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This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

This paper investigates a production planning problem in which the inventory is deteriorating at a constant rate, demand is price dependent and back-order is allowed. It is assumed in most previous works that back-ordering customers may not cancel their orders. However, in reality, many customers may withdraw or cancel their orders before receiving their orders. Tacking the cancellation phenomenon into account, this paper develops a continuous-time model to simultaneously determine the production decision and the selling price. A simple algorithm is used to obtain the optimal solutions. Numerical examples are also used to illustrate the solution searching procedure and the characteristics of the optimal decisions. [ABSTRACT FROM AUTHOR]

In this paper, we handle a negotiation method in which a main negotiation consists of multiple sub-negotiations. In items allocation for commerce, there are some risks on the trade because the market balance is determined by supply and demand. The result of main negotiation is also determined by the order of sub-negotiations and agents' behaviors since agents' budgets have limitations on the actual commercial trading. However, it is difficult to decide the order of negotiations, such as simultaneous decisions and rotations. In this paper, we give the trading model in such cases, that is, agents purchase items by pooled buying. In actual trading as pooled buying, items are sold by the volume discount. Concretely, we discuss joint-stock company and private limited partnership on the web. In the negotiation phase, an agent proposes pooled buying based on the number of items and their prices considering their budgets. The degree of risks is calculated. All agents can see the risks with each item. Agents cooperate to the proposing agent based on the degree of risks. In this paper, we give two scenarios for trading. One is to avoid free riders who get surplus without risks. Another one is to promote agents' participation to increase social surplus. For risk aversion and promoting cooperation, we employ the side-payment policy, that is, cooperative agents' risks are preserved to a minimum. Further, we give some discussions where agents must pay the negotiation costs and charge for storage. [ABSTRACT FROM AUTHOR]

This paper presents a new philosophy to model the behavior of a Student in a Tutorial-like system using Learning Automata (LA). The model of the Student in our system is inferred using a higher-level of LA, referred to Meta-LA, which attempt to characterize the learning model of the Students (or Student Simulators), while the latter use the Tutorial-like system. To our knowledge, this is the first published result which attempts to infer the learning model of an LA when it is treated externally as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of Multi-Automata systems, where the Meta-LA communicate with the Students, also modelled using LA, in a synchronous scheme of interconnecting automata. [ABSTRACT FROM AUTHOR]

A novel neural filter and its application to active control of nonlinear noise are proposed in this paper. This method helps to avoid the premature saturation of backpropagation algorithm; meanwhile, guarantees the system convergence by the proposed self-tuning method. The comparison between the conventional filtered-X least-mean-square (FXLMS) algorithm and proposed method for nonlinear broadband noise in active noise cancellation (ANC) system is also made in this paper. The proposed design method is very easy to be implemented and versatile to the other applications. Several simulation results show that the proposed method can effectively cancel the narrowband and nonlinear broadband noise in a duct. [ABSTRACT FROM AUTHOR]

Performance of a genetic algorithm for function optimization, often appeared in real-world applications, depends on its crossover operator strongly. Existing crossover operators are designed for intensive search in certain promising regions. This paper, first, discusses where the promising search regions are on the basis of some assumptions about the fitness landscapes of objective functions and those about a state of a population, and this discussion reveals that existing crossover operators intensively search some of the promising regions but not all of them. Then, this paper designs a new crossover operator for searching all of the promising regions. For utilizing the advantageous features of this crossover operator, a new selection model considering characteristic preservation is also introduced. Several experiments have shown the proposed method has worked effectively on various test functions. [ABSTRACT FROM AUTHOR]

Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN. [ABSTRACT FROM AUTHOR]

