• 한국산학기술학회논문지
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• 제8권5호
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• pp.1279-1288
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• 2007

시간제한을 가지는 차량경로문제는 배송 및 물류에서 가장 중요한 문제 중의 하나이다. 실제적으로 고객의 서비스를 위하여 주어진 시간 안에 출발해서 배송을 끝마쳐야 한다. 본 연구는 복수 물류센터의 최적차량경로문제를 위하여 유전자 알고리즘을 이용한 2단계 접근방법을 사용한 VRP(Vehicle Routing Problem)모델의 개발이다. 1단계로 구역별로 Clustering한 것은 복수 물류센터의 문제를 쉽게 해결하기 위해 단일 물류센터의 문제로 전환하여 모델을 개발하였다. 2단계로 시간제한을 가지는 최적차량경로를 찾을 수 있는 개선된 유전자 알고리즘을 이용하여 GA-TSP(Genetic Algorithm-Traveling Salesman Problem)모델을 개발하였다. 따라서 본 연구에서 개발한 Network VRP는 ActiveX와 분산객체기술을 이용한 VRP문제의 해를 구하기 위한 전산프로그램을 개발한다.

• 한국콘텐츠학회논문지
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• 제7권6호
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• pp.22-30
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• 2007

대부분의 실세계의 시스템들, 즉 경제, 주식시장, 의료분야 등의 많은 시스템들은 동적이며 복잡한 현상을 갖는다. 이러한 특징들의 시스템을 이해하는 전형적인 방법은 시스템행위에 대한 모델을 세우고 분석하는 것이다. 본 연구에서는 실세계의 동적 시스템에서 발생되는 시계열데이터들에 대하여 최적의 클러스터를 형성하기 위한 방법을 연구한다. 먼저 클러스터 수를 결정하는 기준으로 베이지안정보기준(BIC : Bayesian Information Criterion)근사법의 활용도를 검증하고 데이터 크기와 베이지안정보기준값의 상관관계를 파악함으로 탐색 효율을 높이는 방안을 제안하며 클러스터링 과정으로 모델기반과 유사기반의 방법론을 비교 확인하여 본다. 실제의 시계열데이터(주가)에 대해 실험을 시행하였고 베이지안정보기준 근사 측도는 데이터의 크기에 따라 파티션의 사이즈를 정확히 추정하는 것을 확인하였으며 또한 유사기반의 방식보다 모델기반의 방법론이 클러스터링에서 더 나은 결과를 갖는 것을 확인하였다.

• 대한산업공학회지
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• 제40권3호
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• pp.257-266
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• 2014

The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

• 방송공학회논문지
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• 제27권6호
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• pp.923-935
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• 2022

본 논문에서는 스마트팜 시스템에서 재배 중인 식물 잎의 질병을 검출하고, 질병 유형을 분류하는 방법을 제안한다. 영상으로부터식물 잎의 컬러 정보와 질병 유형의 형태 정보를 다층 퍼셉트론(MLP) 모델을 이용하여 학습한다. 1단계에서는 입력된 영상의 컬러분포를 분석하여 질병 존재 여부를 판단한다. 1단계의 질병 존재 가능성이 높은 영상에 대하여 2단계에서는 Mean shift clustering을 이용하여 작은 영역으로 분할하고, 각 분할된 영역 단위로 컬러 정보를 추출하여 제안한 Color Network에 의하여 질병 여부를 판별한다. 컬러 분할된 영역이 Color Network에 의하여 질병으로 판별되면, 3단계에서는 그 영역의 형태 정보를 추출하여 제안한 Shape Network를 이용하여 질병의 유형을 분류한다. 사과나무 잎과 서양 양상추(Iceberg)에서 발생하는 두 가지 대분류 유형의 질병에 대하여, 제안한 기법은 작은 영역 단위로는 92.3%의 잎 질병 검출률을 보였으며, 보통 2개 이상의 질병 영역이 존재하는 한 장의 영상 단위로는 99.3% 이상의 검출률을 보였다. 본 논문에서 제안한 방법은 스마트팜 환경에서 잎 식물의 질병 여부를 조기에 발견할 수 있으며, 대상 식물에 따른 추가 학습 없이 다양한 식물과 질병 유형으로 확대 적용이 가능하다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.83-86
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• 2006

