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Intelligent agents are a new paradigm for developing software applications. Intelligent Agent (IA) can be defined to be an autonomous, problem-solving computational entity capable of effective operation in dynamic and open environments. Intelligent Agents are often deployed in environments in which they can interact, can cooperate, with other agents, including both people and software. In this paper Intelligent Agent architecture is proposed. The proposed architecture is capable to percept the dynamically changing environment. Based upon the perception, historical knowledge and internal design, the Intelligent Agent infers the action effecting the environment and updates the knowledgebase according to the action taken. Based upon the proposed Intelligent Agent architecture an Intelligent Medical Diagnosis System (IMDS) is given. [ABSTRACT FROM AUTHOR]

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. [ABSTRACT FROM AUTHOR]

This chapter explores the cutting-edge field of cognitive informatics (CI) and its applications in cognitive computing. CI is a transdisciplinary study of cognitive and information sciences, which investigates the internal information processing mechanisms and processes of the natural intelligence – human brains and minds – and their engineering applications in computational intelligence. The 7th IEEE International Conference on Cognitive Informatics (ICCI 2008) has been organized at Stanford University during August 14-16, 2008. The ICCI΄08 program covers a wide spectrum of subjects that contribute to cognitive informatics and cognitive computing. This chapter highlights the latest advances in CI and cognitive computing presented in ICCI΄08. The theoretical framework and applications of CI and cognitive computing are described. A set of selected papers from ICCI΄08 is introduced. [ABSTRACT FROM AUTHOR]

This paper presents a new concept of an e-learning system with intelligent two-way speech communication between the system and its users. Computational intelligence methods allow for analysis, evaluation and assessment of user΄s knowledge and skills and user΄s ability for efficient information processing. The system is also capable of control, supervision and optimization of the e-learning process. Developed as a prototype for mobile technologies, the communication system by speech and natural language between the e-learning system and external users consists of biometric user identification, speech recognition, word and sentence recognition, sentence meaning analysis, and user reaction assessment. Also discussed are selected new concepts of intelligent e-learning systems using speech communication. The discussion focuses on recognition and evaluation of spoken natural language sentences with hybrid neural networks. [ABSTRACT FROM AUTHOR]

Data Mining techniques and Artificial Intelligence strategies can be used to solve problems in the stock market field. Most people consider the stock market erratic and unpredictable since the movement in the stock exchange depends on capital gains and losses. Nevertheless, patterns that allow the prediction of some movements can be found and studied. In this sense, stock market analysis uses different automatic techniques and strategies that trigger buying and selling orders depending on different decision making algorithms. In this paper different investment strategies that predict future stock exchanges are studied and evaluated. Firstly, data mining approaches are used to evaluate past stock prices and acquire useful knowledge through the calculation of financial indicators. Transformed data are then classified using decision trees obtained through the application of Artificial Intelligence strategies. Finally, the different decision trees are analyzed and evaluated, showing accuracy rates and emphasizing total profit associated to capital gains. [ABSTRACT FROM AUTHOR]

Cognitive informatics studies the natural intelligence and the brain from a theoretical and a computational approach, which rigorously explains the mechanisms of the brain by a fundamental theory known as abstract intelligence, and formally models the brain by contemporary denotational mathematics. This paper, as an extended summary of the invited keynote presented in AMT-BI 2010, describes the interplay of cognitive informatics, abstract intelligence, denotational mathematics, brain informatics, and computational intelligence. Some of the theoretical foundations for brain informatics developed in cognitive informatics are elaborated. A key notion recognized in recent studies in cognitive informatics is that the root and profound objective in natural, abstract, and artificial intelligence in general, and in cognitive informatics and brain informatics in particular, is to seek suitable mathematical means for their special needs that were missing in the last six decades. A layered reference model of the brain and a set of cognitive processes of the mind are systematically developed towards the exploration of the theoretical framework of brain informatics. The current methodologies for brain studies are reviewed and their strengths and weaknesses are analyzed. A wide range of applications of cognitive informatics and denotational mathematics are recognized in brain informatics toward the implementation of highly intelligent systems such as world-wide wisdom (WWW+), cognitive knowledge search engines, autonomous learning machines, and cognitive robots. [ABSTRACT FROM AUTHOR]

