Search

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

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

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

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

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

In this paper, a method of controlling packet transmission rate between nodes on an Ad-hoc network is proposed considering the characteristics of IEEE 802.11 possessing different transmission efficiencies by different transmission distances. There have been a lot of researches about algorithms for efficient routing and power saving using static power sources in the field of mobile Ad-hoc networks up until now. However, those researches have been conducted only on the assumption of ideal experimental cases. This paper considers the way of finding adequate transmission rate for the transmission distances between nodes on a mobile Ad-hoc networks so that a more realizable method is presented. In this research, a controlling algorithm for transmission data rates by the distances between mobile nodes is realized using Fuzzy logic, possibly available to be applied to Ad-hoc network routing. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network is an advanced technology that has a variety of applications such as environmental monitoring, battlefield, medical system, and crop precision. Minimizing power consumption of node is important in these networks. Transmit power control and clustering can reduce the energy consumption efficiently when nodes are non-homogeneously dispersed in space. This paper presents the clustering algorithm in wireless sensor networks. The clustering algorithm is based on the optimization of transmit power level by using the soft computing approaches. This solution determines the node transmit power level statistically and achieves energy savings efficiently. [ABSTRACT FROM AUTHOR]

In this paper, the statistical model of communication network delay based on the data measured from a network in operation is studied. In our study, it is found that modeling the round trip time (RTT) in our network by a single statistical model is not adequate. Therefore two combined statistical distributions, Pareto distribution and generalized exponential distribution, are used to develop our model. This approach is verified by the standard Chi-square test widely used by statisticians. Based on the model developed, a gain-scheduling algorithm to adjust controller gain to compensate time delay in a networked control system (NCS) is developed. This paper presents the results of our initial study. In addition, further research based on the design methodology pioneered in this paper is also proposed. [ABSTRACT FROM AUTHOR]

Numerical examples and some simple systems, for instance an inverted pendulum on a cart, are often used in the analytical and simulation study of Networked Control Systems (NCSs). In this paper, we propose to use a system of "Two inverted pendulums Coupled by a Spring" to extend the existing study to investigate: (1) asynchronised multi-rate sampling in a NCS, (2) random, distributed and discrete time delay in networked signal-transfer with possible loss of signals, (3) NCS for nonlinear systems, (4) Robust control of NCS with plant uncertainties and external disturbances, and (5) using NCS to implement controller structure beyond the limit of decentralised control. This system has been used, in the context of point-to-point connections, to study a number of control problems including a study on robust stabilisation of nonlinear systems via decentralised control. Therefore the performances of the system having a NCS structure can be compared with those having a traditional structure of fixed connections. The detailed model, some simulation results of the system under both of a traditional structure and a NCS structure, and proposed further studies are presented in the paper. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for designing a detection, isolation, and identification scheme for sensor faults in linear time-varying systems. Practically important is that the proposed methodology is constructed on the basis of historical data and does not require a priori information to isolate and identify sensor faults. This is achieved by identifying a state space model and designing a fault isolation and identification filter. To address time-varying process behavior, the state space model and fault reconstruction filter are updated using a two-time-scale approach. Fault identification takes place at a higher frequency than the adaptation of the monitoring scheme. To demonstrate the utility of the new scheme, the paper evaluates its performance using simulations of a LTI system and a chemical process with time-varying parameters and industrial data from a debutanizer and a melter process. [ABSTRACT FROM AUTHOR]

This paper proposes spatial outliers detection method of studying multiple non-spatial attributes based on special objects. The spatial outliers detection algorithm based on the Mahalanobis distance is proposed in this paper. The simulated experiment results demonstrate this method is feasible and effective, simultaneously the time complexity of center algorithm is analysed. Except the research type mentioned, spatial outliers detection still includes time-order data and time-space data outliers detections, which are related to the attribute values of other neighbors. [ABSTRACT FROM AUTHOR]

Inspection robot must detect the obstacles from the complex background according to their types when it is crawling along the power transmission line in order to negotiate reliably. In ideal cases, robot's vision system can give satisfactory results, however, motion blur due to camera motion caused by wind or other unknown causes can significantly degrade the quality of the image acquired. It is an undesired effect. In this paper, a complete motion deblurring procedure for obstacle images has been proposed, we try to analyze the running environment of the robot to develop the model of the motion blur. The acquired motion blur information is used to identify the point spread function (PSF) as well as restore the blurred image at the same time. Experiments on real blurred images on power transmission line prove the feasibility and reliability of this algorithm. [ABSTRACT FROM AUTHOR]