This paper proposes a context-based image retrieval system based on color, area, and perimeter intercepted lengths of segmented objects in an image. It characterizes the shape of an object by its area and the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center, and the object color by its mean and standard deviation (STD). Recently, we reported that the color-shape based method (CSBM) is better than conventional color histogram (CCH) and fuzzy color histogram (FCH) in retrieving computer-generated images. However, its performance is only fair in the retrieval of natural images. For CSBM, object color is treated as uniform by reducing the number of colors in an image to only 27 colors. In this paper, we improve the performance by representing the color features of an object with its mean and STD. During the image retrieval stage, t-value is calculated based on the color features of two images, one in the query and the other in the database. The result shows that the proposed method achieves better performance in retrieving natural images compared to CCH, FCH, and CSBM. In the future, the proposed technique will be applied for the retrieval of digitized museum artifacts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a device for testing the human ability to utilise, even unconsciously, beneficial environmental features. An important characteristic of the proposed testing device is that additional stimuli, correlated to the correct task outcome, are introduced during the test without informing the subject. The device tests whether the subject will notice the subtly provided stimuli and use them, consciously or not, to improve the task performance. The use of these additional stimuli is confirmed by observing a sudden drop in the subject's performance (slump-like effect) when the additional stimuli are reversed, i.e., correlate to the wrong task outcome. We present several facts suggesting that the ability tested by the proposed device supports the human skill acquisition. An illustrative example of a specific implementation of the proposed device, based on a visual alternative choice task with additional audio stimuli, is presented to explain the testing process. [ABSTRACT FROM AUTHOR]

Aesthetics of townscapes have been a main factor in urban development. This paper introduces IroKage, an intelligent colour planning support system. The system offers improved colour schemes for existing townscapes based on three elements: colour harmony, impressions of the townscape, and cost for the change of colours. The system is constructed using an evolutionary algorithm and the Kansei engineering approach. After the construction, system evaluation is conducted. The subjects evaluate fifteen colour schemes output from the system in terms of colour harmony and the ideal impressions for the townscapes using the semantic differential method introduced by Osgood et al. The results of the evaluation demonstrate that the system has sufficient ability to propose appropriate colour schemes for the ideal town impressions. [ABSTRACT FROM AUTHOR]

Though IDS (Intrusion Detection System) have been used for many years, the large number of returned alert messages leads to management inefficiencies. In this paper, we propose a novel method based on SVM (Support Vector Machines) with a voting weight schema to detect intrusion. First, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and entropy features are extracted from processes. Next, these three features are sent to the SVM model for learning and then for testing. We then use a general voting schema and a voting weight schema to test attack detection rate, false positive rate and accuracy. Preliminary results show the SVM with a voting weight schema combines low the false positive rates and high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the network expanded problem (NEP) which optimally assigns new adding and splitting cells in PCS (Personal Communication Service) network to switches in an ATM (Asynchronous Transfer Mode) network is studied. In NEP, the locations of cells (or Base Stations, BSs) in PCS network are fixed and known, but new switches should be installed to ATM network and the topology of the backbone network may be changed.Given some potential sites of new switches, the problem is to determine how many switches should be added to the backbone network, the locations of new switches, the topology of the new backbone network, and the assignments of new adding and splitting cells in the PCS to switches of the new ATM backbone network in an optimal manner. The goal is to do the expansion in an attempt to minimize the total communication cost under budget and capacity constraints. The NEP is modelled as a complex integer programming problem. Since finding an optimal solution to this problem is Impractical. A simulated annealing (SA) algorithm is proposed to solve this problem. In the proposed SA, several heuristics are encoded into the perturbation to generate good solutions. Experimental results indicate that the proposed simulated annealing algorithm can get better performance than heuristic algorithm. [ABSTRACT FROM AUTHOR]

