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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]

Electronic product code (EPC) sensor network is a collection of objects for sensing data. It is crucial to ubiquitous society. It can provide an application service based on situation dependency with its properties. The situation dependency is an emerging concept which can collect location-based and personalized information. With the situation dependency, many industries can serve ubiquitous service for independent users. u-Healthcare is one of ubiquitous service to provide seamless medical treatment. The concept of situation dependency is applied in u-Healthcare with EPC sensor network technology. Due to specialized four properties of EPC sensor network which are driven from this paper, the situation dependency is well-established in u-Healthcare service. In this paper, we defined value and architecture of u-Healthcare service and we analyzed application areas of u-Healthcare. [ABSTRACT FROM AUTHOR]

This paper describes the limiting factors for building a virtual Telematics center which integrates ITS (Intelligent Transportation Systems) information collected by the system of each of the three government agencies. It also provides some details into how some of the obstacles and barriers can be solved such as data standardization, system mechanisms and regulations. There are very few papers and case studies regarding real problems and solutions for integrating systems of different organizations in general, and Telematics, in particular. The processes and outcomes of implementing systems in this study can be useful to develop other ITS and Telematics systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user's facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present in an organized form the concept of cancer detection based on data obtained from SELDI-TOF-MS. In this paper, we outline the full process of detection: from raw data, through pre-processing towards classification. Methods and algorithms, their characteristics and suggested implementation indications are described. We aim to present the state of the art over current research. Additionally, we introduce an idea of 24h/day distributed work organization and suggest how to make the research process faster. [ABSTRACT FROM AUTHOR]

This paper considers how the interaction of quantification and dependent anaphora may be analysed in a dynamic semantics. It discusses a simple theory of the creation and accessibility of dependencies based on a dynamic semantics for distributivity and some basic assumptions on number agreement in discourse. This theory forms a partial defence of the line of semantics set out by van den Berg 1996. I argue, however, that it is essential to quantified anaphora to contextualise the labelling of antecedents. [ABSTRACT FROM AUTHOR]

This paper analyzes complex collective behaviors of a multiagent system, which consists of interacting agents with evolutionary learning capabilities. The interaction and learning of the agents are modeled by the concept of Connected Replicator Dynamics expanded from evolutionary Game Theory.The dynamic learning system we analyze shows various behavioral and decision changes including bifurcation of chaos in the sense of physical sciences.The main contributions of the paper are summarized as follows: (1) In amultiagent system, the emergence of chaotic behaviors is general and essential, even if each agent does not have chaotic properties; and (2) However,asimple controlling agent with the Keep-It-Simple-Stupid (KISS) principle, or a sheep-dog agent, is able to domesticate or tame the complex behaviors. [ABSTRACT FROM AUTHOR]

Cultural Resource Management (CRM) represents an interesting application domain for innovative approaches, models and technologies developed by computer science researchers. This paper presents NavEditOW, a system for the navigation, query and updating of ontologies through the web, as a tool providing suitable functionalities for the design and development of semantic portals in the CRM area. NavEditOW supports ontology maintainers, content editors, and end-users, that have little or no specific knowledge on Semantic Web technologies and on related formal tools. A description of the application of the tool to the representation and management of archaeological knowledge for the description of publications in an e-library is also provided. [ABSTRACT FROM AUTHOR]

In this paper, a load balancing method using ring network in the Grid database is proposed. Past research proposed to solve unbalanced load problem. But, past techniques can not be applied as the Grid database has a number of systems and user's request always changes dynamically. The proposed method connects each node having the same replicated data through ring network. If workload overflows in some node, user's request is transferred to a linked node which has the target data. And, this node stops another request from processing until the workload has significantly decreased. Then, to stop request forwarding from a previous node, it changes the link structure by sending a message to the previous node. Through performance evaluation, this paper shows that the proposed method has increased performance and is more suitable to the Grid database than the existing methods. [ABSTRACT FROM AUTHOR]

Clustering a peer-to-peer (P2P) network into distinct semantic clusters can efficiently improve the search efficiency and enhance scalability of the network. This paper considers P2P systems in which peers employ taxonomy hierarchy to describe the contents of their objects, and presents a taxonomy-based approach for constructing semantics-based super-peer networks. By dynamically clustering peers in taxonomy-based semantic space based on the semantics of their data and organizing the clusters into semantic routing overlays, an efficient query-routing algorithm can be used among these clusters. Preliminary evaluation indicates that our approach achieves a competitive trade-off between search latencies and overheads, and load-balancing among super-peers is well maintained. [ABSTRACT FROM AUTHOR]

