• 한국산학기술학회논문지
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• 제12권11호
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• pp.5164-5171
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• 2011

비선형 공정에 대한 퍼지 모델링은 일반적으로 주어진 데이터를 이용하여 입력 변수를 선정하고 각 입력 변수에 대한 입력 공간을 분할하여 이들 입력 변수 및 공간 분할에 의해 퍼지 규칙을 형성한다. 퍼지 규칙의 전반부는 입력 변수 선정, 공간 분할 수 및 소속 함수에 의해 동정되고 퍼지 규칙의 후반부는 간략 추론, 선형 추론에 의해 다항식 함수의 형태로 동정된다. 일반적으로 주어진 데이터를 이용한 비선형 공정에 대한 퍼지 규칙의 형성은 차원이 증가할수록 규칙의 수가 지수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해 각 입력 공간의 퍼지 분할에 의한 퍼지 규칙을 개별적으로 형성함으로써 복잡한 비선형 공정을 모델링 할 수 있다. 따라서 본 논문에서는 개별적인 입력 공간을 활용하여 퍼지 규칙을 생성한다. 퍼지 규칙의 전반부 파라미터는 입력 데이터의 최소 값과 최대 값을 이용하는 최소-최대 방법을 이용하여 동정되고, 소속 함수는 삼각형, 범종형, 사다리꼴형 소속 함수를 사용한다. 마지막으로, 비선형 공정으로는 널리 이용되는 데이터를 이용하여 시스템 특성 및 성능을 평가한다.

• 전자공학회논문지B
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• 제31B권7호
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• pp.199-210
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• 1994

Most of clustering methods usually employ a center or predefined shape of a cluster to assign the input data into the cluster. When there is no information about data set, it is impossible to predict how many clusters are to be or what shape clusters take. (the shape of clusters could not be easily represented by the center or predefined shape of clusters) Therefore, it is difficult to assign input data into a proper cluster using previous methods. In this paper, to overcome such a difficulty a cluster is to be represented as a collection of several subclusters representing boundary of the cluster. And membership functions are used to represent how much input data bllongs to subclusters. Then the position of the nearest subcluster is adaptively corrected for expansion of cluster, which the subcluster belongs to by use of a competitive learning neural network. To show the validity of the proposed method a numerical example is illustrated where FMMC(Fuzzy Min-Max Clustering) algorithm is compared with the proposed method.

• 기술경영경제학회:학술대회논문집
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• 기술경영경제학회 1997년도 제11회 하계학술발표회 논문집
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• pp.160-171
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• 1997

This paper starts out by reviewing the literature that in different ways utilizes patent data as a output of R&D investment. The main focus, however, is an analysis of time-lag between R&D input and output. To achieve this research objective, the basic data associated with the R&D input(expenditure, researchers) and output(patent, utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw output data were altered it to objective data using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag between R&D input and output is from 1 to 4 years. This result is equal to the research conclusion of the existing foreign studies. b) It was found that the time-lag of patents was longer than of utility models. c) It was showed that the time-lag of electrical-electronic, mechanical industry was longer than the chemical one.

• 한국도서관정보학회지
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• 제30권3호
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• pp.295-322
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• 1999

The purpose of this study is to identify and present the solution to the problems of KORMARC on Disc, which was produced by the National Library of Korea and is being distributed nationwide. Currently, KORMARC on Disc has reached the serious level of duplicates of input record, error on input data and noise of retrieval. Futhermore, input data is not in accordance with KORMARC Rules for Descriptive Cataloging, thus generating many problems. Of all thing, since current MARC system itself is based on manual system, it does not correspond effectively to the online environment. Accordingly, in order to elevate the quality of KORMARC database, current problems must be resolved, at the same time, korea Machine Readable Cataloging must be modified into a format, more suitable to Machine Readable environment. Consequently, the current study analyzes and identifies problems of data in KORMARC on Disc, at the same time, it examines currently used KORMARC Format and Korea machine Readable Cataloging Rules for descriptive Cataloging as to provide easier usage and guidelines for accurate data inputs.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계학술발표회 논문집(I)
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• pp.494-497
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• 2006

The objectives of this study are to develop and evaluate the Neural Network algorithm which can predict the inelastic shortening such as the creep strain and the drying shrinkage strain of reinforced concrete members using the previous test data. New learning algorithms for the prediction of creep strain and the drying shrinkage strain are proposed focusing on input layer components and a normalization method for input data and their validity is examined through several test data. In Neural Network algorithm, the main input data to be trained are the compressive strength of the concrete, volume to surface ratio, curing condition, relative humidity, and the applied load. The results show that the new algorithms proposed herein successfully predict creep strain and the drying shrinkage strain.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.17-19
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• 2000

This paper deals with Load Flow Program for client/server system. Clients play roles of input and output of the data. The client upload input-data file to the server which takes the part of the function of solving the Load Flow. The developed LF COM(Component Object Model) carry out solving the Load Flow and saving the result and the input data to DataBase. It proved the developed System to be compatible through the Case Study.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1997년도 봄 학술발표회 논문집
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• pp.89-96
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• 1997

Most civil engineering structures, such as highway bridges, towers, power plants and offshore structures suffer structural damages over their service lives caused by adverse loading such as heavy transportation loads, machine vibrations, earthquakes, wind and wave forces. Especially, if excessive load would be acted on the structure, general or partial stiffness should be degraded suddenly and service lives should be shortened eventually For realistic damage assessment of these civil structures, System Identification method using only structure dynamic response data with unknown input excitation is required and thus becoming more challenging problem. In this paper, an improved Iterative Least Squares method is proposed, which seems to be very efficient and robust method, because only the dynamic response data such as acceleration, velocity and displacement is used without input data, and no information on the modal properties is required. The efficiency and robustness of the proposed method is proved by numerical problems and real single span beam model test.

• 한국정밀공학회지
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• 제10권4호
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.149-151
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• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.319-325
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• 2001

In this note, we propose a simple fuzzy linear regression model for fuzzy input-output data based on Tanaka's approach. Then an LP-based method to derived the satisfying solution of the decision making is developed.