Random fuzzy theory is introduced to evaluate the option value of R&D project in this paper. Based on a new theoretical insight, we develop a random fuzzy jump amplitude model to price R&D option. In classic stochastic jump amplitude model, the value of R&D option depends on the expected number of jumps and the expected size of the jumps. However, in practice, it is so hard to get enough data that the distribution functions of them are difficult to be confirmed accurately. So random fuzzy theory can deal with these problems better. In this paper, the number of jumps and the size of jumps are taken as random fuzzy variables, and investment costs and future cash flows are depicted as fuzzy variables. New research tools for option pricing of R&D project are developed and the capability of dealing with practical problems is enhanced. [ABSTRACT FROM AUTHOR]

Nowadays technological devices can already be provided with enough intelligence to understand and act appropriately on voice commands. The voice communication with technological devices becomes a stronger challenge as technology becomes more advanced and complex. In this paper, a natural language interface is presented which consists of the intelligent mechanisms of human identification, speech recognition, word and command recognition, command syntax and result analysis, command safety assessment, technological process supervision as well as human reaction assessment. In this paper, a review is carried out of selected issues with regards to recognition of speech commands in natural language given by the operator of the technological device. A view is offered of the complexity of the recognition process of the operator's words and commands using neural networks made up of a few layers of neurons. The paper presents research results of speech recognition and automatic recognition of commands in natural language using artificial neural networks. [ABSTRACT FROM AUTHOR]

This paper presents a new SoC test scheduling method based on an ant algorithm. The proposed scheduling algorithm formulates the SoC test scheduling problem as a rectangle bin packing problem and uses ACO to cover more solution space to increase the probability of finding optimal solutions. The experimental results conducted using ITC '02 SoC benchmarks show that the proposed idea gives the test application time comparable to earlier researches in less calculation time. [ABSTRACT FROM AUTHOR]

In this paper, a new efficient hardware architecture and its implementation for an AntNet-based routing are proposed. This architecture is based on the modified AntNet algorithm, and optimized to the ant packets defined in this paper. The modified AntNet is evaluated by the performance comparisons with the original AntNet algorithm through C-level simulations, and then implemented into RTL design. Consequently, the hardware implementation result of the proposed architecture is described. [ABSTRACT FROM AUTHOR]

Recently semantic web is an important issue in many areas. Ontology specifies the knowledge in a specific domain and defines the concepts of knowledge and the relationships between concepts. To specify and define the knowledge in a specific domain, it is required to generate the ontology which conceptualizes the knowledge. Accordingly, to search the information of potential customers for home-delivery marketing of post office, we design a specific domain to generate the ontology in this paper. And we propose how to retrieve the information, using the generated ontology. We also implement the data search robot which collects the information based on the generated ontology. Therefore we confirm that the ontology and the search robot perform the information retrieval exactly. [ABSTRACT FROM AUTHOR]

In this paper, we study clustering with respect to the k-modes objective function, a natural formulation of clustering for categorical data. One of the main contributions of this paper is to establish the connection between k-modes and k-median, i.e., the optimum of k-median is at most the twice the optimum of k-modes for the same categorical data clustering problem. Based on this observation, we derive a deterministic algorithm that achieves an approximation factor of 2. Furthermore, we prove that the distance measure in k-modes defines a metric. Hence, we are able to extend existing approximation algorithms for metric k-median to k-modes. Empirical results verify the superiority of our method. [ABSTRACT FROM AUTHOR]

This paper presents a new Fuzzy Neural Network (FNN) model to evaluate design alternatives in conceptual design. In the proposed method, a fuzzy reasoning based on feedforward neural network is used to evaluate concepts, and a learning algorithm based on ranking-based adaptive evolutionary operator genetic algorithm (RAOGA) is utilized to adjust fuzzy weights and thresholds with fuzzy inputs and outputs in FNN. [ABSTRACT FROM AUTHOR]

Water quality management plays a very important role in fish life and aquatic product quality. The paper firstly selects the index system and confirm the weight of each of the water quality factors dependent on the fish tolerance to the factors of water quality integrated with the result of the expert questionnaire by the Delphi method with the Water Quality Standard, then constructs the fuzzy evaluation model of the multiplex water quality parameter and classifies the standard of water quality into five classes of standard. At last the paper give an experimental model based on the monitored datum in North China. It shows that it can evaluate the water quality integrative and provide the degree of membership that the water quality belongs to all the standards. [ABSTRACT FROM AUTHOR]