Steelmaking-continuous casting rescheduling problem is an NP-hard problem. In this paper, we present a constraint-based approach for steelmaking-continuous casting rescheduling problem in integrated steel production environment. We treat the steelmaking-continuous casting rescheduling problem as a dynamic constraint satisfaction problem. To maintain the production stability and the material flow continuity after rescheduling, the perturbation measure function of the model is defined as the summation of the absolute deviation between the new start times and the original start times of operations in continuous casting stage, the variable selection and value selection heuristics is designed based on the resource slackness to minimize the change extent of the rescheduling solution. The resource conflicts in the original schedule are incrementally repaired by the variable selection and value selection heuristics. The validity of the proposed model and approach are demonstrated by the computational examples. [ABSTRACT FROM AUTHOR]

The agent-based systems have been designed and developed using the latest agent technologies. However, the design and the debugging of these systems contain some difficult problems due to the situational and nondeterministic nature of the agents, and the effective design-supporting technologies have not been proposed so far. In order to make efficient design process of agent system, we propose an interactive development method of agent system based on the agent-repository-based multiagent framework which focuses on an essential feature of agent system design, i.e., the reuse of existing agents stored in the agent repository. In this paper, we propose an Interactive Design Environment of Agent system called IDEA and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new artificial intelligence framework that solves knapsack related problems (a class of complex combinatorial optimization problems). This framework, which is pseudo-parallel and stochastic, uses an agent based system to approximately solve the optimization problem. The system consists of active agents interacting in real time. They mimic the behavior of the parameters of the optimization problem while being individually driven by their own parameters, decision process, and fitness assessment. The application of the framework to the two-dimensional guillotine bin packing problem demonstrates its effectiveness both in terms of solution quality and run time. [ABSTRACT FROM AUTHOR]

The growing complexity of communication networks and their associated information overhead have made network management considerably difficult. This paper presents a novel Network Management Scheme based on the novel concept of Active Information Resources (AIRs). Many types of information are distributed in the complex network, and they are changed dynamically. Under the AIR scheme, each piece of information in a network is activated as an intelligent agent: an I-AIR. An I-AIR has knowledge and functionality related to its information. The I-AIRs autonomously detect run-time operational obstacles occurring in the network system and specify the failures' causes to the network administrator with their cooperation. Thereby, some network management tasks are supported. The proposed prototype system (AIR-NMS) was implemented. Experimental results indicate that it markedly reduces the network administrator workload, compared to conventional network management methods. [ABSTRACT FROM AUTHOR]

Although combinatorial auction has been studied extensively, it is difficult to apply the existing results to a problem with minimal resource requirements. In this paper, we consider a combinatorial auction problem in which an auctioneer wants to acquire resources from a set of bidders to process the tasks on hand. Each task requires a minimal set of resources for executing the operations. Each bidder owns a set of resources to bid for the tasks. The problem is to determine the resource assignment to minimize the total cost to perform the tasks. The main results include: (1) a problem formulation for combinatorial auction with minimal resource requirements; (2) a solution methodology based on Lagrangian relaxation; (3) an economic interpretation and a proposed structure for implementing our solution algorithms. [ABSTRACT FROM AUTHOR]

A hybrid recommendation method is presented in this paper. Its main goal is to improve recommendation recall maintaining high recommendation precision and adaptive ability. The formal model is used to define the method and to analyze how the measures known from traditional Information Retrieval may be adapted to recommendation. The presented theorems show that the method is able to adapt to changing user's needs and achieve the maximal effectiveness if the component methods work properly. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) technique has been widely used in recommending items of interest to users based on social relationships. The notion of trust is emerging as an important facet of relationships in social networks. In this paper, we present an improved mechanism to the standard CF techniques by incorporating trust into CF recommendation process. We derive the trust score directly from the user rating data and exploit the trust propagation in the trust web. The overall performance of our trust-based recommender system is presented and favorably compared to other approaches. [ABSTRACT FROM AUTHOR]

This paper presented an image reconstruction method for a capsule endoscope. The proposed method constructs a 3-D model of the intestine using massive images obtained from the capsule endoscope. It merges all images and yields a complete 3-D model of the intestine. This 3-D model is reformed as a 2-D plane image showing the inner side of the entire intestine. The proposed image composition has been evaluated using the OpenGL 3-D simulator. The composite image provides an easy-to-understand view for examining the intestine. In addition, it provides fast track-and-check diagnosis using the3-D model implementation. [ABSTRACT FROM AUTHOR]