In mathematics science, addition is rather simple computation of arithmetic. However, no matter what advanced computer in the world, and no matter what backward calculator (such as abacus in China etc.) in people΄s hand, to do sum of addition is always a NP problem in all the time. Such as capacities of numbers, precise computing (PC), computing precision (CP), perturbation motion (PM) in computing of numbers, etc. This paper makes an algorithm of novel addition with AI-VCR computation in computer, and gives solutions of avoiding the sum΄s overflowing, insufficient valid figures, PM in arithmetic computation, etc. [ABSTRACT FROM AUTHOR]

Year after year computers increase their processing power. This has been an advantage for the implementation of new and powerful robotic algorithms. While every day more complex algorithms appear, most of the increased processing capabilities of the newest microprocessors get easily exhausted. By analyzing the Artificial Neuron Model, this paper predicts that with the current tendencies in the increase of the microprocessor΄s power in 2050 the processing power of computers could reach the processing power of the human brain. Besides that, while the robotic systems integration becomes harder and no standards exist in this matter, the use of Artificial Neural Networks becomes more popular. This work studies the possibility of having a Neural Processing Architecture in the future, i.e. a Robotic Brain, under the perspective of pure neural processing power, as a feasible alternative in the development and integration of more complex architectures and algorithms in robotics. [ABSTRACT FROM AUTHOR]

Modeling of ethical reasoning has been a matter of discussion and research among distinct scientific fields, however no definite model has demonstrated undeniable global superiority over the others. However, the context of application of moral reasoning can require one methodology over the other. In areas such as medicine where quality of life and the life itself of a patient may be at stake, the ability to make the reasoning process understandable to staff and to change is of a paramount importance. In this paper we present some of the modeling lines of ethical reasoning applied to medicine, and defend that continuous logic programming presents potential for the development of trustworthy morally aware decision support systems. It is also presented a model of moral decision in two situations that emerge recurrently at the Intensive Care Units, a service where the moral complexity of regular decisions is a motivation for the analyze and development of moral decision support methodologies. [ABSTRACT FROM AUTHOR]

Administration of a Web directory and maintenance of its content and the associated structure is a delicate and labor intensive task performed exclusively by human domain experts. Subsequently there is an imminent risk of a directory structures becoming unbalanced, uneven and difficult to use to all except for a few users proficient with the particular Web directory and its domain. These problems emphasize the need to establish two important issues: i) generic and objective measures of Web directories structure quality, and ii) mechanism for fully automated development of a Web directory΄s structure. In this paper we demonstrate how to formally and fully integrate Web directories with the Semantic Web vision. We propose a set of criteria for evaluation of a Web directory΄s structure quality. Some criterion functions are based on heuristics while others require the application of ontologies. We also suggest an ontology-based algorithm for construction of Web directories. By using ontologies to describe the semantics of Web resources and Web directories΄ categories it is possible to define algorithms that can build or rearrange the structure of a Web directory. Assessment procedures can provide feedback and help steer the ontology-based construction process. The issues raised in the article can be equally applied to new and existing Web directories. [ABSTRACT FROM AUTHOR]

This paper presents a novel model for assessing the water quality for the artificial habitat in shrimp aquaculture. The physical-chemical variables involved in the artificial habitat are measured and studied for modeling the environment of the ecosystem. A new physical-chemical index (Г) classifies the behavior of the environmental variables, calculating the frequency and the deviations of the measurements based on impact levels. A fuzzy inference system (FIS) is used for establishing a relationship between environmental variables, describing the negative ecological impact of the concentrations reported. The FIS uses a reasoning process for classifying the environmental levels providing a new index, which describes the general status of the water quality (WQI); excellent, good, regular and poor. [ABSTRACT FROM AUTHOR]

A human-computer ensemble system is one of time concerned cooperative systems, which performs secondo of an ensemble played by a computer-controlled piano cooperating with primo played by a human performer. For creating expressive performance, a rehearsal program is adopted to this system. By the rehearsal program, the system learns the tendency of the expression that the human performer thinks and/or plans. To do so, the program records his/her performance of solo and calculates the tendency, which depends on not only the performer but also the composition of music itself. Hence, it is necessary for the expressive performance to analyze the score of the composition and to experiment dependent on it. This system is an example of intelligent realtime programs appropriate for formal verification and analysis. NΣ-labeled calculus is a formal system for verification for such time-concerned programs. In this paper, a logical specification and experimental results for an expressive ensemble system will be introduced. [ABSTRACT FROM AUTHOR]