The paper presents new developments in an extension of Codd's relational model of data. The extension consists in equipping domains of attribute values with a similarity relation and adding ranks to rows of a database table. This way, the concept of a table over domains (i.e., relation over a relation scheme) of the classical Codd's model extends to the concept of a ranked table over domains with similarities. When all similarities are ordinary identity relations and all ranks are set to 1, our extension becomes the ordinary Codd's model. The main contribution of our paper is twofold. First, we present an outline of a relational algebra for our extension. Second, we deal with implementation issues of our extension. In addition to that, we also comment on related approaches presented in the literature. [ABSTRACT FROM AUTHOR]

Cryptanalysis with genetic algorithm has attracted much interest in recent years. This paper presents an approach for the cryptanalysis of two-round DES based on genetic algorithm. However, cryptanalysis of two-round DES using genetic algorithm is usually a difficult task. In this paper, we adopt known plaintext attack and produce a variety of optimum keys based on fitness function. Furthermore, we count every bit of optimal keys one by one, and find some valuable bits, which generate a significant deviation from the other observed bits. Finally, the 56-bit key is successfully gained without searching the whole search space. The experimental result indicates that this is a promising method and can be adopted to handle other complex block ciphers. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the genetic algorithms in optimization of network planning project. It focuses on the study of multi-task scheduling (Resource-constrained project scheduling problem, RCPSP). For the problem of the resource-constrained task scheduling problem, the paper proposes a method to improve the genetic algorithms optimization of multi-task scheduling problem. From the aspects of the establishment of the optimized algorithms models, the algorithms design, the accomplishment of algorithms as well as the analysis of the result, we conducts this study in detail, and makes a comparison analysis with the other 11 heuristic methods. In this way, we testify our method and get the good results. [ABSTRACT FROM AUTHOR]

This paper focuses on the limit behaviors of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. Some convergence results of general square matrices are given, and some useful properties of homogeneous Markov chains with finite states are investigated. 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. Some applications of the theoretical results in this paper are also discussed. [ABSTRACT FROM AUTHOR]

Different constraint handling techniques have been incorporated with genetic algorithms (GAs), however most of current studies are based on computer experiments. The paper makes an theoretical analysis of GAs using penalizing infeasible solutions and repairing infeasible solutions on average knapsack problem. It is shown that GAs using the repair method is more efficient than GAs using the penalty method on average capacity knapsack problems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a novel, biologically inspired approach to extract visual features relevant for controlling and understanding reach-to-grasp actions. One of the most relevant of such features has been found to be the grip-size defined as the index finger-tip - thumb-tip distance. For this reason, in this paper we focus on this feature. The human visual system is naturally able to recognize many hand configurations - e.g. gestures or different types of grasps - without being affected substantially by the (observer) viewpoint. The proposed computational model preserves this nice ability. It is very likely that this ability may play a crucial role in action understanding within primates (and thus human beings). More specifically, a family of neurons in macaque's ventral premotor area F5 have been discovered which are highly active in correlation with a series of grasp-like movements. This findings triggered a fierce debate about imitation and learning, and inspired several computational models among which the most detailed is due to Oztop and Arbib (MNS model). As a variant of the MNS model, in a previous paper, we proposed the MEP model which relies on an expected perception mechanism. However, both models assume the existence of a mechanism to extract visual features in a viewpoint independent way but neither of them faces the problem of how this mechanism can be achieved in a biologically plausible way. In this paper we propose a neural network model for the extraction of visual features in a viewpoint independent manner, which is based on the work by Poggio and Riesenhuber. [ABSTRACT FROM AUTHOR]

Despite numerous studies have applied various forecasting models to tourism demand analysis, data mining techniques have been largely overlooked by academic researchers in tourism forecasting prior to 1999. Based on our review of published articles in tourism journals that applied data mining techniques to tourism demand forecasting, we find that the application of data mining techniques are still at their infancy. This paper concludes with practical implications and future research areas. [ABSTRACT FROM AUTHOR]

