This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and `the curse of dimension`. Thus, We implemented a simulator called `IFS` and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called `Interactive Feature Selection`. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

Trust can be defined as the level of subjective probability with which an agent will perform a particular action. This paper proposes a comprehensive fuzzy trust model which incorporates dispositional trust, general trust, and situational trust and reputation information. In the model, the preference degrees for the interaction outcomes with respect to the evaluation criteria are expressed in a fuzzy set, and Sugeno`s fuzzy integral is employed to aggregate the satisfaction degrees with respect to the importance of evaluation criteria which can be assigned in a way to preserve the properties of the measure.

The Context-Aware system is the core technology in the Ubiquitous Computing Environment. Recently, the practical use of a sensor is magnified and the application fields of it are gradually extended in order to collect necessary context information. Context-Aware service integrates the context information which is collected by sensors, and then provides, a suitable service to a user through the process of analysis and reasoning. This service is studied in a variety of fields such as marketing, medical treatment, education and so on. In this paper, we analyze the method of recognizing surrounding context and the result of the awareness by using differential and structural property of multiple valued logic function; propose the model that provides appropriate service depending on the change of surrounding contort; confirm the applicability of the Context-aware system by showing the example of application.

This paper deals with a modified method of the dynamic time warping and feature points to extract various important information of the signature for the dynamic signature verification. We could achieve lower equal error rate, small and efficient feature points and fast processing time for the notification.

A convolution with HRTF DB and the original sound is generally used to make the method of sound image localization for virtual speaker realization. But it can decline localization by the confusion between up and down or front and back directions due to the non-individual HRTF depending on each listener. In this paper, we study a virtual speaker using a new HRTF, which is grouping the HRTF around the virtual speaker to improve localization between up and down or front and back directions. To effective HRTF grouping, we decide the location and number of HRTF using informal listening test. A performance test result of virtual speaker using the grouped HRTF shows that the proposed method improves the front-back and up-down sound localization characteristics much better than the conventional methods.

Multi-domain design is difficult because such systems tend to be complex and include a mixtures of electrical, mechanical, hydraulic, and thermal components. To design an optimal system, unified and automated procedure with efficient search technique is required. This paper introduces design method for multi-domain system to obtain design solutions automatically, combining bond graph which is domain independent modeling tool and genetic programming which is well recognized as a powerful tool for open-ended search. The suggested design methodology has been applied for design of electric fitter, electric printer drive, and and pump system as a proof of concept for this approach.

Reducing power consumption to extend network lifetime is one of the most important challenges in designing wireless sensor networks. One promising approach to conserving system energy is to keep only a minimal number of sensors active and put others into low-powered sleep mode, while the active sensors can maintain a connected covet set for the target area. The problem of computing such minimum working sensor set is NP-hard. In this paper, a centralized Voronoi tessellation (CVT) based approximate algorithm is proposed to construct the near optimal cover set. When sensor`s communication radius is at least twice of its sensing radius, the covet set is connected at the same time; In case of sensor`s communication radius is smaller than twice of its sensing radius, a connection scheme is proposed to calculate the assistant nodes needed for constructing the connectivity of the cover set. Finally, the performance of the proposed algorithm is evaluated through theoretical analysis and extensive numerical experiments. Experimental results show that the proposed algorithm outperforms the greedy algorithm in terms of the runtime and the size of the constructed connected cover set.

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

This paper proposes an approach to search for the odor source using an autonomous mobile robot equipped with vision and odor sensors. The robot is initially navigating around the specific area with vision system until it looks for an object in the camera image. The robot approaches the object found in the field of view and checks it with the odor sensors if it is releasing odor. If so, the odor is classified and localized with the classification algorithm based on neural network The AMOR(Autonomous Mobile Olfactory Robot) was built up and used for the experiments. Experimental results on the classification and localization of odor sources show the validity of the proposed algorithm.

In this study, we suggest the effective storage structure and management method for XML-based electrocardiography(ECG) data to support the interoperability between medical information systems, and implement the metadata system of ECG data providing the web-based information service. ECG matadata management system include functions for storing and managing as well as reporting PDF service of ECG data. We analyzed a characteristics of the data and access patterns for XML-based ECG and then used the non-partitioning storing method and indexing the extracted metadata from the HL7 aECC for supporting the quick search. We, using the template mechanism, converts the XML-based results data into various formats in order to provide services of the ECG reporting.

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user`s, preference on fuzzy values and select one which fits to his preference. Users` preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users` answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users` preference than other methods.

Numbers of research on ontology construction and its application are being done for knowledge and information processing using computers. But, the current ontology development methods and ontology construction tools are using in restricted field on propose. Therefore, proper ontology development processes and ontology construction tools on ontology characteristic are needed. In this paper, we propose ontology construction process(OntoProcess) that non-experts in specific field are able to construct ontology through conceptualization of knowledge and formalization of concepts from language resource. Beside, some problems may be occurred while numbers of people are working together to construct ontology: i)duplicated concept definition in conceptualization process of knowledge and ii)decreasing efficiency of ontology construction by short understanding about formal language and tool operation in formalization process. To solve the problems, we propose an ontology construction process for multiple developers (OntoProcess) using meta ontology. We develop an ontology construction system(OntoCS) based on proposed processes, and we show the efficiency of proposed processes and system from ontology construction experiment.

This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

Evaluation of learning ability of students is classified a step of diagnostic, formative and summative evaluation. This step-by-step evaluation is the standard of synthesis judgement, from a student`s prior learning of preparation state to devotion of learning process and even learning result. In this paper, we propose the method of synthesis learning evaluation which is considered evaluation of each step in using fuzzy inference. In order to get objective evaluation of learning ability, we applied to the weights by evaluation steps. And we reflected defuzzification values of final evaluation membership function interval obtained by fuzzy inference about diagnostic, formative and summative evaluation. As a result, it processes definite inference ensures objectivity and shows validity of the synthesis evaluation method.

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, is a positive constant. The increase of has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

We present the development of the digital fuzzy controller for maximum power regulation. A hybrid system that comprises wind and photovoltaic generation subsystems, and battery bank is developed in this paper. We use Takaki-Sugeno (T-S) fuzzy model to deal with the power regulation problem, since each power generator has complex nonlinear terms. The problem for regulation control can be simplified into a stabilization one. Also, in order to utilize the advanced digital device, we perform the intelligent digital redesign method. Finally, the performance of the proposed controller is extensively assessed through computer simulation.

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

In this paper, we focus on the building domain ontology from corpus by extracting concepts and properties relationships based on linguistic patterns. The pharmacy field is selected as an experiment domain and we present an algorithm to extract hierarchical structure for terminology based on the noun/suffix patterns of terminology in domain texts. In order to show usefulness of our domain ontology, we compare a typical keyword based retrieval method with an ontology based retrieval mettled which uses related information in an ontology for a related feedback. As a result, our method shows the improvement of precision by 4.97% without losing recall.

A research about facial image recognition is studied in the most of images in a full race. A representative part, effecting a facial image recognition, is eyes and a mouth. So, facial image recognition researchers have studied under the central eyes, eyebrows, and mouths on the facial images. But most people in front of a camera in everyday life are difficult to recognize a fast change of pupils. And people wear glasses. So, in this paper, we try using Principal Component Analysis(PCA) for facial image recognition in blindfold case.

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

In RFID-based SCM, The traceability and product information is the important target data. In this paper, efficient items traceability model and the integrated model of the product between RFID network and GDS(Global Data Synchronization) network are studied. Information consists of the dynamic data generated from RFID network and static data generated from GDS Network. The integrated model will provide the interoperability between 2 kinds of networks.