Rules determine the functionality of a given system, in either natural or man-made systems. Man-made systems, such as computer applications, use a set of known rules to control the behaviours applied in a strict manner. Biological or natural systems employ unknown rules, these being undiscovered rules which are more complex. These rules are unknown due to the inability to determine how they are applied, unless observed by a third party. The swarm is one of the largest naturally observed systems, with bird flocks and ant colonies being the most notable. It is a collection or group of individuals who use behaviours to complete a given goal or objective. It is the aim of this paper to present rule discovery methods for the mining of these unknown rules within a swarm system, employing a bird flock simulation environment to gather data. [ABSTRACT FROM AUTHOR]

The current research from the field of economic sciences uses the neural and neuro-fuzzy networks procedures as an alternative to classical methods. This paper deals with neuro-fuzzy classifiers and presents a new neuro-fuzzy classifier which considers the example dependent costs in the inductive construction of classifiers. [ABSTRACT FROM AUTHOR]

Software reliability is one of the most important software quality attribute and Software reliability estimation is a hard problem to solve accurately. However for management of software quality and standard practice of the organization, accurate reliability estimation is important. Non-homogeneous Poisson Process (NHPP) models and Artificial Neural Network (ANN) models are among the most important software reliability growth models. In this paper we study an approach using past fault-related data with cubic spline Network model to estimate reliability. A numerical example is shown with simulated datasets. The example shows that the proposed model accurately estimate the software reliability. [ABSTRACT FROM AUTHOR]

Supply chain management system satisfies the customer demands through the most efficient use of resources, including distribution capacity, inventory and labor. E-procurement is expected to be integrated with the supply chain management systems, which are analyzed or processed by computers, in a large-scale and dynamic environment. In this type of systems there can be any number of buyers and suppliers at any time. Each supplier may provide necessary products from other suppliers, piece by piece or as a whole. This type of mechanism forms a multi level hierarchy between suppliers. This paper presents MULEP-a MUlti Level E-Procurement system which deploys the procurement process in this hierarchical supplier structure. System is managed by a set of distributed mobile agents which are developed over Java Agent DEvelopment Framework-JADE for platform independence. [ABSTRACT FROM AUTHOR]

This paper presents the identification of the topological spatial relations that can exist between a Circular Spatially Extended Point and a Line in the geographical space. For the identification of the topological spatial relations that can exist between these two types of objects, the intersections existing between the several parts of the objects were verified. Using 14 conditions and the respective proofs, a set of 38 topological spatial relations were identified. The geometric characterization of the topological relations was also achieved, proving the existence of such relations. [ABSTRACT FROM AUTHOR]

This paper presents the conceptual neighborhood graphs with the transitions between the topological spatial relations that can exist between a circular spatially extended point and a line. The final objective of this work is the use of the transitions in the prediction of a mobile user position in a road network. The conceptual neighborhood graphs were identified using the snapshot model and the smooth-transition model. In the snapshot model, the identification of neighborhood relations is achieved looking at the topological distance existing between pairs of spatial relations. In the smooth-transition model, conceptual neighbors are identified analyzing the topological deformations that may change a topological spatial relation. The obtained graphs and the corresponding topological distances between spatial relations can be used as an alternative, or as a complement, to map-matching techniques usually used to predict the positions of mobile users. [ABSTRACT FROM AUTHOR]

Multi-spinner is used in Photoresist process of semiconductor manufacturing. Photoresist process is the process of wafer deposal including wafer cleaning, surface treatment, Photoresist spread and Soft Baking. Though each successful process of this Multi-spinner equipment works by moving wafer, if optical scheduling applied to improve transfer path, it increases the volume of produced semiconductors. The main concern of paper is to optimize scheduling method using artificial neural network to solve scheduling problem of each processes of Multi-spinner. The effectiveness of optical scheduling is proved by computer simulation. [ABSTRACT FROM AUTHOR]

In this paper an experimental study of classification and diagnosis of different number of broken rotor bars and broken end-ring in the three-phase squirrel cage induction motors is presented. Six different faulted rotors are investigated. These faults are one, two, three broken bars, broken end-ring, a bar with high resistance and healthy rotor. The base structure of the study consist of current signal analysis (CSA), feature extraction, Artificial Neural Network (ANN) and diagnosis algorithm. The motor current signal is used for obtaining of effects of broken bars and end-ring in the rotor. To get sight of the effects the current signal that is in the time domain is transformed time-frequency domain via Short Time Fourier Transform (STFT). And the spectrums are averaged and normalized on the time axis. The rotor cage faults are classified with ANN by using these spectrums. And result matrixes of ANN are considered improved decision structure. Thus the faulted rotors are diagnosed at 100% accuracy and classified 98,33% accuracy. [ABSTRACT FROM AUTHOR]