This paper presents a technique for dynamically determining search spaces in order to enable sensor exploration during autonomous search and tracking (SAT) missions. In particular, marine search and rescue scenarios are considered, highlighting the need for exploration during SAT. A comprehensive method which is independent of search space representation is introduced, based on exploration frontiers and reachable set analysis. The advantage of the technique is that recursive Bayesian estimation can be performed indefinitely, without loss of information. Numerical results involving multiple search vehicles and multiple targets demonstrate the efficacy of the approach for coordinated SAT. These examples also highlight the added benefit for human mission planners resulting from the technique's simplification of the search space allocation task. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic programming algorithm to draw optimal intermodal freight routing with regard to international logistics of container cargo for export and import. This study looks into the characteristics of intermodal transport using two or more modes, and presents a Weighted Constrained Shortest Path Problem (WCSPP) model. This study draws a Pareto optimal solution that can simultaneously meet two objective functions by applying the Label Setting algorithm, one of the Dynamic Programming algorithms, after setting feasible area using the objective function values drawn in the model. To improve the algorithm performance, pruning rules have also been presented. The algorithm is applied to real transport paths from Busan to Rotterdam. This study quantitatively measures the savings effect of transport cost and time by comparing single transport modes with existing intermodal transport paths. Lastly, this study applies the multiple Pareto optimal solutions drawn, to a mathematical model and MADM model, and compares the two evaluation methods as a means to evaluate the solutions. [ABSTRACT FROM AUTHOR]

Reduced ordered binary decision diagrams (ROBDDs) have become widely used for CAD applications such as logic synthesis, formal verification, and etc. The size of RDBDDs for a Boolean function is very sensitive to the ordering choices of input variables and the problem of finding a minimum-size variable ordering is known to be NP-complete. In this paper, we propose a new ROBDD minimization algorithm based on the microcanonical optimization (MO). MO iteratively applies an initialization phase and a sampling phase to combinatorial optimization problems. In the proposed MO-based algorithm, the initialization phase is replaced with the existing Sifting algorithm known to be a very fast local search algorithm to find a minimum-size ROBDD. We derived equations for the proposed MO-based algorithm parameters empirically. The algorithm has been experimented on well known benchmark circuits and the experiments show that, even with slightly better solutions, the run time of the algorithm is 24% and 48% of the Genetic and SA algorithms', respectively, on average. The proposed MO-based algorithm is a good candidate for the large size problems that cannot be attacked by exact algorithms when a near-optimal solution is required. [ABSTRACT FROM AUTHOR]

This paper proposes a new theory for forming a maximum-value-cooperation coalition known as the Maximal Social Welfare Feasible Coalition. This theory can give such solution because it does not assume that each player requesting to join a coalition knows information of other players. However, all players' private information requesting to join the coalition is known by an honest coordinator. This allows the coordinator to select a coalition structure with maximal value of cooperation among successful players so as they get at least at their required minimum values. Not only this maximal value is shown to be equal to or larger than the value of a core coalition but also the value allocation is shown to be Pareto optimal. [ABSTRACT FROM AUTHOR]

In this paper, the performance influencing class analysis (PI-CA) framework is proposed for performance analysis of pattern recognition systems dealing with data with great variety and diversity. Through the PICA procedure, the population of data is divided into subsets on which the system achieves different performances by means of statistical methods. On basis of the division, performance assessment and analysis are conducted to estimate the system performance on the whole data population. The PICA framework can predict true performance in real application and facilitate comparison of different systems without the same test set. The PICA framework is applied to the analysis of a broadcast news segmentation system. The procedure is presented and experimental results were given, which verified the effectiveness of PICA. [ABSTRACT FROM AUTHOR]