This paper considers a generalized fuzzy version of maximum flow problem in which arc capacities are fuzzy variables. The problem is to find a maximum flow under some chance constraints with respect to credibility measure of arc flow of network. Some crisp equivalents of fuzzy chance constraints are presented when the fuzzy capacities are characterized by trapezoidal fuzzy numbers and general fuzzy numbers respectively. Furthermore, a genetic algorithm is used to solve the maximum flow with these crisp equivalents. Finally, a numerical example is provided for the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we develop an energy and bandwidth efficient approach for target classification in sensor networks. Instead of adopting decision fusion to reduce network traffic as some recent research, we try to realize energy efficient target classification from a computational point of view. Our contribution is we propose a novel tree construction algorithm that autonomously organizes the distributed computation resources to execute the trained BP-network (BPN) in parallel manner. We evaluate the performance of our parallel computing paradigm compared to the traditional client/server-based computing paradigm from perspectives of energy consumption and communication traffic through analytical study. Finally, we take a target classification experiment to show the effectiveness of the proposed computing paradigm. [ABSTRACT FROM AUTHOR]

A good message scheduling algorithm could give timeliness guarantee to real-time control network. Based on DM algorithm, this paper presents an improved deadline-based algorithm, Deadline Monotonic with Urgent Message Considered (DMUMC), for FF control network message scheduling. The main idea of DMUMC is that deadline of urgent aperiodic message, such as alarms, was taken into account when establishing BAT. The simulation results show that DMUMC algorithm significantly reduces response time of urgent aperiodic message by adjusting service time of low priority periodic message with large deadline, and improves timeliness and message schedulability of FF control network, compared with traditional methods that didn't consider the issue of aperiodic message. [ABSTRACT FROM AUTHOR]

The focus of situation-aware ubiquitous computing has increased lately. An example of situation-aware applications is a multimedia education system. The development of multimedia computers and communication techniques has made it possible for a mind to be transmitted from a teacher to a student in distance environment. This paper proposes an Adaptive Fault Tolerance (AFT) algorithm in situation-aware middleware framework and presents its simulation model of AFT-based agents. FTE(Fault Tolerance Environment) provide several functions and features capable of developing multimedia distant education system among students and teachers during lecture. AFT is a system that is suitable for detecting and recovering software error based on distributed multimedia education environment as FTE by using software techniques. This method detects an error by using process database. When an error occurs, FTA(Fault Tolerance Agent) inspects it by using API(Application Program Interface) function for process database. If an error is found, FTA decides whether it is hardware error or software error. In case of software error, it can be recoverable. The purpose of AFT system is to maintain and recover for FTE session automatically. This paper proposes an Adaptive Fault Tolerance (AFT) algorithm in situation-aware middleware framework and presents its simulation model of AFT-based agents. [ABSTRACT FROM AUTHOR]

This paper proposes a new model of access control by analyzing the window and attributes of the object, and based on this, a mechanism that offers a seamless multimedia view without interfering with access control is also suggested. There are two approaches to software architecture on which applications for multimedia distance education environment in situation-aware middleware are based. Those include CACV(Centralized-Abstraction and Centralized-View) and RARV(Replicated-Abstraction and Replicated-View).To win over such dilemma for centralized or replicated architecture, a combined approach, CARV(the Centralized Abstraction and Replicated View) architecture is used to realize the application sharing agent. [ABSTRACT FROM AUTHOR]

A mobile ad hoc network (MANET) is an autonomous system of mobile nodes connected by wireless links. There is no static infrastructure such as base station in cell mobile communication. Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and multicast routing in MANET is a challenging task. Finding and maintaining QoS multicast routing in the data is still more challenging. In this paper, we present an entropy-based genetic algorithm (GA) to support QoS multicast routing in mobile ad hoc networks (EQMGA). The key idea of EQMGA algorithm is to construct the new metric-entropy and select the long-life path with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network. The simulation results demonstrate that the proposed approach and parameters provide an accurate and efficient method to estimate and evaluate the route stability in dynamic mobile networks. [ABSTRACT FROM AUTHOR]