많은 유전자 정보와 그 부산물은 많은 방법을 통해 연구되어 왔다. DNA 마이크로어레이 기술의 사용은 많은 데이터를 가져왔으며, 이렇게 얻은 데이터는 기존의 연구 방법으로는 분석하기 힘들다. 본 눈문에서는 많은 양의 데이터를 처리할 수 있게 하기 위하여 K-means 클러스터링 알고리즘을 이용한 분할 클러스터링을 제안하였다. 제안한 방법을 쌀 유전자로부터 나온 마이크로어레이 데이터에 적용함으로써 제안된 클러스터링 방법의 유용성을 검증하였으며, 기존의 K-means 클러스터링 알고리즘을 적용한 결과와 비교함으로써 제안된 알고리즘의 우수성을 확인 할 수 있었다.

• International journal of advanced smart convergence
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• 제5권2호
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• pp.53-58
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• 2016

With the growing use of big data and data mining, it serves to understand how such techniques can be used to understand various relationships in the healthcare field. This study uses hierarchical methods of data analysis to explore similarities in hospitalization across several New York state counties. The study utilized methods of measuring crowding distance of data for age-specific hospitalization period. Crowding distance is defined as the longest distance, or least similarity, between urban cities. It is expected that the city of Clinton have the greatest distance, while Albany the other cities are closer because they are connected by the shortest distance to each step. Similarities were stronger across hospital stays categorized by age. Hierarchical clustering can be applied to predict the similarity of data across the 10 cities of hospitalization with the measurement of crowding distance. In order to enhance the performance of hierarchical clustering, comparison can be made across congestion distance when crowding distance is applied first through the application of converting text to an attribute vector. Measurements of similarity between two objects are dependent on the measurement method used in clustering but is distinguished from the similarity of the distance; where the smaller the distance value the more similar two things are to one other. By applying this specific technique, it is found that the distance between crowding is reduced consistently in relationship to similarity between the data increases to enhance the performance of the experiments through the application of special techniques. Furthermore, through the similarity by city hospitalization period, when the construction of hospital wards in cities, by referring to results of experiments, or predict possible will land to the extent of the size of the hospital facilities hospital stay is expected to be useful in efficiently managing the patient in a similar area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권5호
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• pp.1316-1332
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• 2012

Formation flying is an important technology that enables high cost-effective organization of outer space aircrafts. The ad-hoc wireless network based on direct-sequence ultra-wideband (DS-UWB) techniques is seen as an effective means of establishing wireless communication links between aircrafts. In this paper, based on the theory of matched filter and error bits correction, a hybrid detection algorithm is proposed for realizing multiuser detection (MUD) when the DS-UWB technique is used in the ad-hoc wireless network. The matched filter is used to generate a candidate code set which may contain several error bits. The error bits are then recognized and corrected by an novel error-bit corrector, which consists of two steps: code mapping and clustering. In the former step, based on the modified optimum MUD decision function, a novel mapping function is presented that maps the output candidate codes into a feature space for differentiating the right and wrong codes. In the latter step, the codes are clustered into the right and wrong sets by using the K-means clustering approach. Additionally, in order to prevent some right codes being wrongly classified, a sign judgment method is proposed that reduces the bit error rate (BER) of the system. Compared with the traditional detection approaches, e.g., matched filter, minimum mean square error (MMSE) and decorrelation receiver (DEC), the proposed algorithm can considerably improve the BER performance of the system because of its high probability of recognizing wrong codes. Simulation results show that the proposed algorithm can almost achieve the BER performance of the optimum MUD (OMD). Furthermore, compared with OMD, the proposed algorithm has lower computational complexity, and its BER performance is less sensitive to the number of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.190-207
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• 2015