This paper proposes an improved voice activity detection (VAD) algorithm for controlling discontinuous transmission (DTX) of the GSM adaptive multi-rate (AMR) speech codec. First, based on the wavelet transform, the original IIR filter bank and the open-loop pitch detector are implemented via the wavelet filter bank and the wavelet-based pitch detection algorithm, respectively. The proposed wavelet filter bank divides the input speech signal into 9 frequency bands so that the signal level at each sub-band can be calculated. In addition, the background noise can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then one can apply support vector machine (SVM) to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database show that the proposed algorithm gives considerable VAD performances superior to the AMR VAD Option 1 and comparable with the AMR VAD Option 2. [ABSTRACT FROM AUTHOR]

Automatic speech recognition for real-world applications such as a robot should deal with speech under noisy environments. This paper presents coarse phoneme recognition which uses a phoneme group instead of a phoneme as a unit of speech recognition for such a real-world application. In coarse phoneme recognition, the design of the phoneme group is crucial. We, thus, introduce two types of phoneme groups - exclusive and overlapping phoneme groups, and evaluate coarse phoneme recognition with these two phoneme grouping methods under various kinds of noise conditions. The experimental results show that our proposed overlapping phoneme grouping improves the correct phoneme inclusion rate by 20 points on average. [ABSTRACT FROM AUTHOR]

Social networks have been working as a medium to provide cooperative interactions between people. However, as some of users take malicious actions, the social network potentially contains some risks (e.g., information distortion). In this paper, we propose a robust information diffusion (or propagation) model to detect malicious peers on social network. Especially, we apply statistical sequence analysis to discover a peculiar patterns on recommendation flows. Through two experimentation, we evaluated the performance of risk discovery on social network. [ABSTRACT FROM AUTHOR]

Considering the interpretability of association classifier, and high classification accuracy of SVM, in this paper, we propose ACIK, an association classifier built with help of SVM, so that the classifier has an interpretable classification model, and has excellent classification accuracy. We also present a novel family of Boolean kernel, namely itemset kernel. ACIK, which takes SVM as learning engine, mines interesting association rules for construct itemset kernels, and then mines the classification weight of these rules from the classification hyperplane constructed by SVM. Experiment results on UCI dataset show that ACIK outperforms some state-of-art classifiers, such as CMAR, CPAR, L3, DeEPs, linear SVM, and so on. [ABSTRACT FROM AUTHOR]

In mining data streams, one of the most challenging tasks is adapting to concept change, that is change over time of the underlying concept in the data. In this paper, we propose a novel ensemble framework for mining concept-changing data streams. This algorithm, called QACC (Quick Adaptation to Changing Concepts), realizes quick adaptation to changing concepts using an ensemble of classifiers. For quick adaptation, QACC sensitively detects concept changes in noisy streaming data. Empirical studies show that the QACC algorithm is efficient for various concept changes. [ABSTRACT FROM AUTHOR]

This paper reports the first known solution to the Stochastic Point Location (SPL) problem when the Environment is non-stationary. The SPL problem [12,13,14] involves a general learning problem in which the learning mechanism attempts to learn a "parameter", say λ*, within a closed interval. However, unlike the earlier reported results, we consider the scenario when the learning is to be done in a non-stationary setting. The Environment communicates with an intermediate entity (referred to as the Teacher) about the point itself, advising it where it should go. The mechanism searching for the point, in turn, receives responses from the Teacher, directing it how it should move. Therefore, the point itself, in the overall setting, is moving, delivering possibly incorrect information about its location to the Teacher. This, in turn, means that the "Environment" is itself non-stationary, implying that the advice of the Teacher is both uncertain and changing with time - rendering the problem extremely fascinating. The heart of the strategy we propose involves discretizing the space and performing a controlled random walk on this space. Apart from deriving some analytic results about our solution, we also report simulation results which demonstrate the power of the scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach Immune Algorithm (IA) is applied to improve the efficiency of composing near optimal test sheet from item banks to meet multiple assessment criteria. We compare the results of immune and Genetic Algorithm (GA) to compose test-sheets for multiple assessment criteria. From the experimental results, the IA approach is desirable in composing near optimal test-sheet from large item banks. And objective conceptual vector (OCV) and objective test-sheet test item numbers (M) can be effectually achieved. Hence it can support the needs of precisely evaluating student's learning status. We successfully extend the applications of artificial intelligent - Immune to the educational measurement. [ABSTRACT FROM AUTHOR]

