• Korean Journal of Environment and Ecology
• /
• v.6 no.2
• /
• pp.127-133
• /
• 1993

To investigate the structure and the conservation strategy of natural forest at Dosolbong area in Mt. Sobaek, 20 plots(400$m^2$) set up with random sampling method. Three groups were classified by cluster analysis. High positive correlations was proved between Acer pseudo-sieboldianum and Tilia amurensis, Weigela subsessilis, Corylus heterophylla var. thunbergii and Stephanandra incisa, Lespedeza bicolor and Styrax obassia, and high negative correlations was proved between Rhododendron schlippenbachii and Symplocos chinensis, Carpinus cordata. Species diversity(H') of investigated area was calculated 2.2521-2.3772. Leontopodium japonicum community at Dosolbong area in Mt. Sobaek should be conserved.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.157-166
• /
• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Journal of Korea Multimedia Society
• /
• v.12 no.11
• /
• pp.1609-1622
• /
• 2009

In wireless sensor networks, a clustering protocol is an efficient method to prolong network lifetime. In general, it results in more energy consumption at the cluster-head node. Hence, such a protocol must changes the cluster formation and cluster-head node in each round to prolong the network lifetime. But, this method also causes large amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new clustering algorithm. In this algorithm, we exclude duplicated data of adjacent nodes and transmits the threshold value. We define a group as the sensor nodes within close proximity of each other. In a group, a node senses and transmits data at a time on the round-robin basis. In a view of whole network, group is treated as one node. During the setup phase of a round, intra clusters are formed first and then they are re-clustered(network cluster) by choosing cluster-heads(group). In the group with a cluster-head, every member node plays the role of cluster-head on the round-robin basis. Hence, we can lengthen periodic round by a factor of group size. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.

• Industrial Engineering and Management Systems
• /
• v.8 no.2
• /
• pp.101-108
• /
• 2009

Biclustering is a method to extract subsets of objects and features from a dataset which are characterized in some way. In contrast to traditional clustering algorithms which group objects similar in a whole feature set, biclustering methods find groups of objects which have similar values or patterns in some features. Both in clustering and biclustering, validating how much the result is informative or reliable is a very important task. Whereas validation methods of cluster solutions have been studied actively, there are only few measures to validate bicluster solutions. Furthermore, the existing validation methods of bicluster solutions have some critical problems to be used in general cases. In this paper, we review several well-known validation measures for cluster and bicluster solutions and discuss their limitations. Then, we propose several improved validation indices as modified versions of existing ones.

• The Research Journal of the Costume Culture
• /
• v.6 no.4
• /
• pp.120-135
• /
• 1998

The purpose of this study were to develop the taxonomy of apparel buying decision approaches and to identify the relationships between the apparel buying decision approaches and the situational characteristics. Data were collected via a questionnaire developed on the previous studies and the focus interview from 425 female college students living at Pusan, and analyzed by Factor Analysis, Cluster Analysis, Analysis of Variance, and Discriminant Analysis. Results indicated that apparel buying decision approaches consisted of eight dimensions and situational characteristics of affecting a particular apparel buying decision approaches were composed of three or five factors. The four types of apparel buying decision approaches were derived by Cluster Analysis and ANOVA: Recreational Shoppers, Brand Conscious Shoppers, Quality Conscious Shoppers, and Apathetic Shoppers. The findings revealed some patterns that were similar to previous studies and was useful to marketing managers who can view their customer segments in terms of the types in the taxonomy. Further, it provided a tool by which sales representatives can develop adaptive selling approaches based on a small set of buying situation and corresponding apparel buying decision approaches.

• Journal of KIISE:Software and Applications
• /
• v.31 no.11
• /
• pp.1519-1533
• /
• 2004

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively importance degree. Furthermore, we perform a cluster retrieval to reflect the user's Query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. We have proven the performance of a proposed re-ranking model based on the similarity union of the fuzzy retrieval model and the document cluster retrieval model.

• The KIPS Transactions:PartA
• /
• v.10A no.1
• /
• pp.15-24
• /
• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Journal of the Korea Safety Management & Science
• /
• v.13 no.3
• /
• pp.115-121
• /
• 2011

Incheon Port has many advantages for import of log and timber for furniture. More than 50% of lumber which was imported is through Incheon Port. However, most companies move and set up their business by taking lease of workshop. Because of expensive cost of transportation, it is suggested to construct a lumber Industrial district in the North Port in Incheon. By researching the national plans about the North Port and Incheon Ports, an adequate acquaintance of lumber industry in Incheon has been realized. ills study conducted the economic effect analysis for lumber industry clustering and the necessity of cluster composition is derived. Then effectiveness analysis for lumber industry cluster composition is sequentially operated.

• The Bulletin of The Korean Astronomical Society
• /
• v.36 no.2
• /
• pp.109.1-109.1
• /
• 2011

Shock waves are ubiquitous in astrophysical environments. In particular, shocks formed by merger of subclumps, infall of matter and internal flow motion in intracluster media (ICMs) and cluster outskirts are relatively weak with Mach number M ${\lesssim}$ a few. At such weak shocks, it has been believed that the diffusive shock acceleration (DSA) of cosmic rays is rather inefficient. Yet, the presence of nonthermal phenomena, such as radio halos and relics, suggests that contrary to the expectation, DSA as well as magnetic field amplification should operate at weak shocks in cluster environments. We recently initiated a study of weak, collisionless, astrophysical shocks using a PIC(Particle-in-Cell) code. The PIC code describes the motion of electron and ion particles under the electromagnetic field which is represented in grid zones. Here, we present a preliminary work of one-dimensional simulations. We show how shocks are set up as the turbulent electromagnetic field is developed in the shock transition layer, and discuss the implication on DSA and magnetic field amplification.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.8 no.2
• /
• pp.27-43
• /
• 1983

The purpose of this paper is to analyze the structure and properties of the patterns by extracting the general patterns on socio-economic development from huge data by statistical analysis. We collected data concerning socio-logical, economical and technological aspects. Indicators used for this study amounted to a total of 136, and among them 39 were on science & technology. What is more, these indicators were set up with the resent data for the first half of the 1970's mainly, and 141 nations were selected as the sample. Some rinkage patterns to the total indicators were abstracted by cluster analysis based on the correlation matrix. And some rinkage patterns to the total countries were educed by applying cluster analysis of centroid method to the respective indicators.