Support vector regression (SVR) is a new technique for pattern classification , function approximation and so on. In this paper we propose an new constructing approach of classification rules based on support vector regression and its derivative characteristics for the classification task of data mining. a new measure for determining the importance level of the attributes based on the trained SVR is proposed. Based on this new measure, a new approach for clas-sification rule construction using trained SVR is proposed. The performance of the new approach is demonstrated by several computing cases. The experimen-tal results prove that the approach proposed can improve the validity of the ex-tracted classification rules remarkably compared with other constructing rule approaches, especially for the complicated classification problems. [ABSTRACT FROM AUTHOR]

The heterogeneity in the contemporary multimedia environments requires a format-agnostic adaptation framework for the consumption of digital video content. Preferably, scalable bitstreams are used in order to satisfy as many circumstances as possible. In this paper, the scalable extension on the H.264/AVC specification is used to obtain the parent bitstreams. The adaptation along the combined scalability axis of the bitstreams must occur in a format-independent manner. Therefore, an abstraction layer of the bitstream is needed. In this paper, XML descriptions are used representing the high-level structure of the bitstreams by relying on the MPEG-21 Bitstream Syntax Description Language standard. The adaptation process is executed in the XML domain by transforming the XML descriptions considering the usage environment. Such an adaptation engine is discussed in this paper in which all communication is based on XML descriptions without knowledge of underlying coding format. From the performance measurements, one can conclude that the transformations in the XML domain and the generation of the corresponding adapted bitstream can be realized in real time. [ABSTRACT FROM AUTHOR]

Pattern recognition and classification problems are most popular issue in machine learning, and it seem that they meet their second golden age with bioinformatics. However, the dataset of bioinformatics has several distinctive characteristics compared to the data set in classical pattern recognition and classification research area. One of the most difficulties using this theory in bioinformatics is that raw data of DNA or protein sequences cannot be directly used as input data for machine learning because every sequence has different length of its own code sequences. Therefore, this paper introduces one of the methods to overcome this difficulty, and also argues that the capability of generalization in this method is very poor as showing simple experiments. Finally, this paper suggests different approach to select the fixed number of effective features by using Support Vector Machine, and noise whitening method. This paper also defines the criteria of this suggested method and shows that this method improves the precision of diagnosing abnormal protein sequences with experiment of classifying ovarian cancer data set. [ABSTRACT FROM AUTHOR]

To improve the safety of drivers and walkers in a city, several traffic monitors are usually set on lanes. These traffic monitors can also improve the security of communities. In this paper, we integrate the so-called linear/circular consecutive-k-out-of-n:F systems into our proposed traffic-monitor system. The objective is to find the optimal design of monitors under limited budget for the system. The main purposes of this paper are : (1) to propose a new traffic-monitor system, (2) to present an immune algorithm (IA) for the optimal design of traffic monitors, and (3) to report numerical results of various parameters by the proposed algorithm. It is shown that the proposed immune algorithm performs well for all test problems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare the effectiveness of various computational intelligence approaches applied to the task of retrieving musical rhythm from musical symbolic files. The study presented in this paper describes how Artificial Neural Networks and Rough Sets can be used for searching the metric structure of musical files. The described approaches are based on examining physical attributes of sound that are most significant in determining the placement of a particular sound in the accented location of a musical piece. The results of the experiments show that the approach based solely on duration is sufficient enough to retrieve the metric structure of rhythm from musical files. [ABSTRACT FROM AUTHOR]

This paper is an extension of our earlier work in combining strengths of rough set theory and neuro-fuzzy decision trees in classifying software defect data. The extension includes the application of a rough-fuzzy classification trees to classifying defects. We compare classification results for five methods: rough sets, neuro-fuzzy decision trees, partial decision trees, rough-neuro-fuzzy decision trees and rough-fuzzy classification trees. The analysis of the results include a paired t-test for accuracy and number of rules. The results demonstrate that there is improvement in classification accuracy with the rough fuzzy classification trees with a minimal set of rules. The contribution of this paper is a comparative study of several hybrid approaches in classifying software defect data. [ABSTRACT FROM AUTHOR]

Currently used data-mining algorithms treat data globally. Nevertheless, with such methods, potentially useful knowledge that relates to local phenomena may be undetected. In this paper, we introduce new patterns in a form of local frequent events and epochs, boundaries of which correspond to discovered changes in a data stream. A local frequent event is an event which occurs in some period of time frequently, but not necessarily in the whole data stream. Such an event will be called a frequent event in a data stream. An epoch is understood as a sufficiently large group of frequent events that occur in a similar part of the data stream. The epochs are defined in such a way that they do not overlap are separated by so called change periods. In the paper, we discuss some potential applications of the proposed knowledge. Preliminary experiments are described as well. [ABSTRACT FROM AUTHOR]