Classification problem asks to construct a classifier from a given data set, where a classifier is required to capture the hidden oracle of the data space. Recently, we introduced a new class of classifiers ICF, which is based on iteratively composed features on {0,1, ∗ }-valued data sets. We proposed an algorithm ALG-ICF ∗  to construct an ICF classifier and showed its high performance. In this paper, we extend ICF so that it can also process real world data sets consisting of numerical and/or categorical attributes. For this purpose, we incorporate a discretization scheme into ALG-ICF ∗  as its preprocessor, by which an input real world data set is transformed into {0,1, ∗ }-valued one. Based on the experimental studies on conventional discretization schemes, we propose a new discretization scheme, integrated construction (IC). Our computational experiments reveal that the ALG-ICF ∗  equipped with IC outperforms a decision tree constructor C4.5 in many cases. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a very promising classification technique developed by Vapnik. However, there are still some shortcomings in the original SVM approach. First, SVM was originally designed for binary classification. How to extend it effectively for multi-class classification is still an on-going research issue. Second, SVM does not consider the distribution of each class. In this paper, we propose an extension to the SVM method of pattern recognition for solving the multi-class problem in one formal step. Contrast to previous multi-class SVMs, our approach considers the distribution of each class. Experimental results show that the proposed method is more suitable for practical use than other multi-class SVMs, especially for unbalanced datasets. [ABSTRACT FROM AUTHOR]

Fuzzy interpolative reasoning is an important research topic of sparse fuzzy rule-based systems. In recent years, some methods have been presented for dealing with fuzzy interpolative reasoning. However, the involving fuzzy sets appearing in the antecedents of fuzzy rules of the existing fuzzy interpolative reasoning methods must be normal and non-overlapping. Moreover, the reasoning conclusions of the existing fuzzy interpolative reasoning methods sometimes become abnormal fuzzy sets. In this paper, in order to overcome the drawbacks of the existing fuzzy interpolative reasoning methods, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed fuzzy interpolative reasoning method can handle the situation of non-normal and overlapping fuzzy sets appearing in the antecedents of fuzzy rules. It can overcome the drawbacks of the existing fuzzy interpolative reasoning methods in sparse fuzzy rule-based systems. [ABSTRACT FROM AUTHOR]

Forecasting activities are frequent and widespread in our life. Since Song and Chissom proposed the fuzzy time series in 1993, many previous studies have proposed variant fuzzy time series models to deal with uncertain and vague data. A drawback of these models is that they do not consider appropriately the weights of fuzzy relations. This paper proposes a new method to build weighted fuzzy rules by computing cardinality of each fuzzy relation to solve above problems. The proposed method is able to build the weighted fuzzy rules based on concept of large itemsets of Apriori. The yearly data on enrollments at the University of Alabama are adopted to verify and evaluate the performance of the proposed method. The forecasting accuracies of the proposed method are better than other methods. [ABSTRACT FROM AUTHOR]

The process scheduling aims to arrange CPU time to multiple processes for providing users with more efficient throughput. Except the class of process set by user, conventional operating systems have applied the equivalent scheduling policy to every process. Moreover, if the scheduling policy is once determined, it is unable to change without resetting the operating system which takes much time. In this paper, we propose an intelligent CPU process scheduling algorithm using fuzzy inference with user models. It classifies processes into three classes, batch, interactive and real-time processes, and models user's preferences to each process class. Finally, it assigns the priority of each process according to the class of the process and user's preference through the fuzzy inference. The experimental result shows the proposed method can adapt to user and allow different scheduling policies to multiple users. [ABSTRACT FROM AUTHOR]

