• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.12
• /
• pp.3963-3970
• /
• 2009

This study proposes a method to evaluate the level of 'soft-hard' of color quantitatively by evaluating the shape with edge sharpness automatically and by evaluating color in the color image scale in a drawing in art therapy using a computer. The dependent variable is the rank for the color experts to rate the level of 'soft-hard'. The mean and standard deviation of Value(V), and Chroma(C), colors, main color, clusters, length of edge, and sharp line rate of edge are considered as the independent variable. The appropriate independent variables to explain the dependent variable are selected through the step wise regression analysis. The inter-rater reliability of two raters is checked and the validity of developed system is verified by the rank correlations coefficient between the ranks of rater's and system's. This system can be used to evaluate of the shape or color in a drawing objectively and quantitatively for art therapy assessment, and to give the useful information to the fashion, textile, interior industry as well as color psychology and art therapy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.12
• /
• pp.3675-3681
• /
• 2009

One of the most important factors in the lifetime of WSN(Wireless Sensor Network) is the limited resources and static control problem of the sensor nodes. In order to achieve energy efficiency and network utilities, sensor nodes can be well organized into one cluster and selected head node and normal node by dynamic conditions. Various clustering algorithms have been proposed as an efficient way to organize method based on LEACH algorithm. In this paper, we propose an efficient clustering algorithm using information entropy theory based on LEACH algorithm, which is able to recognize environmental differences according to changes from data of sensor nodes. To measure and analyze the changes of clusters, we simply compute the entropy of sensor data and applied it to probability based clustering algorithm. In experiments, we simulate the proposed method and LEACH algorithm. We have shown that our data balanced and energy efficient scheme, has high energy efficiency and network lifetime in two conditions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.1
• /
• pp.459-466
• /
• 2011

In this paper, we proposed a data mining framework for the management of alerts in order to improve the performance of the intrusion detection systems. The proposed alert data mining framework performs alert correlation analysis by using mining tasks such as axis-based association rule, axis-based frequent episodes and order-based clustering. It also provides the capability of classify false alarms in order to reduce false alarms. We also analyzed the characteristics of the proposed system through the implementation and evaluation of the proposed system. The proposed alert data mining framework performs not only the alert correlation analysis but also the false alarm classification. The alert data mining framework can find out the unknown patterns of the alerts. It also can be applied to predict attacks in progress and to understand logical steps and strategies behind series of attacks using sequences of clusters and to classify false alerts from intrusion detection system. The final rules that were generated by alert data mining framework can be used to the real time response of the intrusion detection system.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.11
• /
• pp.453-462
• /
• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.177-189
• /
• 2017

The aim of this study was to understand and describe the awareness of the emotional labor of nursing college students in a graduation year. The participants were eleven students in nursing colleges. The data were collected from September 5, 2016 to November 25 through in-depth interviews until it was saturated. All interviews were recorded and transcribed as they were spoken. Colaizzi's phenomenological method was used for data analysis. In this study, twenty-one themes, ten theme clusters and five categories were generated. The five categories consisted of 'Confused by irrational circumstances,' 'Skepticism on nursing occupation,' 'Empathy for the nurse's difficult situation,' 'Learning nurses' words and behavior', and 'Preparing for the future.' These results will contribute to the qualitative improvement of nursing practice education by providing the grounds for an effective educational strategy development that manages the emotional labor of Nursing students from clinical practice. In-depth studies on the experience of nursing students' emotional labor and studies on various factors affecting the awareness of emotional labor in nursing students and problems will be required.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.4
• /
• pp.265-275
• /
• 2018

This study is a qualitative investigation that used Colaizzi's (1978) phenomenological method to evaluate the meaning and nature of the experience of dialysis of eight long-term (>30 years) hemodialysis patients with chronic renal failure. Data were collected from February 27, 2017 to May 30, 2017 by in-depth interviews. Respondents were then divided into three categories, 'entirely different life', 'getting back up again' and 'focusing on survival' with nine theme clusters and 22 themes. In general, patients initially experienced an entirely different life and overcame a difficult situation when beginning dialysis, then came to know methods for self-care, shared the experience of dialysis, and focused on survival while receiving dialysis over the long-term. Development of variety of education methods depending on long-term experience of dialysis and nursing care intervention to enable chronic patients to adapt to dialysis and continue their own self-management is necessary.