Collaborative filtering systems based on a matrix are effective in recommending items to users. However, these systems suffer from the fact that they decrease the accuracy of recommendations, recognized specifically as the sparsity and the first rater problems. This paper proposes the constructing full matrix through Naïve Bayesian, to solve the problems of collaborative filtering. The proposed approach uses Naïve Bayesian, in order to convert the sparse ratings matrix into a full ratings matrix; subsequently using collaborative filtering, to provide recommendations. The proposed method is evaluated in the EachMovie dataset and the approach is demonstrated to perform better than both collaborative filtering and content-based filtering. [ABSTRACT FROM AUTHOR]

Utterance verification is a process, in which a spoken utterance is verified against the given keyword. This process is used to make a decision on acceptance or rejection. In this paper, we propose a new approach to the utterance verification, using a boosting classifier with ten confidence measures. This classifier combines a set of 'weak' learners into a 'strong' one. The experimental results present that it can remarkably improve the verification performance. Compared with a single confidence measure, the equal error rate is reduced by up to 23%. The results also show that the boosting classifier is better than the SVM and MLP classifiers, in term of the equal error rate. [ABSTRACT FROM AUTHOR]

A multistage stochastic financial optimization manages portfolio in constantly changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. In this paper, we present a decision-making process that uses our proposed Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm to solve multi-stage portfolio optimization problem. The objective function is classical return-variance function. The performance of our algorithm is demonstrated by optimizing the allocation of cash and various stocks in S&P 100 index. Experiments are conducted to compare performance of the portfolios optimized by different objective functions with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) in terms of efficient frontiers. [ABSTRACT FROM AUTHOR]

Car plate Localization, which remains a difficult problem under complicated environment, is the key problem in many traffic related applications. In this paper we describe a new method based on modified Pulse Coupled Neural Network (PCNN) with adaptive threshold, which can capture relatively complete objects in human perception. After inverse filtering, PCNN processing is applied to produce a firing time sequence image. Then car plates' position and rotated angle can be extracted from the firing image. Experiment results show that the correct car plate locating rate reaches 98%, which is higher than other Localization methods on the same image database. [ABSTRACT FROM AUTHOR]

In order to meet the require of high speed and comfort and low producing cost, semi-active suspension for high-speed railway vehicle is adopted to control the vibration of car body. But accurate mathematic model of vehicle dynamics system is difficult to establish for the particularity of wheel-rail interaction and non-linear of system sky damper control needs the absolute speed of car body that its exact value is difficult to obtain. So adaptive fuzzy control method based on the acceleration feedback is put forward in the paper. The method which can modify automatically the scaling factor and control rules according to the acceleration and its change rate of car body is simulated by using ADAMS and MATLAB software. The simulation results show that the method can attenuate the vibration of car body and improve the comfort and stationarity effectively. [ABSTRACT FROM AUTHOR]

Owing to the fact that the networked control system is featured by uncertain delays, the stochastic time delay can be transformed into a deterministic delay by placing a special mount of buffers at the nodes in the networked control system, thus transform the stochastic networked control system into a deterministic delay system. A controller applying the predictive functional control is hence designed, for the purpose of improving the control capability through realizing the presumption of mould matching and multi-step prognosis. The results of both the simulation based on MATLAB and the experiment based on distributed control system (DCS) show that the method put forward in this paper are not only correct but also effective. [ABSTRACT FROM AUTHOR]

In this paper, a class of adaptive control systems is to be studied, in which the identification loop is closed over communication networks. The network-induced delays caused by the communication network are then inevitable and randomly time-varying in general. And these delays between controller and identifier can make a mess of the transmitted packet sequences which may deteriorate the control performance even destabilize the system. Obviously, the randomly time-varying delay is a key problem in networked adaptive control systems, and a strategy using the concept of fixed maximum delay is presented to avoid this problem. The adaptive system is proven to be convergent if such strategy is to be used. After that, with a view to further improve the performance, another modified strategy is proposed and the simulation results verify its validity and practicability. [ABSTRACT FROM AUTHOR]