Selecting faculty members to form a mission committee and simultaneously scheduling the corresponding committee meetings is a tough decision problem frequently encountered in every academic department. In this paper, we present a formal model of the problem. We also present an approach showing that, with simple database construction, the problem can be transformed into a constrained itemset mining problem, which is an important branch of frequent itemset mining. Thus, the problem can be exactly solved by techniques for constrained itemset mining. For high efficiency, we provide a method to convert some problem constraint into an anti-monotone constraint, which can be easily embedded into the framework of frequent itemset mining and is considered very effective for search space pruning. Experiments were performed and the results show that our approach offers very high performance. [ABSTRACT FROM AUTHOR]

The frequent pattern tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It, however, needed to process all transactions in a batch way. In the past, we proposed the Fast Updated FP-tree (FUFP-tree) structure to efficiently handle the newly inserted transactions in incremental mining. In this paper, we attempt to modify the FUFP-tree maintenance based on the concept of pre-large itemsets for efficiently handling deletion of records. Pre-large itemsets are defined by a lower support threshold and an upper support threshold. The proposed approach can thus achieve a good execution time for tree maintenance especially when each time a small number of records are deleted. Experimental results also show that the proposed Pre-FUFP deletion algorithm has a good performance for incrementally handling deleted records. [ABSTRACT FROM AUTHOR]

It is generally accepted that knowledge based systems would be smarter if they can deal with uncertainty. Some research has been done to extend Description Logics(DLs) towards the management of uncertainty, most of which concerned the statistical information such as "The probability that a randomly chosen bird flies is greater than 0.9". In this paper, we present a new kind of extended DLs to describe degrees of belief such as "The probability that all plastic objects float is 0.3". We also introduce the extended tableau algorithm for Pr$\mathcal {A}\mathcal {L}\mathcal {C}$ as an example to compute the probability of the implicit knowledge. [ABSTRACT FROM AUTHOR]

Previous works have proposed to apply model-based diagnosis (MBD) techniques to detect and locate faults in the control software of mobile autonomous robots at runtime. The localization of faults at the level of software components enables the autonomous repair of the system by restarting failed components. Unfortunately, classical MBD approaches assume that components fail independently. In this paper we show that dependent failures are very common in this application domain and we propose the concept of diagnosis environments (DEs) in order to tackle the arising problems. We provide an algorithm for the computation of DEs and present the results of case studies. [ABSTRACT FROM AUTHOR]

In this paper, it is shown how behavioural properties can be specified for three types of violent criminals. Moreover, it is shown how empirical material in the form of informal descriptions of traces of crime-related events can be formalised. Furthermore, it is shown how these formalised traces and behavioural properties can be used in automated analysis, for example in order to determine which type of criminal can have committed such a crime. Moreover, an underlying dynamical model is presented that shows causal mechanisms behind each of the behaviours, and their dependencies on the characteristics of the type of criminal and inputs in terms of stimuli from the environment. [ABSTRACT FROM AUTHOR]

A novel equalization scheme for 16 QAM signals through a wireless ATM communication channel using Reduced-Complex Bilinear Recurrent Neural Network (R-CBLRNN) is proposed in this paper. The 16 QAM signals from a wireless ATM communication channel have severe nonlinearity and intersymbol interference due to multiple propagation paths in the channel. The R-CBLRNN equalizer is compared with the conventional equalizers including a Volterra filter equalizer, a decision feedback equalizer (DFE), and a multilayer perceptron type neural network (MLPNN) equalizer. The results show that the R-CBLRNN equalizer for 16 QAM signals gives very favorable results in both of the Mean Square Error(MSE) and the Symbol Error Rate (SER) criteria over conventional equalizers. [ABSTRACT FROM AUTHOR]

