• 품질경영학회지
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• 제18권1호
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• pp.48-54
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• 1990

The purpose of this study is to design effective working systems which adapt to change in human needs by developing an method which forms into optimal groups using the measure of cohesion. Two main results can be derived from the study as follows : First, the clustering method based on the entropic measure of cohesion is predominant with respect to any other methods proposed in designing the work groups, since this clustering criterion includes symmetrical relations of total work groups and the dissimilarity as well as the similarity relations of predicate value, the clustering method based on this criterion is suitable for designing the new work structure. Second, total work group is clustered as the workers who have the equal predicate value and then clustering results are produced through the combinatorial agglomerative hierarchical clustering method. This clustering method present more economic results than the method that clustering the total work group do.

• 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.

• 정보관리학회지
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• 제25권3호
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• pp.65-82
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• 2008

검색 자원의 증가와 멀티 버전 자료의 증대에 따라 목록의 기능 중 집중기능이 필요하게 되면서, FRBR(Functional Requirements for Bibliographic Records)이 제안되었으나, 기존 서지데이터를 FRBR로 변환시 불완전함과 FRBR을 구체화할 목록규칙의 부재 등 현행 목록데이터를 FRBR로 완벽히 구현하기에는 한계가 있다. FRBR을 온라인목록에 적용하기 위해서는 방대한 양의 기존 MARC 데이터를 FRBR 알고리즘으로 처리해야 하지만, 기입력된 MARC 데이터와 현행 목록규칙은 FRBR을 완벽히 지원하지 않기 때문에 FRBR로의 변환이 용이하지 않다. 따라서 목록규칙과 MARC 기술에 대한 지침이 필요하다. 또한 국내에 FRBR 시스템을 구축하기 위해서는 FRBR 알고리즘을 실재 목록시스템에 적용하여 문제점과 해결책을 모색해야 한다. 이에 본고는 FRBR을 목록에 적용하기 위해서는 FRBR을 위한 목록규칙과 MARC 데이터 기술 방안이 선행되어야 한다는 전제하에 FRBR을 위한 저작 표현형 알고리즘을 개발하고, 실재 도서관 시스템에 적용하여 저작의 집중도와 적용시의 문제점을 분석하고, FRBR의 저작 집중성을 높이기 위한 방안으로 목록규칙과 MARC 입력방안을 제안하고 이를 검증하였다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2017년도 춘계 종합학술대회 논문집
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• pp.257-258
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• 2017

Modern society tends to bring excessive labor to people and, therefore, further health management is required. In this paper, by using the clustering technique, one of machine learning methods, we try to bring out the measure of fatigue from heart rate (HR) variation during daytime work, helping people to get high-quality of healthy and calm life.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.341-349
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• 2021

Many emerging mobile ad-hoc network application communications are group-oriented. Multicast supports group-oriented applications efficiently, particularly in a mobile environment that has a limited bandwidth and limited power. Energy effectiveness along with safety are 2 key problem in MANET design. Within this paper, MANET is presented with a stable, energy-efficient clustering technique. In this proposed work advanced clustering in the networks with ticket ID cluster manager (TID-CMGR) has formed in MANET. The proposed routing scheme makes secure networking the shortest route possible. In this article, we propose a Cluster manager approach based on TICKET-ID to address energy consumption issues and reduce CH workload. TID-CMGR includes two mechanism including ticket ID controller, ticketing pool, route planning and other components. The CA (cluster agent) shall control and supervise the functions of nodes and inform to TID-CMGR. The CH conducts and transfers packets to the network nodes. As the CH energy level is depleted, CA elects the corresponding node with elevated energy values, and all new and old operations are simultaneously stored by CA at this time. A simulation trial for 20 to 100 nodes was performed to show the proposed scheme performance. The suggested approach is used to do experimental work using the NS- simulator. TIDCMGR is compared with TID BRM and PSO to calculate the utility of the work proposed. The assessment shows that the proposed TICKET-ID scheme achieves 90 percent more than other current systems.

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1211-1218
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• 2017

Monte Carlo neutron transport codes generally use the method of successive generations to converge the fission source distribution to-and then maintain it at-the fundamental mode. Recently, a phenomenon called "clustering" has been noted, which produces fission distributions that are very far from the fundamental mode. In this study, a mathematical model of clustering in Monte Carlo has been developed. The model draws on previous work for continuous-time birth-death processes, as well as methods from the field of population genetics.

• 한국전자거래학회지
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• 제27권2호
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• pp.193-204
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• 2022

스마트교통카드 데이터는 대표적인 모빌리티 데이터로 이를 이용하여 대중교통 이용행태를 분석하고 정책 개발에 활용할 수 있다. 본 논문은 이러한 연구의 하나로 전철 이용패턴을 이용하여 전철역들을 분류하는 문제를 다룬다. 전철역의 클러스터링을 다룬 기존 논문들은 이용행태 중 통행량만을 고려하였기에 본 논문은 이에 대한 보완적인 방법의 하나로 통행시간을 고려한 클러스터링을 제안한다. 각 역의 승객들을 출근 시간 출발, 출근 시간 도착, 퇴근 시간 출발, 퇴근 시간 도착 승객들로 분류한 다음 각각의 통행시간을 와이블 분포로 모형화하여 추정한 형상모수를 역의 특성값으로 정의하였다. 그리고 특성 벡터들을 K-평균 클러스터링 기법을 사용하여 클러스터링하였다. 실험결과 통행시간을 고려하여 역의 클러스터링을 수행하면 기존 연구의 클러스터링 결과와 유사한 결과가 나올 뿐만 아니라 더 세분화 된 클러스터링이 가능함을 관찰하였다.

• Transactions on Electrical and Electronic Materials
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• 제15권3호
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• pp.125-129
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• 2014

A dataset can be clustered by merging the bucket indices that come from the random projection of locality sensitive hashing functions. It should be noted that for this to work the merging interval must be calculated first. To improve the feasibility of large scale data clustering in high dimensional space we propose an enhanced Locality Sensitive Hashing Clustering Method. Firstly, multiple hashing functions are generated. Secondly, data points are projected to bucket indices. Thirdly, bucket indices are clustered to get class labels. Experimental results showed that on synthetic datasets this method achieves high accuracy at much improved cluster speeds. These attributes make it well suited to clustering data in high dimensional space.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.