• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2001.10a
• /
• pp.579-582
• /
• 2001

Vector Transformation is a new method unified vector quantization and coding. So far, codebook generation applied to coding was LBG algorithm. But using the advantage of SOFM(Self-Organizing Feature Map) based on neural network can improve a system's performance. In this paper, we generated VTC(Vector Transformation Coding) codebook applied with SOFM algorithm and compare the result for several coding rates with LBG algorithm. The problem of Vector quantization is complicated calculation and codebook generation. So, to solve this problem, we used neural network approach method.

• KSBB Journal
• /
• v.16 no.4
• /
• pp.381-388
• /
• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.195-196
• /
• 2007

The purpose of this paper is to analyze the resource allocation problem in a self organizing network from the viewpoint of game theory. The main focus is to suggest the model and analyze a power control algorithm in wireless ad-hoc networks using non cooperative games. Our approach is based on a model for the level of satisfaction and utility a wireless user in a self organizing network derives from using the system. Using this model, we show a distributed power control scheme that maximizes utility of each user in the network. Formulating this as a non-cooperative game we will show the feasibility of such power control as well as existence of the Nash Equilibrium achieved by the non-cooperative game.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.669-673
• /
• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.2
• /
• pp.101-112
• /
• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of Internet Computing and Services
• /
• v.2 no.2
• /
• pp.105-116
• /
• 2001

According to increase of the factory-automation's(FA) in the field of production, the autonomous guided vehicle's(AGV) role is also increased, The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study. the research about ac1ion base system to evolve by itself is also being actively considered In this paper. we composed an ac1ive and effective AGV fuzzy controller to be able to do self-organization, For composing it. we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. self-organizing controlled(SOC) fuzzy controller proposed in this paper is capable of Self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06a
• /
• pp.415-417
• /
• 2006

분산형 P2P 그리드 시스템을 구축하는데 있어 연산 수행을 위한 노드 구성 기법과 구성된 토플로지에 적합한 연산 수행 보델 및 스케줄링 기법은 필수 요소이다. 하지만 기존 연구에서는 자원 제공자와 휘발성을 고려하지 않은 연산 수행 모델을 사용하였기 때문에 연산의 안정적인 수행이 보장되지 못하고, 시스템의 성능이 떨어지는 문제점이 발생한다. 이에 본 논문에서는 가용성 기반의 자가 조직적 계산 오버레이 네트워크(SelfCON:Self-organizing Computation Overlay Network) 구성 기법과 구성된 토폴로지에 적합한 연산 수행 모델 및 스케줄링 기법을 제안한다. 제안 기법은 자원 제공자 노드의 휘발성을 고려하여 안정성을 높임으로써 전체 연산 성능을 향상시킨다.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.784-789
• /
• 1994

In the mobile robot research, monitoring the present status and self-navigating the robot in various environment are signifiant. This paper treates a navigation algorithm using a fuzzy logic and a sensor system - laser range finder. The navigation algorithm using a fuzzy logic is achieved by organizing the knoweledge base for self-navigation of mobile robot. In order that mobile robot is economically arrived the goal, the knowledge base is applied to acquire the informations of moving distance, direction, and velocity in every cycle time.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.994-998
• /
• 1996

We propose an approach to integrating fuzzy logic control with RBF(Radial Basis Function) networks and show how the integrated network can be applied to multivariable self-organizing and self-learning fuzzy controller. Using the hybrid learning algorithm. To investigate its usefulness and performance, this controller is applied to a time-delayed process system. Simulation results show good control performance and fast convergency in hybrid loaming method.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.445-453
• /
• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.