• Journal of Communications and Networks
• /
• v.18 no.4
• /
• pp.580-588
• /
• 2016

Energy efficiency is the main objective in the design of a wireless sensor network (WSN). In many applications, sensing data must be transmitted from sources to a sink in a timely manner. This paper describes an investigation of the trade-off between two objectives in WSN design: minimizing energy consumption and minimizing end-to-end delay. We first propose a new distributed clustering approach to determining the best clusterhead for each cluster by considering both energy consumption and end-to-end delay requirements. Next, we propose a new energy-cost function and a new end-to-end delay function for use in an inter-cluster routing algorithm. We present a multi-hop routing algorithm for use in disseminating sensing data from clusterheads to a sink at the minimum energy cost subject to an end-to-end delay constraint. The results of a simulation are consistent with our theoretical analysis results and show that our proposed performs much better than similar protocols in terms of energy consumption and end-to-end delay.

• Journal of the Korean Academic Society of Industrial Cluster
• /
• v.3 no.1
• /
• pp.26-38
• /
• 2009

Many economic development practitioners view cluster theory and analysis as constituting a general approach to strategy making in economic development, which may lead them to prioritize policy and planning interventions that cannot address the actual development challenges in their cities and regions. This paper discusses the distinction between strategy formation and strategic planning, where the latter is the programming of development strategies that are identified through a blend of experience, intuition, and analysis. Cluster theories and analytical tools can provide useful informational inputs into a strategy making effort and they can also be helpful for programming specific interventions (i.e., strategic planning). However, they should not be used as the exclusive or even predominant framework for filtering information about the competitive advantages of a region or for formulating strategy. To do so forces strategy making into a conceptual box defined by only one highly stylized theory of regional growth and development.

• Korean Journal of Child Studies
• /
• v.32 no.1
• /
• pp.87-101
• /
• 2011

This study employed a child-centered approach in the examination of patterns of preschooler's learning behaviors. A hierarchical cluster analysis was employed in order to discern a meaningful typology of such behavior. The subjects consisted of 232 children (117 boys, 106 girls) and their mothers from 6 kindergartens and 6 day care centers. The results of this study were as follows. The cluster analysis yielded five types of learning behaviors; the competent type, the average type, the low attention/persistence type, the low motivation -attitude type, and the deficient type. The most consistent level differences among these types appeared to lie in distinctions among the average Attention/Persistence scores. The composition of the cluster types, including both the age and gender of the children, was ascertained. Our results indicated that preschool learning behavior types could be seen to differentially relate to children's self-regulation, cognitive styles, problem behaviors, and the level of maternal involvement. It was revealed that a child's characteristics was more important than maternal involvement and beliefs. As there were more girls and older children in the learning type, this type was seen to be more competent.

• Journal of Korean Society of Forest Science
• /
• v.81 no.3
• /
• pp.203-213
• /
• 1992

The purpose of this study is to segment recreationists into groups which are homogeneous with respect to their spending patterns and trip characteristics. Date were derived from a larger study aimed at developing nationally representative expenditure profiles for recreation visitors to Corps of Engineers projects. Segmentation of these data reduces variance and helps to identify distinctive final demand vectors for input - output application. A - priori and cluster analysis approaches for identifying segments are compared. The a - priori segmentation approach identified 12 segments and the cluster analysis approach identified 3 segments. The 3 nonresident clusters - labeled "day use", "overnight", and "overnight camping" - show lower mean squares within groups than the a - priori segments on almost all nonresident spending categories with an exception of boating expenses. For the Corps of Engineers, implications of these findings for the estimation of economic impacts are discussed.

• Journal of the Korean Society of Safety
• /
• v.37 no.2
• /
• pp.18-27
• /
• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.

• Journal of the East Asian Society of Dietary Life
• /
• v.24 no.6
• /
• pp.915-923
• /
• 2014

This study investigated organic coffee choice motives from a coffee shop market segmentation perspective in order to understand the potential importance they may have upon attitudes and behavioral intentions to buy organic coffee. A factor-cluster segmentation approach was used for this study. An exploratory factor analysis identified five organic coffee choice motives: 'Sensory', 'Environment', 'Trust', 'Health' and 'Price'. Based upon these five choice motives, cluster analyses classified all respondents into three homogeneous subgroups: 'Highly motivated', 'Moderately motivated' and 'Unmotivated'. Analysis of variance tests indicated that attitudes and intentions to purchase organic coffee were significantly different among the three clusters. In particular, two cluster groups representing 'Highly motivated' and 'Moderately motivated' were found to offer the most utility for further organic coffee market segmentation research. Especially, due to perceptions about high price premium of organic coffee, the 'Moderately motivated' group had higher positive attitudes, although, their intentions to buy organic coffee were not higher than those of the 'Unmotivated' cluster. Findings support previous research propositions that high price could be the strongest barrier for people to purchase organic products including the organic coffee business context. This will assist to market and promote pricing strategies for caf$\acute{e}$s and restaurants to optimize organic coffee sales revenue. Implications for all cluster groups regarding unique socio-demographic characteristics and behavioral intentions are discussed. Organic coffee marketers can apply these findings towards the development of effective target market strategies.