In T −fuzzy rough sets a fuzzy T −similarity relation is employed to describe the similar degree between two objects and to construct lower and upper approximations for arbitrary fuzzy sets. The existing researches on T −fuzzy rough sets mainly concentrate on constructive and axiomatic approaches of lower and upper approximation operators. In this paper we define attribute reduction based on T −fuzzy rough sets. The structure of proposed attribute reduction is investigated in detail by the approach of discernibility matrix. At last an example is proposed to illustrate our idea in this paper. [ABSTRACT FROM AUTHOR]

The subject-matter of the consideration touches the problem of vagueness. The notion of the rough set, originated by Zdzisław Pawlak, was constructed under the influence of vague information and methods of shaping systems of notions leading to conceptualization and representation of vague knowledge, so also systems of their scopes as some vague sets. This paper outlines some direction of searching for a solution to this problem. In the paper, in connection to the notion of the rough set, the notion of a vague set is introduced. Some operations on these sets and their properties are discussed. The considerations intend to take into account a classical approach to reasoning, based on vague premises, and suggest finding a logic of vague sentences as a non-classical logic in which all counterparts of tautologies of classical logic are laws. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of the Acoustic Event Detection (AED) and Classification (AEC) evaluations carried out in February 2006 by the three participant partners from the CHIL project. The primary evaluation task was AED of the testing portions of the isolated sound databases and seminar recordings produced in CHIL. Additionally, a secondary AEC evaluation task was designed using only the isolated sound databases. The set of meeting-room acoustic event classes and the metrics were agreed by the three partners and ELDA was in charge of the scoring task. In this paper, the various systems for the tasks of AED and AEC and their results are presented. [ABSTRACT FROM AUTHOR]

Hypothesis testing is an important way to detect the statistical difference between two populations. In this paper, we use the Fisher permutation and bootstrap tests to differentiate hippocampal shape between genders. These methods are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. An efficient algorithm is adopted to rapidly perform the exact tests. We extend this algorithm to multivariate data by projecting the original data onto an "informative direction" to generate a scalar test statistic. This "informative direction" is found to preserve the original discriminative information. This direction is further used in this paper to isolate the discriminative shape difference between classes from the individual variability, achieving a visualization of shape discrepancy. [ABSTRACT FROM AUTHOR]

The active functions of aDBS (active Database System) are currently achieved by ECA rule system. But it is difficult for EC A rules system to be analyzed and validated. And there is no perfect model today. Based on the Petri Net, This paper thinks over the character of rules system, and establishes a system model. And some behaviors are analyzed in this paper. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter's information alone being observable. [ABSTRACT FROM AUTHOR]

Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based asynchronous BCI for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of motor imagery based asynchronous BCI can be effectively investigated. This paper also proposes an online training method, attempting to automate the process of finding the optimal parameter values of the BCI system to deal with non-stationary EEG signals. Experimental results have shown that the proposed methodfor online training of asynchronous BCI significantly improves the performance. [ABSTRACT FROM AUTHOR]

With the increasing popularity of self-organized communication systems, distributed trust and reputation systems in particular have received increasing attention. By formalizing trust relationships, trust between parties within the community can be derived by analyzing the trust paths linking the parties together. This paper treats trust network as an emergent property. The emergence interpretation concerns both the maintenance and usage of trust network inference. Specifically, in P2P system, trust network is maintained by individual agents at micro level, and it is used (i.e., queried) as one entity at macro level. In this paper, we first discuss micro level activities, that is, we classifies trust into functional trust and referral trust to facilitate trust inference, and extend the referral trust to include factors of similarity and truthfulness, so that our approach can accommodate the personalized feature of reputation rating, and reduce trust inference error effectively; then we discuss macro level properties of trust network. Specifically, we investigate the emergence of network structural properties of trust and reputation system in terms of efficiency and cost. That is, efficiency measures how well information propagates over trust system, and cost measures how expensive it is to build this system. Preliminary simulation results show the performance improvement of P2P community and the emergence of economical small-world trust network, namely relatively high efficiency and low cost. [ABSTRACT FROM AUTHOR]

