• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.1022-1027
• /
• 1988

The author propose a method to realize a self organization in the artificial system. In self organizing system, sub-systems are not constructed as functional parts of the system but cooperate with one another to realize the total system. Each sub-system obtains the local purpose from the global purpose by learning. This function is realized by using a neural network. The validity of this method is confirmed by some simulations.

• 대한기계학회논문집
• /
• 제18권8호
• /
• pp.1939-1951
• /
• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• 전자공학회논문지B
• /
• 제32B권12호
• /
• pp.1652-1662
• /
• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• 한국IT서비스학회지
• /
• 제20권1호
• /
• pp.81-92
• /
• 2021

As the degree of economic development of society increases, the maintenance issues on the existing social overhead capital becomes essential. Accordingly, the adaptation of the concept of Level of service in highway maintenance is indispensable. It is also crucial to manage and perform the service level such as road assets to provide universal services to users. In this regards, the purpose of this study is to improve the maintenance service rating model and to focus on the assessment items and weights among the improvements. Particularly, in determining weights, an Analytic Hierarchy Process (AHP) is performed based on the survey response results. After then, this study conducts unsupervised neural network models such as Self-Organizing Map (SOM) and Davies-Bouldin (DB) Index to divide proper sub-groups and determine priorities. This paper identifies similar cases by grouping the results of the responses based on the similarity of the survey responses. This can effectively support decision making in general situations where many evaluation factors need to be considered at once, resulting in reasonable policy decisions. It is the process of using advanced technology to find optimized management methods for maintenance.

• 전자공학회논문지SC
• /
• 제38권4호
• /
• pp.31-36
• /
• 2001

본 논문에서는 근육의 움직임에 의해 유발되는 전기적 선호인 근전도(EMG) 신호를 신경회로망을 통해 분류하여 인체의 움직임을 파악하는 방법을 제안한다. 신호분류를 위한 신경회로망으로 학습에 의해 스스로 출력뉴런을 구성하는 SOM을 사용하였으며， 기존의 방식과 다르게 전처리 과정 없이 신호자세를 SOM의 입력으로 사용하여 패턴을 분류하는 간단한 방식이다. 실험과 시뮬레이션을 통해 제안한 방식의 유용성을 확인하였다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
• /
• pp.431-434
• /
• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 대한산업공학회지
• /
• 제25권3호
• /
• pp.382-392
• /
• 1999

Industrial robots have increased in both the number and applications in today's material handling systems. However, traditional approaches to robot controling have had limited success in complicated environment, especially for real time applications. One of the main reasons for this is that most traditional methods use a set of kinematic equations to figure out the physical environment of the robot. In this paper, a neural network model to solve robot manipulator's inverse kinematics problem is suggested. It is composed of two Self-Organizing Feature Maps by which the workspace of robot environment and the joint space of robot manipulator is inter-linked to enable the learning of the inverse kinematic relationship between workspace and joint space. The proposed model has been simulated with two robot manipulators, one, consisting of 2 links in 2-dimensional workspace and the other, consisting of 3 links in 2-dimensional workspace, and the performance has been tested by accuracy of the manipulator's positioning and the response time.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
• /
• pp.447-453
• /
• 2003

Emotion을 이용한 컴퓨터 인공 지능, 그래픽, 로봇, 상호 작용 등 다양한 분야에 나타나고 있다. 이에 각 분야에 적용되어진 이론적 배경과, 적용의 특징, 기술 등을 본 글에서 다루고자 한다. 먼저 이론적 접근방식에 있어서는 심리학적 접근과, 사람의 감정 연구, Behavior-Bas설 접근, 생물 행동적 접근, 등이 있으며 이를 구현하기 위한 기술로는 학습 알고리즘, Neural Network 의 Self-Organizing Maps, Fuzzy Cognition Maps등이 있다. 적용 분야로는 Software Agent, Agent Robot과 Entrainment Robot 등이 있다. 본 글에서는 이들의 적용 사례들을 살펴보고 Emotion Architecture에 대해서 분석하고자 한다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
• /
• pp.297-300
• /
• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

• International Journal of Precision Engineering and Manufacturing
• /
• 제5권3호
• /
• pp.11-18
• /
• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.