The Open Vehicle Routing Problem was brought forward several decades ago, but it has still received little attention from researchers for a satisfactory solution. In this paper, a novel real number encoding method of Particle Swarm Optimization (PSO) for Open Vehicle Routing Problem is proposed. The vehicle is mapped into the integer part of the real number; and the sequence of customers in the vehicle is mapped into the decimal fraction of the real number. After decoding, several heurist methods are applied into the post-optimization procedure, such as Nearest Insertion algorithm, GENI algorithm, and 2-Opt. They are used to optimize the inner or outer routes and modify illegal solutions. In the experiments, a number of numerical examples are carried out for testing and verification. The performance of the proposed post-optimization algorithm is analyzed and the particle swarm optimization algorithm is compared with other heuristic methods for the same problem. [ABSTRACT FROM AUTHOR]

Facility layout problems concerning space layout optimization have been investigated in depth by researchers in many engineering fields. In this paper, a particular facility layout problem with aisles and two objectives at minimizing total cost of material handling and maximizing adjacent requirement between resources is discussed and formulated as an nonlinear mixed-integer programming. To solve the NP-hard problem, a multiple objective genetic algorithm approach with local search method is developed. The application on a practical FLP case and numerical analysis show the effectiveness and efficiency of the proposed method on FLPs. [ABSTRACT FROM AUTHOR]

In this paper, we build a Japanese emotion corpus and perform statistical analysis on it. We manually entered in about 1,200 example dialogue sentences. We collected statistical information from the corpus to analyze the way emotion is expressed in Japanese dialogue. Such statistics should prove useful for dealing with emotion in natural language. We believe the collected statistics accurately describe emotion in Japanese dialogue. [ABSTRACT FROM AUTHOR]

Software metrics are collected at various phases of the software development process. These metrics contain the information of software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which is one of data mining techniques, is adopted to build the software quality prediction models in early period of software testing. In this paper, three clustering methods, k-means, fuzzy c-means and Gaussian mixture model, are investigated for the analysis of two real-world software metric datasets. The experiment results show that the best method in predicting software quality is dependent on practical dataset, and clustering analysis technique has advantages in software quality prediction since it can be used in the case having little prior knowledge. [ABSTRACT FROM AUTHOR]

Quantifiers and numerals often cause mistakes in Chinese-Japanese machine translation. In this paper, an approach is proposed based on the syntactic features after classification. Using the difference in type and position of quantifiers between Chinese and Japanese, quantifier translation rules were acquired. Evaluation was conducted using the acquired translation rules. Finally, the adaptability of the experimental data was verified and the methods achieved the accuracy of 90.75%, which showed that they were effective in processing quantifiers and numerals. [ABSTRACT FROM AUTHOR]

Authors of news stories through their choice in words and phrasing inject an underlying emotion into their stories. A story about the same event or person can have radically different emotions depending on the author, newspaper, and nationality. In this paper we propose a system to judge the emotion of a news article based on emotion word, idiom and modifier dictionaries. This type of system allows one to judge the world opinion on varying topics by looking at the emotion used within news articles about the topic. [ABSTRACT FROM AUTHOR]

The increasing need for efficient image storage in hospitals imposes heavy requirements on the design of picture archiving and communication systems. Thus new methods are needed to improve the medical image compression performance. In this paper, we propose an efficient coding algorithm called OSS (Optimal Subband Shift) scheme based on the RB-IWT (Reversible Biorthogonal Integer Wavelet Transform). In the new scheme, the original image is first decomposed by the RB-IWT. Then, the image coefficients of every subband are multiplied by the powers of two. Finally, the SPECK coding is applied. Experimental results show that the OSS scheme does not only provide the PSNR performance better than SPECK using the original RB-IWT without the subband shift, but also has the low coding complexity. So this idea is valuable for future research in medical image coding and its applications. [ABSTRACT FROM AUTHOR]

In this paper we present a semantic analyzer for aiding emotion recognition in Chinese. The analyzer uses a decision tree to assign semantic dependency relations between headwords and modifiers. It is able to achieve an accuracy of 83.5%. The semantic information is combined with rules for Chinese verbs containing emotion to describe the emotion of the people in the sentence. The rules give information on how to assign emotion to agents, receivers, etc. depending on the verb in the sentence. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the modeling of oil well pressure data compensation system(OWPCS) based on Neural Networks(NN). Firstly, the operational principle and configuration of OWPCS is described. Then the currently widely used modeling method for OWPCS is given, and its limitations and disadvantages are also illustrated. Secondly, in order to solve the OWPCS modeling problem more reasonably, a new approach based on Neural Network is proposed. Thirdly, the feasibility of using NN to solve this problem is analyzed, and a three-layer BP network is constructed to testify the new modeling method. Fourthly, considering the defect of BP learning algorithm and the special application environment of OWPCS, some improvements are given. Finally, experiment results are presented to show the reasonableness and effectiveness of the new method. [ABSTRACT FROM AUTHOR]