It has been shown that various geometric vision problems such as triangulation and pose estimation can be solved optimally by minimizing L ∞  error norm. This paper proposes a novel algorithm for sequential estimation. When a measurement is given at a time instance, applying the original batch bi-section algorithm is very much inefficient because the number of seocnd order constraints increases as time goes on and hence the computational cost increases accordingly. This paper shows that, the upper and lower bounds, which are two input parameters of the bi-section method, can be updated through the time sequence so that the gap between the two bounds is kept as small as possible. Furthermore, we may use only a subset of all the given measurements for the L ∞  estimation. This reduces the number of constraints drastically. Finally, we do not have to re-estimate the parameter when the reprojection error of the measurement is smaller than the estimation error. These three provide a very fast L ∞  estimation through the sequence; our method is suitable for real-time or on-line sequential processing under L ∞  optimality. This paper particularly focuses on the triangulation problem, but the algorithm is general enough to be applied to any L ∞  problems. [ABSTRACT FROM AUTHOR]

In this paper the Vehicle Ad Hoc Network (VANET) is examined. As widely agreed VANETs must rely heavily on node-to-node communication while ease of access has to be assured at the same time. Beside the performance ensuring data authentication is another concern in VANETs. In our paper we focus on security aspects for the VANETs that aim to ensure data authentication and allow to ensure data secrecy. Ensuring data secrecy is not a standard feature of the VANETs since usually it is assumed that nodes in the network has to be able to receive all information send over the channel. However we think that it is necessary to ensure security against data eavesdrop and message forgery in some applications (e.g. police pursuits or military convoys). [ABSTRACT FROM AUTHOR]

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generalization problem is a key problem in NN society, which can be grouped into two classes: the generalization problem with unlimited size of training sample and that with limited size of training sample. The generalization problem with limited size of training sample is considered in this paper. Similar to margin maximization criterion in SVM, we propose a margin maximization training algorithm for BP network to further improve the generalization ability of BP network. Experimental results show that the margin maximization training algorithm proposed in this paper does improve the performance of BP network, and shows a comparable performance with SVM. [ABSTRACT FROM AUTHOR]

Chaotic noise cancellation has potential application in both secret communication and radar target identification. To solve the problem of parameter estimation in chaotic noise, a novel radial basis function neural network (RBF-NN) -based chaotic time series data modeling method is presented in this paper. Together with the spectral analysis technique, the algorithm combines neural network's ability to approximate any nonlinear function. Based on the flexibility of RBF-NN predictor and classical amplitude spectral analysis technique, this paper proposes a new algorithm for parameter estimation in chaotic noise. Analysis of the proposed algorithm's principle and simulation experiments results are given out, which show the effective of the proposed method. We conclude that the study has potential application in various fields as in secret communication for narrow band interference rejection or attenuation and in radar signal processing for weak target detection and identification in sea clutter. [ABSTRACT FROM AUTHOR]

The sub-block-based image retrieval method utilizes global and local image features to retrieve trademarks through separating individual an images into some blocks. This paper proposes a sub-block-based trademark image retrieval algorithm under the polar coordinate system. Experiment results show that our algorithm can keep excellent invariance in image translation, scaling and rotation and the retrieved results can satisfy human visual perception very well. [ABSTRACT FROM AUTHOR]

We contribute two new algorithms in this paper called PImGA and PIrlEA respectively in which we construct populations online in each iteration. Every iteration process in these two algorithms does not like the normal EA and GA in which they employ the inefficient value iteration method in general, instead of, in this paper, we employ the efficient policy iteration as the computation method for searching optimal control actions or policies. Meanwhile,these algorithms also do not like general EA and GA for selection operator to get a optimal policy, instead of we make the Agent learning a good or elite policy from its parents population. The resulted policy will be as one of elements of the next population. Because this policy is obtained by taking optimal reinforcement learning algorithm and greedy policy, the new population always can be constructed by applying better policies than its parents, that is to say, the child or offspring will inherit parents' good or elite abilities. Intuitively, for a finite problem, the resulted population from simulation will accommodate the near optimal policies after a number of iterations. Our experiments show that the algorithms can work well. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) is a stochastic evolutionary computation technique based on the behavior of swarms that can be used to optimize objects with complex search spaces. However, it has been observed that its performance varies duo to the dimensionality of the object and the location of the global optimum in the search space. This paper introduces a "random" velocity boundary condition to address the problem, where the velocity boundary alters randomly to prevent the velocity of a particle from stopping on a same boundary during the evolution. Simulation results on two benchmark functions with 30 and 300 dimensionalities and three types of locations of the global optimum solutions in the search spaces have shown that with the proposed "random" velocity boundary condition, a highly competitive optimization performance can be obtained for PSO regardless of the dimensionality and the location of the global optimum solution. [ABSTRACT FROM AUTHOR]