In the combustion engineering, to decrease pollution and increase production efficiency, and to optimally keep solid burning material amount constant in a burner online, it needs a smart method to detect the amount variation of the burning materials in a high temperature environment. This paper presents an online machine vision system for automatically measuring and detecting the burning material amount inside a burner or a boiler. In the camera-protecting box of the system, a sub-system for cooling is constructed by using the cooling water circulation techqique. In addition, the key and intelligent step in the system is to detect the pile profile of the variable burning material, and the algorithm for the pile profile tracing was studied based on the combination of the gey level (color) discontinuity and similarity based image segmentation methods, the discontinuity based sub-algorithm is made on the quaternion convolution, and the similarity based sub-algorithm is designed according to the region growing with multi-scale clustering. The results of the two sub-algoritms are fused to delineate the final pile profile, and the algorithm has been tested and applied in different industrial burners and boilers. The experiements show that the proposed algorithm works satisfactorily.

• Safety and Health at Work
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• 제3권4호
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• pp.278-286
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• 2012

Objectives: Authors investigated the pattern of the rate of occupational injuries and illnesses (ROII) at the level of enterprises in order to build a network for exchange of experience and knowledge, which would contribute to workers' safety and health through safety climate of workplace. Methods: Occupational accidents were analyzed at the manufacturing work site unit. A two step clustering process for the past patterns regarding the ROII from 2001 to 2009 was investigated. The ROII patterns were categorized based on regression analysis and the patterns were further divided according to the subtle changes with Mahalanobis distance and Ward's linkage. Results: The first clustering of ROII through regression analysis showed 5 different functions; 29 work sites of the linear function, 50 sites of the quadratic function, 95 sites of the logarithm function, 62 sites of the exponential function, and 54 sites of the sine function. Fourteen clusters were created in the second clustering. There were 3 clusters in each function categorized in the first clustering except for sine function. Each cluster consisted of the work sites with similar ROII patterns, which had unique characteristics. Conclusion: The five different patterns of ROII suggest that tailored management activities should be applied to every work site. Based on these differences, the authors selected exemplary work sites and built a network to help the work sites to share information on safety climate and accident prevention measures. The causes of different patterns of ROII, building network and evaluation of this management model should be evaluated as future researches.

• Animal cells and systems
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• 제5권3호
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• pp.253-262
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• 2001

Ligand-receptor clustering event is the most important step in leukocyte adhesion and spreading on endothelial cells. Intercellular adhesion molecule-1 (ICAM-1) has been shown to enhance leukocyte adhesion, but the molecular event during the process of adhesion is unclear. To visualize the dynamics of ICAM-1 movement during adhesion, we have engineered stable Chinese hamster ovary cell lines expressing ICAM-1 fused to a green fluorescent protein (IC1_GFP/CHO) and examined them under the fluorescence microscopy. The transfection of IC1_GFP alone in these cells was sufficient to support the adhesion of K562 cells that express $\alpha$L$\beta$2 (LFA-1) integrin stimulated by CBR LFA-1/2 mAb. This phenomenon was mediated by ICAM-1-LFA-1 interactions, as an mAb that specifically inhibits ICAM-1-LFA-1 interaction (RRl/l) completely abolished the adhesion of LFA-1＊ cells to IC1_ GFP/CHO cells. We found that the characteristic of adhesion was followed almost immediately (~10 min) by the rapid accumulation of ICAM-1 on CHO cells at a tight interface between the two cells. Interestingly, ICI_GFP/CHO cells projected plasma membrane and encircled approximately half surface of LFA-1+ cells, as defined by confocal microscopy. This unusual phenomenon was also confirmed on HUVEC after adhesion of LFA-1＊ cells. Neither cytochalasin D nor 2,3-butanedione 2-monoxime an inhibitor of myosin light chain kinase blocked LFA-1-mediated ICAM-1 clustering, indicating that actin cytoskeleton and myosin-dependent contractility are not necessary for ICAM-1 clustering. Taken together, we suggest that leukocyte adhesion to endothelial cells induces specialized form of ICAM-1 clustering that is distinct from immunological synapse mediated by T cell interaction with antigen presenting cells.