Fourteen properties and their interdependencies contributed to the analysis of six classes implication operators-S-implications, R-implications, QL-implications, Force implication, f- generated implication operator and g- generated implication operator- are explored in this paper. It is found that all the properties can be inferred from three mutually independent properties. Then, the proven concerning which properties are true, false or satisfied for each of the six classes of implication operators is given. Based on which, the author get the property I(x(n(x)) = n (x) for all x ∈[0,1] that does not hold for force implication, f-generated implication operator and g-generated implication operator. [ABSTRACT FROM AUTHOR]

This paper develops a fuzzy model that considers the uncertain attributes of change orders in a research and development (R&D) project. It is assumed that the arrival times of change orders follow a fuzzy renewal process. The contingency based on the confidence level offered by the decision-maker is estimated by fuzzy simulation. Furthermore, the effect of schedule delays which increase further the project cost and schedule is considered explicitly. Finally, three numerical examples are presented to illustrate the application of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on the use of the linguistic information of the text pair in Gaussian mixture model (GMM) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, cognate score and a bilingual lexicon extracted from the parallel corpus under consideration. A set of manually prepared training data has been assigned to train the Gaussian mixture model. Another set of data was used for testing. Using the Gaussian mixture model approach, we could achieve error reduction of 160% over length based approach when applied on English-Arabic parallel documents. In addition, the results of (GMM) outperform the results of the combined model which exploits length, punctuation, cognate and bilingual lexicon in a dynamic framework. [ABSTRACT FROM AUTHOR]

Many traditional algorithms use single metric generated by multi-events to detect intrusion by comparison with a certain threshold. In this paper we present a metric vector-based algorithm to detect intrusion while introducing the sample distance for both discrete and continuous data in order to improve the algorithm on heterogeneous dataset. Experiments on MIT lab Data show that the proposed algorithm is effective and efficient. [ABSTRACT FROM AUTHOR]

New applications of artificial neural networks are capable of recognition and verification of effects and the safety of commands given by the operator of the technological device. In this paper, a review of selected issues is carried out in relation to estimation of results and safety of the operator's commands as well as the supervision of the process. A view is offered of the complexity of effect analysis and safety assessment of commands given by the operator using neural networks. The first part of the paper introduces a new concept of modern supervising systems of the process using a natural language human-machine interface and discusses general topics and issues. The second part is devoted to a discussion of more specific topics of automatic command verification that has led to interesting new approaches and techniques. [ABSTRACT FROM AUTHOR]

Environments based on ambient intelligence require new interfaces that allow a natural interaction. The development of these interfaces has to be done in a standard way, considering the dynamic characteristics of these environments. In this paper we present the results in the development of an intelligent environment and a description language for the automatic generation of a spoken dialogue interface, which adapts to the characteristics of every environment. [ABSTRACT FROM AUTHOR]

In recent years, several methods on human emotion recognition have been published. In this paper, we proposed a scheme that applied the emotion classification technique for emotion recognition. The emotion classification model is Support Vector Machines (SVMs). The SVMs have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. The Emotion Recognition System will be recognise emotion from the sentence that was inputted from the keyboard. The training set and testing set were constructed to verify the effect of this model. Experiments showed that this method could achieve better results in practice. The result showed that this method has potential in the emotion recognition field. [ABSTRACT FROM AUTHOR]

Part-of-speech (POS) tagging and shallow parsing are sequence modeling problems. While HMM and other generative models are not the most appropriate for the task of labeling sequential data. Compared with HMM, Maximum Entropy Markov models (MEMM) and other discriminative finite-state models can easily fused more features, however they suffer from the label bias problem. This paper presents a method of Chinese POS tagging and shallow parsing based on conditional random fields (CRF), as new discriminative sequential models, which may incorporate many rich features and well avoid the label bias problem. Moreover, we propose the information feedback from syntactical analysis to lexical analysis, since natural language should be a multi-knowledge interaction in nature. Experiments show that CRF approach achieves 0.70% F-score improvement in POS tagging and 0.67% improvement in shallow parsing. And we also confirm the effectiveness of information feedback to some complicated multi-class words. [ABSTRACT FROM AUTHOR]