• International Journal of Safety
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• 제11권1호
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• pp.26-32
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• 2012

This paper introduces a feasibility evaluation method for prognosis systems based on an empirical model in nuclear power plants. By exploiting the dynamical signature characterized by abnormal phenomena, the prognosis technique can be applied to detect the plant abnormal states prior to an unexpected plant trip. Early $operator^{\circ}{\emptyset}s$ awareness can extend available time for operation action; therefore, unexpected plant trip and time-consuming maintenance can be reduced. For the practical application in nuclear power plant, it is important not only to enhance the advantages of prognosis systems, but also to quantify the negative impact in prognosis, e.g., uncertainty. In order to apply these prognosis systems to real nuclear power plants, it is necessary to conduct a feasibility evaluation; the evaluation consists of 4 steps (: the development of an evaluation method, the development of selection criteria for the abnormal state, acquisition and signal processing, and an evaluation experiment). In this paper, we introduce the feasibility evaluation method and propose further study points for applying prognosis systems from KHNP's experiences in testing some prognosis technologies available in the market.

• 한국경영과학회지
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• 제26권2호
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• 대한의용생체공학회:의공학회지
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• 제12권2호
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• pp.89-98
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• 1991

This study is concerned on the automation for cell diagnosis which has better objectivity and speed of test than human beings. Diagnosis is on the basis of shape change of abnormal Cells. Used parameters are nucleus area, nucleus perimeter, nucleus shape, cytoplasm area, nucleus/cytoplsm ratio, which was obtained using image processing technics. A new mode method is proposed on the automatic threshold selection for superior process time compared with Otsu's. Contour of the cytoplasm of abnormal cell is obtained using me- dian filter and sorel operator. The mask to get only original shape of abnormal cells is formed uslng the contour filling algorithm. In the result the normal cells are separated from the abnormal cells and the abnormal cells can be distinguished through screwing of abnormal cell's image with reference data to judge abnormal cells. Owing to this study the number of inspections which the pathologists should examine will be decreased and the time for inspection will be shortened.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• 한국정밀공학회지
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• 제15권2호
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• pp.162-169
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• 1998

Lacking for the skilled grinding operator possessed of the experiential knowledges in machine shop, there is the just requirement which includes the establishment of the optimal grinding conditions. Accordingly, we attemt to develope the selection system of optimal grinding conditions such as workpiece velocity, depth of cut and wheel velocity and to add the trouble shooting system by means of the neural network. Those systems are robust to the each machine error and environmental unstable state. In addition. we produce the loaming process that is progressed with additional data modified by skilled operators, and excluding is advanced to similarity of input data.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1992년도 추계학술대회 논문집
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• pp.121-125
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• 1992

Grinding is considered as a very effective machining technology to attain the good surface quality of a components. However, the grinding operation still needs the skill and experience of an operator because of a lack of scientific and engineering principles. Therefore, recent development focus on expert systems which deal with domain specific knowledge in order to solve this problem. This paper describes an expert system for selecting the optimism grinding wheel by using the fuzzy reasoning and operation conditions in grinding. By the system unskilled workers will be able to make use of the knowledge and experience of skilled workers. The system is consist of programs that, (1) selection of the best wheel form among avairable wheel by using fuzzy reasoning, (2) determine wheel depth of cut, feed and wheel speed for grinding, work speed, and dressing condition. The developed system perform in using the CLIPS as a software tool and run under the IBM PC/AT as a hardware tool.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• 정보관리연구
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• 제19권1호
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• pp.30-49
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• 1988

성공적(成功的)인 바코드시스템을 구축(構築)하기 위해서는 바코드심볼, 미디어, 프린터, 오퍼레이터, 스캐너, 디코더 등에 대한 면밀한 검토가 선행되어야 한다. 본교(本橋)에서는 바코드시스템 구성요소(構成要素)의 종류(種類), 특징(特徵), 평가(評價), 선택기준(選擇基準) 등과 바코드의 적용방법(適用方法)에 관하여 도서관업무(圖書館業務)를 중심(中心)으로 고찰(考察)하였다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1998년도 춘계공동학술대회 발표논문집 IMF시대의정보화 추진전략
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• pp.213-219
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• 1998

Genetic Algorithms(GAs ; 유전자 알고리즘)은 자연적 선택(natural selection)의 유전적인 메카니즘에 기초한 탐색 알고리즘(search algo-rithms)이다. GA는 세대(generation)를 거듭함에 따라 어떤 최적화화하는 해에 수렴해가는 탐색 알고리즘으로 전세대의 우수개체로부터 새로운 세대의 개체들이 집합이 형성되는 과정을 이용한 탐색 알고리즘이다. GA에 대한 최근의 활발한 연구와 많은 관심은 주로 기존의 기법이 특정 영역의 지식을 많이 필요로하는데 비해서 GA는 효율적인 영역독립 탐색경험들의 집합을 제공하여 최적해를 얻는 기법으로서 전역함수 최적화와 NP 등의 문제에 유용하다는 연구결과가 제시되고 있기 때문이다. 본 연구에서는 GA에 대한 명확한 이해와 세대의 형성 , 개체를 선택하기 위한 타당한 연산자(operator)에 관한 내용을 고찰하고, GA가 언제, 어떻게 사용되는가에 대해 응용사례를 중심으로 GA의 향후 연구방향에 대해 논의하고 GA가 앞으로 어떤 분야에서 어떻게 발전해 나가야 할 지에 대한 과제에 대해 논의한다.

• 한국건설관리학회논문집
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• 제8권6호
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• pp.235-245
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• 2007

This research study presents the development and application of an Elitist Genetic Algorithm (Elitist GA) for solving the resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. Main features of the developed algorithm are that the elitist roulette selection operator is developed to preserve the best individual solution for the next generation so as to obtain the improved solution, and that parallel schedule generation scheme is used to generate a feasible solution to the problem. The experimental results on standard problem sets indicate that the proposed algorithm not only produces reasonably good solutions to the problems over the heuristic method and other GA, but also can find the optimal and/or near optimal solutions for the large-sized problems with multiple resources within a reasonable amount of time that will be applicable to the construction industry. This paper will help researchers and/or practitioners in the construction project scheduling software area with alternative means to find the optimal schedules by utilizing the advantages of the Elitist GA.