• Journal of Environmental Science International
• /
• v.25 no.6
• /
• pp.749-756
• /
• 2016

This study shows how the growth of the top part of plants cultivated using the single-row strawberry method, with 12 cm plant spacing, as well as that of plants cultivated through conventional planting, is characterized by the presence of many leaves in the first flower cluster harvest. The leaf area and crown diameter were the largest in the 12 cm spacing method. The hight top fresh weight (59.2 g) was detected wen the 12 cm spacing method was used followed by conventional planting and, 9 cm and 6 cm spacing method. The K and Ca contents in the first flower cluster were the highest when the 12 cm spacing method (2.0% and 2.1%, respectively) and conventional planting, (0.42% and 0.86%, respectively) were used, and these values were significantly higher than the K and Ca contents obtained using the other two methods. The N, P, Mg, Fe, and B contents show no significant differences across the planting methods. The sugar content of the first flower cluster fruits was the highest when the 12 cm spacing method was used, while the sugar content of the fourth flower cluster fruits was highest after conventional planting. Firmness was the highest in the first, third, and fourth flower clusters after conventional planting, while no significant differences were observed for the 6 cm, 9 cm, and 12 cm spacing methods. A yield of 25 g or above during November to December was observed to be the highest when the 12 cm spacing method was used, while a yield of 10-16 g was the highest when both the 9 cm and 12 cm spacing methods werw used. The yield of products in January-April was the highest when the 12 cm spacing and conventional planting methods were used, and total product yield was also the highest for these methods. A significant portion of non-marketable products (39 g) was obtained when the conventional planting method was used.

• International Commerce and Information Review
• /
• v.16 no.3
• /
• pp.275-304
• /
• 2014

Due to the rapid progress of globalization, fierce international competition, a declining population, low birth rate and aging population, deficit in a difficult situation at home and abroad, Japan's local governments expand internal and external alliances and partnerships to maximize the economic benefits to the region seeking to enable in the region have efficient allocation of human and material resources through industry support, funding, and administrative efficiency for the purpose of speeding up the formation of a mega regional economies. Union of Kansai Government implements, especially in the wide area of industrial clusters, economic policy need to comprehensive planning and growth strategy such as adjusting the growth strategy attempts to promote the local economy and to enable investment planning and coordination within the current mega regions, These roles are further improving and upgrading their importance of mega regional phase in Japan's regional economic policy.

• Journal of Environmental Science International
• /
• v.20 no.6
• /
• pp.737-753
• /
• 2011

The typical characteristics of seasonal winds were studied around the Pohang using two-stage (average linkage then k-means) clustering technique based on u- and v-component wind at 850 hpa from 2004 to 2006 (obtained the Pohang station) and a high-resolution (0.5 km grid for the finest domain) WRF-UCM model along with an up-to-date detailed land use data during the most predominant pattern in each season. The clustering analysis identified statistically distinct wind patterns (7, 4, 5, and 3 clusters) representing each spring, summer, fall, and winter. During the spring, the prevailed pattern (80 days) showed weak upper northwesterly flow and late sea-breeze. Especially at night, land-breeze developed along the shoreline was converged around Yeongil Bay. The representative pattern (92 days) in summer was weak upper southerly flow and intensified sea-breeze combined with sea surface wind. In addition, convergence zone between the large scale background flow and well-developed land-breeze was transported around inland (industrial and residential areas). The predominant wind distribution (94 days) in fall was similar to that of spring showing weak upper-level flow and distinct sea-land breeze circulation. On the other hand, the wind pattern (117 days) of high frequency in winter showed upper northwesterly and surface westerly flows, which was no change in daily wind direction.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.2
• /
• pp.129-137
• /
• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.