• Journal of Korean Society for Quality Management
• /
• v.18 no.1
• /
• pp.48-54
• /
• 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
• /
• v.3 no.4
• /
• pp.278-286
• /
• 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.

• Journal of the Korean Society for information Management
• /
• v.25 no.3
• /
• pp.65-82
• /
• 2008

The purposes of this study are to explore FRBR utilities such as work clustering and expression clustering and problems of application of the FRBR by developing work and expression clustering algorithm and implementing it into cataloging system, and to suggest new cataloging rules for FRBR and guideline of MARC description to improve FRBR work clustering. FRBR was suggested by necessitation of collocation function of bibliographic records according to increase of searching materials and multi-version materials, but FRBRization has some problems such as imperfect conversion of bibliographic records to FRBR records and inappropriateness of current cataloging rules for FRBR. Bibliographic records must be processed by FRBR algorithm to construct FRBRized system, but bibliographic records and current cataloging rules couldn't perfectly support FRBRization. Therefore cataloging rules and guidelines of MARC description for FRBR are needed. For constructing FRBRized cataloging system in Korea, it is needed to find problems and solution through FRBR practical application such as developing FRBR algorithm and applying it to cataloging records.

• Proceedings of the Korea Contents Association Conference
• /
• 2017.05a
• /
• pp.257-258
• /
• 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
• /
• v.22 no.10
• /
• pp.177-182
• /
• 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
• /
• v.21 no.7
• /
• pp.341-349
• /
• 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
• /
• v.49 no.6
• /
• pp.1211-1218
• /
• 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.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.193-204
• /
• 2022

Smart card data is representative mobility data and can be used for policy development by analyzing public transportation usage behavior. This paper deals with the problem of classifying metro stations using metro usage patterns as one of these studies. Since the previous papers dealing with clustering of metro stations only considered traffic among usage behaviors, this paper proposes clustering considering traffic time as one of the complementary methods. Passengers at each station were classified into passengers arriving at work time, arriving at quitting time, leaving at work time, and leaving at quitting time, and then the estimated shape parameter was defined as the characteristic value of the station by modeling each transit time to Weibull distribution. And the characteristic vectors were clustered using the K-means clustering technique. As a result of the experiment, it was observed that station clustering considering pass time is not only similar to the clustering results of previous studies, but also enables more granular clustering.

• Transactions on Electrical and Electronic Materials
• /
• v.15 no.3
• /
• pp.125-129
• /
• 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
• /
• v.8 no.2
• /
• pp.116-120
• /
• 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.