• 한국콘텐츠학회논문지
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• 제10권2호
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• pp.14-24
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• 2010

무선 센서 네트워크에서는 센서 노드의 제한된 자원으로 동작하기 때문에 클러스터 기반의 라우팅 방법을 통해 네트워크의 수명을 향상시키고 있다. 본 논문에서는 클러스터 기반의 라우팅 기법이 가진 에너지 효율을 최대화하기 위해 방향, 거리, 밀도, 그리고 잔여 에너지량을 사용하고자 한다. 상위 클러스터 헤드의 방향 정보를 이용하여 새로운 클러스터 헤드를 자율적으로 선출할 때 고립 노드의 발생 빈도를 최소화하고, 거리 정보를 이용하여 셋업 단계에서 새로운 클러스터와 이전의 클러스터 모두에 포함되어 정보를 갱신할 필요가 없는 센서 노드들을 슬립 모드로 전환하여 에너지 소모를 줄이며, 단지 새로 가입되는 센서노드만 정보 교환함으로써 불필요한 에너지 소비를 줄이고자 한다. 뿐만 아니라, 클러스터 기반의 라우팅 기법에서 내부나 외부의 공격에 강한 키 관리 기법을 통해 안전하고 에너지 효율적인 통신이 가능하도록하여 전체적인 네트워크 효율을 높이고자 한다. 따라서 전체 네트워크내의 멤버들에게 클러스터 헤드가 될 수 있는 안전하고 균등한 기회를 주고자 하는 클러스터 헤드 선출 기법을 제안한다.

• 디지털산업정보학회논문지
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• 제12권2호
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• pp.7-14
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• 2016

In this paper presents a low power algorithm using a feasible cluster generation method considered glitch. The proposed algorithm is a method for reducing power consumption of a given circuit. The algorithm consists of a feasible cluster generation process and glitches removal process. So that glitches are not generated for the node to which the switching operation occurs most frequently in order to reduce the power consumption is a method for generating a feasible cluster. A feasible cluster generation process consisted of a node value set, dividing the node, the node aligned with the feasible cluster generation. A feasible cluster generation procedure is produced from the highest number of nodes in the output. When exceeding the number of OR-terms of the inputs of the selected node CLB prevents the signal path is varied by the evenly divided. If there are nodes with the same number of outputs selected by the first highest number of nodes in the input produces a feasible cluster. Glitch removal process removes glitches through the path balancing in the same manner as [5]. Experimental results were compared with the proposed algorithm [5]. Number of blocks has been increased by 5%, the power consumption was reduced by 3%.

• 한국멀티미디어학회논문지
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• 제17권10호
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• pp.1160-1170
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• 2014

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.435-437
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• 2016

하둡 분산 파일시스템(HDFS)는 빅데이터의 병렬 분산 처리를 위해 다수의 노드에 데이터를 중복 저장하는 파일시스템이다. HDFS의 분산 노드 클러스터는 수천 개 이상의 규모 확장성을 갖추고 있으나 빅데이터 처리를 위한 전용 하드웨어를 가정하고 있으며, 기존의 기업 및 병원에서 사용하고 있는 다양한 유휴 전산 자원을 고려하지는 못하는 문제가 있다. 본 논문에서는 기관 내 존재하는 다양한 유휴 전산 자원을 필요에 따라 동적으로 HDFS에 추가함으로써 빅데이터 저장 및 분석 성능을 향상시킬 수 있는 동적 클러스터 관리 기법을 제시한다.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.53-64
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• 2019

The purpose of this study is to find out the fast delivery route that several drones return a truck again after departing from it for delivery locations at each cluster while the truck goes through the cluster composed of several delivery locations. The main issue is to reduce the total delivery time composed of the delivery time by relatively slow trucks via clusters and the sum of maximum delivery times by relatively fast drones in each cluster. To solve this problem, we use a three-step heuristic approach. First, we cluster the nearby delivery locations with minimal number of clusters satisfying a constraint of drone flight distance to set delivery paths for drones in each cluster. Second, we set an optimal delivery route for a truck through centers of the clusters using the TSP model. Finally, we find out the moved centers of clusters while maintaining the delivery paths for the truck and drones and satisfying the constraint of drone flight. distance in the two-dimensional region to reduce the total delivery time. In order to analyze the effect of this study model according to the change of the number of delivery locations, we developed a R-based simulation prototype and compared the relative efficiency, and performed paired t-test between TSP model and the cluster-based models. This study showed its excellence through this experimentation.

• Journal of Information Technology Applications and Management
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• 제12권3호
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• 한국경제지리학회지
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• 제14권1호
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• pp.52-70
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• 2011

산업클러스터는 산, 학, 연 네트워크를 통한 지식의 공유와 교류를 통해 기업 혁신의 촉진을 추구한다는 점에서 기본적으로 지식경영의 개념과 연관돼 있다. 이런 이유로 최근 기존의 기업 또는 독립된 조직 단위에서만 이루어져 온 지식경영이 산업클러스터 단위에서도 적용될 필요가 있다는 주장이 나오고 있다. 본 연구는 이런 연구의 흐름과 실무적 요구를 감안해 산업클러스터에서 지식경영이 효과적으로 이루어지려면 어떤 특성이 반영될 필요가 있으며 효과적 지식경영의 적용을 위한 과제는 무엇인지를 탐색해 본다. 연구 목적의 달성을 위해 기존 연구를 토대로 관련 개념을 정의해 보고 심층면접과 사례연구를 통해 연구문제의 답을 탐색해 본다.

• 한국안전학회지
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• 제33권6호
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• pp.128-135
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• 2018

Currently, the government selects products that are likely to cause harm to consumers as safety management targets and classifies them into three types: safety certification, safety confirmation, and supplier conformity verification. In addition, the government conducts safety surveys on products in circulation or accident products, and recalls products that are of great concern to consumer risks. In this paper, we have developed RAP (Risk Assessment method based on Probability), which is a probability based product risk assessment method, for the classification of safety management type of product and safety investigation, and have shown an application example. In this process, information is used for the CISS (Consumer Injury Surveillance System) of the Korean Consumer Agency. In addition, we apply the cluster analysis to classify the current supervised children products into three groups. Then, we confirm the effectiveness of RAP by comparing the result of RAP application, cluster analysis result and current safety management classification type. Also, we recognize the need to review the current safety management classification criteria for classifying products into three types.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
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• pp.13-20
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• 1994

We consider the satellite cluster scheduling problem which is one of the most interesting problems in satellite communication scheduling area. This problem is known to be NP-complete and a couple of heuristic algorithms had been developed. In this paper, we suggest another algorithm for this problem which has the same computational complexity as the best existing one and provides much better solution quality. Extensive computational simulation results are reported.