Many function-approaching methods such as neural network, fuzzy method are used in reinforcement learning methods for solving its huge problem space dimensions. This paper presents a novel ART2 neural network based reinforcement learning method (ART2-RL) to solve the space problem. Because of its adaptive resonance characteristic, ART2 neural network is used to process the space measurement of reinforcement learning and improve the learning speed. This paper also gives the reinforcement learning algorithm based on ART2. A simulation of path planning of mobile robot has been developed to prove the validity of ART2-RL. As the complexity of the simulation increased, the result shows that the number of collision between robot and obstacles is effectively decreased; the novel neural network model provides significant improvement in the space measurement of reinforcement learning. [ABSTRACT FROM AUTHOR]

This paper shows an agent based approach to solve the Tragedy of the Commons. This game is one of the well-known problems that involve sharing limited common resources. In this paper, to control the usage of common resources, we employ Levy Based Control Strategy and extend it with Autonomous Role Selection. In this approach, what levy plan of meta-agents should be used is very important to avoid the tragedy situation. Accordingly, we apply Genetic Algorithm (GA) to each agent to obtain evolutionally suitable levy plan. As a result, we show effectiveness of this approach and it depends on a fitness function of GA. Therefore, we examine the various fitness functions considered for a social balance and the importance of agents' role by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a new persistent storage to efficiently manage OWL Web ontologies. The Semantic Web is recognized as a next direction for progress of the current Web. To realize the Semantic Web, various technologies have been developed. Especially, OWL (Web Ontology Language) is the most important and state-of-the-art technology toward the ideal Semantic Web. However, most of research is being focused on designing and building OWL documents (OWL Ontology). One of the most important issues is how to efficiently and persistently store a very large OWL data into database management systems. In this paper, we propose a persistent storage system to achieve the purpose. This paper describes the performance evaluation result on the load time. The experiment result explicitly shows that our proposal provides more enhanced performance comparing with the existing systems. [ABSTRACT FROM AUTHOR]

This paper presents a middleware based on SOA (Service-Oriented Architecture) for context-awareness in a home network with ubiquitous computing. The context-awareness middleware addresses design issues such as heterogeneity, dynamicity, and extensibility issues of ubiquitous computing environment. We also suggest a context model supporting semantic level interoperability of context. In this paper, first we model context metadata ontology as well as context information ontology for the context-awareness middleware. Then we explain a service-oriented context-awareness middleware based on the context model which is introduced in this paper. [ABSTRACT FROM AUTHOR]

One of the particulars to be considered for effective servicing of wireless Internet to mobile terminal is the information of terminal environment. It is because time cost is required in producing mobile contents to suit environment of the connected terminal. In other words, in order to provide mobile web service by considering terminal characteristics, methods to minimize response time and server capacity in accordance with transcoding time in server are required. In this paper, response time to occur in the course of mobile web contents servicing was analyzed and, as a solution for this, Pre-Service method was introduced. Also, this paper suggests two methods in Pre-Service process for selecting optimize content in Cache. One is Device Profile Aware Selection using mobile device information. The other is Content Aware Selection using component information of requested content. Performance level of each method proposed was compared through experiment and the result of analysis was described. [ABSTRACT FROM AUTHOR]

Now, the study of UWB signal's propagation attenuation model concentrates mainly on residential indoor environment. In this paper, the monitor of the personnel and equipments in tunnel of coal mine is regarded as application background, the environment in coal mine is looked as a kind of special indoor environment. On the basis of residential indoor UWB propagation model, the tunnel of coal mine is divided into near-field region and far-field region according to Fresnel theory. The propagation mode of UWB signal is mainly multimode in near-field region and mainly waveguide in far-field region. Considering the additional ullage factors that caused by the factors of tunnel, such as personnel and coal transports and slicers etc at the same time, this paper puts forward the UWB signal propagation attenuation model in coal mine. Aiming at the parameter of symbol BER, comparison and analysis that based on the existing residential indoor propagation attenuation model and the model of this paper is done. [ABSTRACT FROM AUTHOR]