Integration of deliberation and reaction has been an important research topic concerning agents in view of the need for an agent to react tentatively and immediately to the changing world when unexpected events occur while executing a plan. An agent is not supposed to think for a long time before reacting. Also, its reaction is not supposed to change the world greatly. However, there are some cases where deliberation is necessary for achieving an emergency goal or where the emergency plan execution prevents the resumption of the suspended plan execution. This paper presents a new concept of online interruption planning that integrates deliberation and emergency deliberation. When an emergency goal is given while executing a plan, our agents suspend the current plan execution, make and execute an emergency plan, and resume the suspended plan execution. Because our agents continuously modify the suspended plans while executing an emergency plan, they can resume the suspended plans correctly and efficiently even if the world has changed greatly due to the emergency plan execution. [ABSTRACT FROM AUTHOR]

Decision support systems are designed to assist a user by delivering targeted support for a specific decision problem. The application domain is an important consideration in developing the system, and the decision problem defines needed data, models and processing. Dynamic environments offer unique challenges to the system designer since the situation can change rapidly, requiring modification to the supporting technology. Modern intelligent techniques make it possible to envision a new type of adaptive decision support system (ADSS) that can perceive context and associate relevant models and data with the decision problem. In this paper we propose a framework for ADSS that utilizes intelligent methods to contextualize the decision problem and provide appropriate support to the user. [ABSTRACT FROM AUTHOR]

In order to establish autonomous behavior for technical systems, the well known trade-off between reactive control and deliberative planning has to be considered. Within this paper, we combine both principles by proposing a two-level hierarchical reinforcement learning scheme to enable the system to autonomously determine suitable solutions to new tasks. The approach is based on a behavior representation specified by hybrid automata, which combines continuous and discrete behavior, to predict (anticipate) the outcome of a sequence of actions. On the higher layer of the hierarchical scheme, the behavior is abstracted in the form of finite state automata, on which value function iteration is performed to obtain a goal leading sequence of subtasks. This sequence is realized on the lower layer by applying policy gradient-based reinforcement learning to the hybrid automaton model. The iteration between both layers leads to a consistent and goal-attaining behavior, as shown for a simple robot grasping task. [ABSTRACT FROM AUTHOR]

In this paper, a modified ant colony system (ACS) algorithm is proposed to find a shortest path based on the preference of links. Most of the shortest path search algorithms aim at finding the distance or time shortest paths. However, these shortest paths are not surely an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose the preference-based shortest path search algorithm which uses the properties of the links of the map. The properties of the links are specified by a set of data provided by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links. [ABSTRACT FROM AUTHOR]

This paper gives a brief description of current situation of distributed generation system, and points out that microgrid can run in two kinds of operation modes. The key problems which need to be cautiously considered exist in each operation mode are summarized, and advanced artificial intelligence techniques are adopted to solve those problems as effective tools. The application situation and research status of multi-agent system, artificial neural network, genetic algorithm, fuzzy logic in distributed generation system home and abroad are summarized detailedly. The existing problems and solving thoughts of each artificial intelligence technique are analyzed, and developing directions of artificial intelligence applications in distributed generation system in the future are also prospected. [ABSTRACT FROM AUTHOR]

Intelligence Science is an interdisciplinary subject which dedicates to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain, research on the principles and models of natural intelligence at molecular, cellular, and behavior levels. Cognitive science studies human mental activities, such as perception, learning, memory, thinking, consciousness etc. Artificial intelligence attempts simulation, extension, and expansion of human intelligence using artificial methods and technologies. Researchers specialized in above three disciplines work together to explore new concepts, theories, and methodologies. If successful, it will create a brilliant future in 21st century. The paper will outline the framework of intelligence science and present its ten big challenges. Tolerance Granular Space Model (TGSM) will be discussed as one of helpful approaches. [ABSTRACT FROM AUTHOR]