• The Journal of Information Systems
• /
• v.28 no.3
• /
• pp.105-121
• /
• 2019

Purpose One of the reasons for the expansion of information systems in the enterprise is the increased efficiency of data analysis. In particular, the rapidly increasing data types which are complex and unstructured such as video, voice, images, and conversations in and out of social networks. The purpose of this study is the customer needs analysis from customer voices, ie, text data, in the web environment.. Design/methodology/approach As previous study results, the word frequency of the sentence is extracted as a word that interprets the sentence has better affects than frequency analysis. In this study, we applied the TF-IDF method, which extracts important keywords in real sentences, not the TF method, which is a word extraction technique that expresses sentences with simple frequency only, in Korean language research. We visualized the two techniques by cluster analysis and describe the difference. Findings TF technique and TF-IDF technique are applied for Korean natural language processing, the research showed the value from frequency analysis technique to semantic analysis and it is expected to change the technique by Korean language processing researcher.

• Journal of Fashion Business
• /
• v.8 no.2
• /
• pp.26-41
• /
• 2004

This study aims to compare and analyze somatotype characteristics in clothing ergonomics as well as Sasang (Oriental physical constitution type classification into 4 kinds: taeyang, taeeum, soyang, soeum) medicine. The subjects were women collegians in their 20s. As a result, a new approach was made in somatotype classification. The following are conclusions: 1. The results of body measurement of the subjects belonged to 1 in most items when they were compared with the average records of female adults in their 20s in the national standard physique report. Thus, the subjects belonged to the average somatotype. 2. According to Sasang physical constitution classification, no subjects belonged to taeyang-type. Taeeum type (28.4%), had lower-body development greatest height and even development in width, thickness and girth. Soeum-type(37.8%) had the smallest physique. Soyang-type(33.8%) showed small values in height but great values in width, thickness and girth. 3. The factor analysis revealed 5 factors of somatotype characteristics: lower body factors including body weight, upper body factors, height factors including stature, belly width factors including waist and belly, and other factors comprising ankle and head size. 4. A cluster analysis by way of factor scores resulted in 3 types: cluster 1 44.6%, biggest values, largest somatotypes; cluster 2 17.6%, average somatotypes; cluster 3 tiniest somatotypes in most items. 5. In the crosstabs analysis, taeeum-type (57.6%) appeared a lot in cluster 1, soyang-type (76.9%) appeared most in cluster 2, and soeum-type (69.9%) was mostly seen in cluster 3. To sum up, the somatotype analysis of clothing ergonomics had something to do with constitution classification suggested in Sasang medicine. For clear justification, more systematic and scientific research should be followed with even more diverse subjects in sex and age.

• Journal of Korea Multimedia Society
• /
• v.20 no.12
• /
• pp.1960-1969
• /
• 2017

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

• The Korean Journal of Coaching Psychology
• /
• v.7 no.3
• /
• pp.109-125
• /
• 2023

This study aimed to investigate the influence of power distance on organizational outcome variables from the perspective of employee-organizational fit. Through cluster analysis, we sought to identify the subgroups that exist based on the combination of employees' power distance and organizational power distance. Additionally, we examined whether differences exist among these subgroups in terms of positive and negative organizational outcomes. A total of 398 participants were included in the study, and three distinct clusters were identified through cluster analysis. Cluster 1 comprised individuals with low power distance among employees and high power distance within the organization(LH), Cluster 2 consisted of individuals with high power distance in both employees and organizations(HH), and Cluster 3 represented individuals with significantly higher power distance among employees compared to their respective organizations(HL). When analyzing the differences between these three subgroups in relation to organizational outcomes, no significant differences were found in positive work affects. Overall, the LH group exhibited the most favorable organizational results, while the HH group displayed the most negative organizational outcomes. In light of these findings, we discussed the academic and practical implications of this study, as well as its limitations.