Measuring structural similarity between XML documents has become a key component in various applications, including XML data mining, schema matching, web service discovery, among others. The paper presents a novel structural similarity measure between XML documents using kernel methods. Results on preliminary simulations show that this outperforms conventional ones. [ABSTRACT FROM AUTHOR]

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, We applied this neural network model to channel equalization. Simulations show that DLS outperforms ordinary least squares in channel equalization problems. [ABSTRACT FROM AUTHOR]

The probabilistic filtering method filters out an unpromising candidate solution by conducting a simple preliminary evaluation before a complete evaluation in order to improve the efficiency of a local search. In this paper, we improve probabilistic filtering so that it can be applied in general to large-scaled optimization problems. As compared to the previous probabilistic filtering method, our enhanced version includes a scaling and truncation function to increase the discriminating power of probabilistic filtering and repair some defects of the previous bias function in adjusting the level of greediness. Experiments have shown that our method is more effective in improving the performance of a local search than the previous method. It has also been shown that the probabilistic filtering can be effective even when the preliminary evaluation heuristic is somewhat inaccurate, and the lesser the cost of preliminary evaluation, the greater is its effectiveness. [ABSTRACT FROM AUTHOR]

Collaborative design has been recognized an alternative environment for product design in which multidisciplinary participants are naturally involving. Reuse of product design information has long been recognized as one of core requirements for efficient collaborative product design. This paper addresses integration of multi-modal data using a graph for an assembly design information sharing and reuse in the collaborative environment. In the system, assembly product images obtained from multi-modal devices are utilized to share and to reuse design information. The proposed system conducts the segmentation of an assembly product image by using a labeling method and generates an attribute relation graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the integration of multi-modal data has been realized to retrieve assembly design information using a product image. [ABSTRACT FROM AUTHOR]

This paper considers a fuzzy Lyapunov method for stability analysis of nonlinear systems subjected to external forces. The nonlinear systems under external forces can be represented by Tagagi-Sugeno (T-S) fuzzy model. In order to design a nonlinear fuzzy controller to stabilize this nonlinear system, the parallel distributed compensation (PDC) scheme is used to construct a global fuzzy logic controller. We then propose the robustness design to ensure the modeling error is bounded and some stability conditions are derived based on the controlled systems. Based on the stability criterion, the nonlinear systems with external forces are guaranteed to be stable. This control problem can be reformulated into linear matrix inequalities (LMI) problem. [ABSTRACT FROM AUTHOR]

With the explosive growth of data available on the Internet, a recent area of investigation called Web Mining has arise. In this paper, we will study general aspects of this area, principally the process of Web Usage Mining where log files are analyzed. These files register the activity of the user when interact with the Web. In the Web Usage Mining, different techniques of mining to discover usage patterns from web data can be applied. We will also study applications of Fuzzy Logic in this area. Specially, we analyze fuzzy techniques such as fuzzy association rules or fuzzy clustering, featuring their functionality and advantages when examining a data set of logs from a web server. Finally, we give initial traces about the application of Fuzzy Logic to personalization and user profile construction. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-agent control system incorporating hybrid intelligence and its physical testbed are presented. The physical testbed is equipped with a large number of embedded devices interconnected by three types of physical networks. It mimics a ubiquitous intelligent environment and allows real-time data collection and online system evaluation. Human control behaviours for different physical devices are analysed and classified into three categories. Physical devices are grouped based on their relevance and each group is assigned to a particular behaviour category. Each device group is independently modelled by either fuzzy inference or neural network agents according to the behaviour category. Comparative analysis shows that the proposed multi-agent control system with hybrid intelligence achieves significant improvement in control accuracy compared to other offline control systems. [ABSTRACT FROM AUTHOR]