I offer an alternative to Searle΄s original Chinese Room argument which I call the Sanskrit Room argument (SRA). SRA distinguishes between syntactic token and semantic symbol manipulations and shows that both are involved in human language understanding. Within classical mechanics, which gives an adequate scientific account of token manipulation, a symbol remains a subjective construct. I describe how an objective, quantitative theory of semantic symbols could be developed by applying the Schrodinger equation directly to macroscopic objects independent of Born΄s rule and hence independent of current statistical quantum mechanics. Such a macroscopic quantum mechanics opens the possibility for developing a new theory of computing wherein the Universal Turing Machine (UTM) performs semantic symbol manipulation and models macroscopic quantum computing. [ABSTRACT FROM AUTHOR]

This paper presents a proposal to facilitate the use of online documents from the World Wide Web (WWW) - to acquire knowledge for intelligent Conversation Agents (CA). Information extracted from public web pages has--long been an issue that web pages may contain incorrect information or are outright hoaxes. Therefore, we propose a Web Knowledge Trust Model (WKTM) to find 'trustworthy' websites and to ensure the credibility and reliability of the knowledge extracted from the web-derived corpora. The results indicate that WKTM is useful for evaluating the trustworthiness of web sites and it is useful for the developing of key criteria for a conversation agent's domain knowledge base. [ABSTRACT FROM AUTHOR]

Nowadays, organizations are overwhelmed with a large amount of electronic data that require proper management to discover previously hidden knowledge. Having a set of non-transformed data may be a huge waste as specific processes onto the data would result in the discovery of valuable knowledge. This paper describes the development of a predictive model to classify undergraduate students' class of graduation: first class, second class upper division, second class lower division, or third class. The hybrid techniques used to support the classification are Artesian probability and neural network. [ABSTRACT FROM AUTHOR]

In this paper, we describe GCUCE (Grid Computing for Ubiquitous Computing Environment) core service modules which are composed of context, environment, event, task, space management for application mobility. They are interacting with each other based on context-awareness infrastructure which provides intelligent functions to users. Moreover, we present a unified ubiquitous service interacting with Grid core service modules on Computation Grid and Access Grid computing, which provides scalability and collaboration. Also, we describe an enterprise service model, and base on that model, shall show the performance of GCUCE by performing several experiments for DOWS (Distributed Object-oriented Wargame Simulation). [ABSTRACT FROM AUTHOR]

Group Decision Making (GDM) is an important human activity and it has many practical applications in society, economy, management and engineering, etc. Researchers are faced with new challenges in research on theory and methods of GDM with the rapid advent of internet and information technology. One of the challenges in collaborative work is social decision making in a computer mediated environment. Social trust models like recommender system, Bayesian trust for pervasive computing, are becoming invaluable part of distributed systems, where uncertainty prevails. In this paper we have proposed a Trust based collaborative decision making algorithm for distributed environment in which a group of agents collaborate for decision making. [ABSTRACT FROM AUTHOR]

An agent usually holds a very large number of beliefs. Hopefully, efficient belief changes should be performed only in the part of its relevant states at a time. Parikh showed that AGM belief change operations do not always respect his relevance criterion. Kourousias and Makinson showed that they will do so if the given belief set K is in canonical form K'. However, even when K is closed under classical consequence, K' will be a belief base and usually is not closed under classical consequence. In this paper, we first show that there are two alternative approaches to guarantee the relevance criterion. They are constructed by replacing the family K⊥x with the family K⊥'x and K⊥*x respectively. The latter is general enough to generate all the relevance-respecting belief change operations definable on K itself. [ABSTRACT FROM AUTHOR]

This paper aims to propose a novel approach for high heel design using the integral schemes of Kansei engineering and grey-based artificial intelligence. First, to meet the market΄s needs, the Kansei engineering scheme is adopted in order to translate the customer΄s preferences into the product΄s form elements. Secondly, to speed up and enhance the translation performance, the grey system theory and radial basis function neural network schemes are used. Thirdly, a bi-directional evaluation hybrid Kansei engineering system is constructed via the aforementioned methodology. Finally, a form design expert system is proposed in consideration of designer΄s usage. To illustrate the functions of the proposed design expert system, an example of the development of new high heels is demonstrated. [ABSTRACT FROM AUTHOR]

