledge service platform that the enterprise deliver and communicate the internal and external information. In this paper, we propose the construction structure of the EDCP that using Web Services, we implement it into one case and utilize WebBench to be the analyzed tool that testing the efficiency when many people connect to the line at the same time, and prove the feasibility of this framework of the module. And we probe into the relevant literatures of collaboration, and make up the deficiency of relevant literatures in the past. Conduct to be the reference of the research that the enterprise and organization establish the relation of collaboration using EDCP in the future. Keywords: Enterprise Digital Content Portal, Collaboration, Web Services. [ABSTRACT FROM AUTHOR]

Discovering frequent item sets is a key problem in data mining association rules. In this paper, there is a celerity association rules method based on data sort search. Using the plenitude and call terms of frequent item sets, the method efficiency can be improved greatly for the searching time won't increase as the number of item set of the data does, moreover the data can be found by searching the database within 3 times. Using the change between the frequent item sets and standby item sets, the data celerity renew and the min-sup renew can be true. [ABSTRACT FROM AUTHOR]

A method to balance the communication among Multi-Agents in real time traffic synchronization is proposed in this research. The paper presents Air Traffic Flow Management (ATFM) problem and its synchronization property. For such a complex problem, combing grid computing with multi-agent coordination techniques to improve ATFM computational efficiency is the main objective of actual research. To demonstrate the developed model - ATFM in Grid Computing (ATFMGC), the grid architecture, the basic components and the relationship among them are described. At the same time, the function of agents (tactical planning agent etc.), their knowledge representation and inference processes are also discussed. As criteria to measure the effective to reduce quantity of the communication among agents and the delay of the flights, Standard of Balancing among Agents (SBA) is used in the analysis. The simulation shows the efficiency of the developed model and successful application in the case study. [ABSTRACT FROM AUTHOR]

The paper addresses a study case in the frame of ground transportation aiming at improving service quality inside road tunnels. As part of a global project on vehicle automation which aim is to realize a reduced scale multi-sensor platoon of vehicles, a formal specification analysis is carried out and a UML-based design introduced taking into account different riding scenarios inside road tunnels besides a fuzzy control for longitudinal and lateral guidance of a caravan of vehicles. The proposed multimodel fuzzy controllers deal with the grappling and/or the unhooking of the automated train of vehicles for safe tunnels. [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, "flocking" and "arrangement" of multiple autonomouse 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]

This paper proposes a so-called fuzzy switching multiple (FSM) model which can achieve smooth switching when the control input at switching boundaries. Parallel distributed compensation scheme is employed to design the controller for the FSM. By utilizing fuzzy Lyapunov function, we derive the stabilization condition for closed-loop FSM systems. A design example illustrates the utility of the proposed approach. [ABSTRACT FROM AUTHOR]

A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. The MCPSO is used to automatic design of fuzzy identifier and fuzzy controller for nonlinear dynamic systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamic systems. [ABSTRACT FROM AUTHOR]

The proposed controller in this paper, which combines the capability of fuzzy logic with the robustness of sliding mode controller, presents prevailing results with its adaptive architecture and proves to overcome the global stability problem of the control of nonlinear systems. Effectiveness of the controller and the performance comparison are demonstrated with chosen control techniques including PID and PD type self-tuning fuzzy controller on a quarter car model which consists of component-wise nonlinearities. [ABSTRACT FROM AUTHOR]

In this paper a strategy of grading fuzzy sliding mode control (FSMC) applied in the AC servo system is presented. It combines the fuzzy logic and the method of sliding mode control, which can reduce the chattering without decreasing the system robustness. At the same time, the exponent approaching control is added by grading. The control strategy makes the response of the system quick and no overshoot. It is simulated to demonstrate the feasible of the proposed method by MATLAB6.5 and the good control effect is received. [ABSTRACT FROM AUTHOR]

A new fuzzy control method for nonlinear pipeline system is discussed in this paper. The nonlinear dynamics of pipeline system is composed by two gravity-flow tanks, and are described by Takagi-Sugeno (T-S) fuzzy model. The controller design is based on overall stability, and is carried out via the parallel distributed compensation (PDC) scheme. To obtain better output dynamic performance, a given bounds is introduced to the output of nonlinear systems. Moreover, by means of linear matrix inequality (LMI) technique, it is shown that the existence of such constrained control system can be transformed into the feasibility of a convex optimization problem. Finally, by applying the designed controller, the simulation results demonstrate the efficiency. [ABSTRACT FROM AUTHOR]

A fuzzy model representation for describing the linguistic model of the object to be controlled in a control system is prompted. With the linguistic model of controlled object or process to be controlled, we can construct a close loop system representation. Consequently, we can discuss the system appearance with the assistance of the linguistic model as we do using a mathematic model in a conventional control system. In this paper, we discuss the describing ability of a fuzzy model and give a formal representation method for describing a fuzzy model. The combine method for a fuzzy system constructed by multiple fuzzy models is also discussed based on the controller model and the linguistic model of controlled object. [ABSTRACT FROM AUTHOR]