Our work explores the use of several text categorization techniques for classification of manufacturing quality defect and service shop data sets into fixed categories. Although our work was in the area of manufacturing quality the technique is applicable to free form, short text summaries of data that may be stored in a database, file, or document. We refer to these types of text as "technical text passages". These summaries may not follow standard grammar conventions; they commonly contain abbreviations, technical phrases, misspelled words and industry specific acronyms. Typical types of text to be classified include aircraft engine repair shop findings, industrial manufacturing quality problems and corrective actions, and standardization of attributes in a bill-of-materials. In this paper, we will present our results in using machine learning and rule based algorithms to categorize text. Our results show that the rules based approach is as good as several machine learning approaches. For example, using Support Vector Machine algorithms we were able to achieve 82% accuracy on validation set, using 1,645 training samples and 823 validation samples. Each category had 50 or more samples. Using rule-based approach we were able to achieve 80% accuracy. [ABSTRACT FROM AUTHOR]

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case. [ABSTRACT FROM AUTHOR]

The build up of water ice on aircraft flight surfaces poses a significant safety risk. As a result, much effort has gone into studying this problem in order to understand how individual droplets contribute to the accretion process. One approach has been to capture the moment of impact of a supercooled droplet onto a surface placed in an icing tunnel. However, this produces a large number of images that must be analysed manually. This paper describes the development of an automated analysis system, employing image processing techniques, that is capable of classifying the impact images without operator input. Using a carefully chosen feature vector and K-means clustering algorithm, the classification results from the automated system are comparable with that achieved using the manual approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

Pose problem is a big challenge for applying face recognition technology under real world conditions. In this paper, appearance based approach was proposed to recognize face across front and non-frontal view images by reconstructing frontal view features. Statistical learning method based on sample images is applied to find transformation matrix which encapsulated general knowledge of pose transition in feature subspace, therefore, different view feature vectors constituted linear equations and transformation matrix can be solved from the equations by least square (LS) approach. Experimental results on popular FERET and CMU databases showed that the proposed method could cope with the head rotation roughly within half profile view. Compared with model based approaches, this method is not dependent on heavy computation and has merit of easy implementing in live conditions. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to develop image watermarking algorithms which are portable to a variety of applications such as copyright protection, fingerprinting and identification. Therefore, we require that the watermark be binary and be not only detectable but also extractable. The embedding technique is based on the parent-child structure of the multiwavelet transform called "triple tree" and this technique does not require the original image in the watermark extraction. The experimental results show that the watermark. [ABSTRACT FROM AUTHOR]

Face recognition is a biometric identification method which among the other methods such as, finger print identification, speech recognition, signature and hand written recognition has assigned a special place to itself. In principle, the biometric identification methods include a wide range of sciences such as machine vision, image processing, pattern recognition neural networks and has various applications in film processing, control access networks and etc. There are several methods for recognition and appearance based methods is one of them. One of the most important algorithms in appearance based methods is linear discriminant analysis (LDA) method. One of the drawbacks for LDA in face recognition is the small sample size (SSS) problem so it is suggested to first reduce the dimension of the space using methods among which, principal component analysis (PCA) is the most popular one. In this paper we show that there exist stronger methods such as discrete cosine transform (DCT). [ABSTRACT FROM AUTHOR]

This paper presents a new method for the signature analysis of mechanical watch movements. Contrary to the existing method, it analyzes the time-frequency features of mechanical watch movement through a combination of reassigned scalogram and Finite Element Analysis (FEA). By mapping the signal into a 2D domain of time and frequency, the reassigned scalogram reveals the frequency components of the shocks at different time. On the other hand, FEA gives the modal frequencies of the movements. By comparing the frequency components at different shocks to the modal frequencies of the movement, possible malfunctions of the movement can then be detected. A number of demonstration examples are included. [ABSTRACT FROM AUTHOR]

This paper presents a multi step-size monic normalization equation-error linear filter. We also extend the idea to nonlinear adaptive filter and derive a multi step-size monic normalization equation-error bilinear filter (MSS MNEEBF). The algorithms enjoy fast convergence behavior and can remove biased estimates associated with conventional equation-error adaptive filters. Simulation results validate the usefulness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a neural network global PID-sliding mode control method for the tracking control of robot manipulators with bounded uncertainties. A certain sliding mode controller with PID sliding function is developed. In this controller, the switching gain is tuned by a RBF neural network on the reachable condition of sliding mode. Thus, the effect of chattering can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding function. Mathematical proof of the stability and convergence of the control system is given. Simulation results demonstrate that the chattering and the steady state errors are eliminated and satisfactory trajectory tracking is achieved. [ABSTRACT FROM AUTHOR]

The development of the transformer insulation failure undergoes three stages: insulation aging, incipient faults and a short circuit. This paper presents a complete scheme to simulate single-phase transformers with insulation deterioration and arcing phenomena i.e. in the first two stages. The approach incorporates Transmission Line Methods (TLM), Jiles-Atherton Hysteretic Model, Composite Cassie-Mayr Arcing Model and Dielectric Model. A small 25kVA 11kV/220V power transformer with aging and incipient faults is taken as example for simulation. [ABSTRACT FROM AUTHOR]

This paper describes results concerning the robustness and generalization capabilities of kernel methods in detecting intrusions using network audit trails. We use traditional support vector machines (SVM), biased support vector machine (BSVM) and leave-one-out model selection for support vector machines (looms) for model selection. We also evaluate the impact of kernel type and parameter values on the accuracy of a support vector machine (SVM) performing intrusion classification. Through a variety of comparative experiments, it is found that SVM performs the best for detecting Normal and User to Super User, BSVM performs the best for Denial of Service attacks, and looms based on BSVM performs the best for Probe and Remote to Local. We show that classification accuracy varies with the kernel type and the parameter values; thus, with appropriately chosen parameter values, intrusions can be detected by SVMs with higher accuracy and lower rates of false alarms. [ABSTRACT FROM AUTHOR]

Clustering is grouping of patterns according to similarity or distance in different perspectives. Various data representations, similarity measurements and organization manners are led to several classes of clustering methods. In this paper a new combinatorial method is proposed that iteratively uses another clustering method such as Rival Penalized Competitive Learning (RPCL) or K-Means as the core of clustering system. Moreover, some novel auxiliary techniques are suggested to increase the clustering performance. The proposed method has been compared with well known clustering methods such as K-Means, its improvement, ISODATA and DSRPCL2. The new combinatorial technique can detect the drawbacks of core clustering method and improve its efficiency. Our method is applied on some standard multi-class datasets. After clustering, labels of grouped samples in each cluster are compared with their real class labels to show the accuracy of clustering. [ABSTRACT FROM AUTHOR]

In order to be successful in multi-agent electronic negotiating environments, intelligent agents should be capable of adapting their negotiation strategies and tactics so that they can achieve an agreement with optimized profit. In this paper, some findings are going to be shown in which negotiating intelligent agents in electronic commerce start negotiating using a simplified standard protocol in conjunction with a combined negotiation. Taking advantage of a new developed evolutionary algorithm, agents configure their negotiation strategies somehow they can get more profit. They can use fuzzy fairness function to behavior with fairness or without fairness. [ABSTRACT FROM AUTHOR]

Seam pucker grade is one of the most important quality parameters in garments manufacturing industry. At present, seam pucker is usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. Instead of subjective evaluation, this paper presents an objective method by using image analysis and pattern recognition. The evaluation system consists of image acquisition, image normalization, feature extraction and self organizing map classifier. Textural features of seam puckers are studied with a widely used statistical method, the co-occurrence matrix approach. The grades of seam puckers can be obtained from the trained self organizing map classifier and the results are very promising. [ABSTRACT FROM AUTHOR]

The new product development (PD) phase of automotive vehicles in particular and of any complex product in general is a process that requires effective use of knowledge and experience generated from the past. It also involves a large amount of uncertainty which is very difficult to incorporate in well established methodologies and techniques such as quality tools without a large number of assumptions. The use of expert systems combined with artificial intelligence methods is the modern approach to incorporate uncertainty into the various estimations necessary for the improvement of the product development process. This paper introduces a new concept for the design of a reliability improvement system for the automotive industry that combines some of the newest approaches in the discipline of knowledge engineering. The methodology presented aims to make the most effective use of, and prevent the loss of, knowledge that organisations generate through their product development processes. [ABSTRACT FROM AUTHOR]

This paper presents an energy back-propagation algorithm (EBP). Learning and convergence processes of the standard backpropagation algorithm (SBP) are based on the energy function. The energy function is used with the convergence process to extract the nearest image for the unknown tested image. The EBP algorithm shows considerably better performance in terms of time of learning, time of convergence, and size of input image compared to the SBP algorithm. [ABSTRACT FROM AUTHOR]