• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.110-115
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• 2017

Currently, the Ministry of Employment and Labor is strengthening monitor programs in regards to occupational industrial safety and health act compliance in business operations. However, industrial accidents occur persistently. Therefore, the study strives to diagnose and understand the issues in its educational stature, targeting managing supervisors in large scale shipbuilding industry whose completed the regular safety and health act sessions. This research considered a total of 3,252 employees whose completed theory-based cluster sessions for three months since February, 2016. The group is divided into two categories; 551 participants whose completed 8 hours of training and 2,701 participants whose completed 4 hours of training. Technical statistics were used to measure the knowledge of safety and health, educational environment, curriculum and educational effects on managing supervisors. A t-test was used to analyze the difference between the training hours. The result indicated that the target participants' knowledge on safety and health before the session was 50.24 points average (100 point scale), showing low standards in general. In depth analysis indicated that both 8 hours and 4 hours groups scored lowest in educational methods and communications between the lecturer and participants factors within the educational curriculum category. Meanwhile, transition in knowledge acquirement, work attitude, and work behaviors scored the highest in the analysis, showing a high satisfaction factors in educational effects. Therefore, the improvement in educational time and period can increase the efficacy of the educational programs. Also, theory-based cluster programs based on lectures suggests positive influence in knowledge acquirement and behavioral transitions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.266-269
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• 2002

This paper explores a fundamental study of acquiring the users' KANSEI information regarding the recognition of shape models. Since there are many differences such as background differences and knowledge differences among users, they will produce different evaluations based on their KANSEI even when an identical shape model is presented. Cluster analysis is proved to be available for catching a group tendency and for constructing a mapping relation between a description of the shape model and the HANSEl database. In order to investigate an analogical relation and a mutual influence in our consciousness, first, we made a questionnaire that asked subjects to represent images having different colors and shape cones by using 4 pairs of adjectives (KANSEI words). Next, based on the cluster analysis of the questionnaire using a fuzzy set theory, we proposed a hypothesis showing how the analogical relation and the mutual influence work in our mind while viewing the shape models. Furthermore, how the properties of KANSEI depend on their descriptions was also investigated by virtue of the cluster analysis. This work will be valuable to construct a personal KANSEI database regarding the Shape Model Processing System.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.481-486
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• 2021

Machine learning algorithms adopt criterion function as a key component to measure the quality of their model derived from data. Cluster analysis also uses this function to rate the clustering result. All the criterion functions have in general certain types of favoritism in producing high quality clusters. These clusters are then described by attributes and their values. Category utility and partition utility play an important role in cluster analysis. These are fully analyzed in this research particularly in terms of how they are related to the favoritism in the final results. In this research, several data sets are selected and analyzed to show how different results are induced from these criterion functions.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.181-194
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• 2020

Due to the changes in the distribution market, issues related to storage and distribution of agricultural, aquatic and livestock products, and storage and transportation of processed and fresh food are rapidly emerging, and as a result, Cold Chain is naturally receiving attention as one of the logistics services. The purpose of this study is to evaluate the competitiveness of location in the construction of a cold chain cluster centered on the metropolitan area, which has attracted attention in relation to the distribution of cold chains, such as recently refrigerated frozen foods. To this end, this study evaluated the competitiveness of cold chain cluster candidates in the metropolitan area by utilizing the CFPR (Consistent Fuzzy Preference Relations) method that can efficiently extract and quantify expert knowledge. As a result, the location competitiveness was found to be superior to Incheon New Port's hinterland, Gyeonggi South Area (Yongin), Gyeonggi West Area (Gimpo Logistics Complex), and Pyeongtaek Oseong Logistics Complex. In particular, this study extracted the knowledge of refrigerated and refrigerated logistics warehouse operation experts, and conducted detailed competitiveness assessments for cold chain cluster candidates in the metropolitan area, and suggested the optimal cluster candidates. In the future research, it is necessary to classify the questionnaire into the owner, large business group, and public business group, etc., who have the right to purchase and build to secure ownership of the fresh food distribution center.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.50-58
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• 2016

Purpose: The purpose of this study was to identify the knowledge structure of cancer survivors. Methods: For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords. Results: According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'. Conclusion: Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

• Korean Journal of Occupational Health Nursing
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• v.24 no.2
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• pp.76-85
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• 2015

Purpose: The purpose of this study was to identify knowledge structure of the Korean Journal of Occupational Health Nursing from 1991 to 2014. Methods: 400 articles between 1991 and 2014 were collected. 1,369 keywords as noun phrases were extracted from articles and standardized for analysis. Co-occurrence matrix was generated via a cosine similarity measure, then the network was analyzed and visualized using PFNet. Also NodeXL was applied to visualize intellectual interchanges among keywords. Results: According to the results of the content analysis and the cluster analysis of author keywords from the Korean Journal of Occupational Health Nursing articles, 7 most important research topics of the journal were 'Workers & Work-related Health Problem', 'Recognition & Preventive Health Behaviors', 'Health Promotion & Quality of Life', 'Occupational Health Nursing & Management', 'Clinical Nursing Environment', 'Caregivers and Social Support', and 'Job Satisfaction, Stress & Performance'. Newly emerging topics for 4-year period units were observed as research trends. Conclusion: Through this study, the knowledge structure of the Korean Journal of Occupational Health Nursing was identified. The network analysis of this study will be useful for identifying the knowledge structure as well as finding general view and current research trends. Furthermore, The results of this study could be utilized to seek the research direction in the Korean Journal of Occupational Health Nursing.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.322-329
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• 2015

The purpose of this study was to examine the oral health knowledge of maritime police officers, whose job belonged to the cluster of special occupations, in an effort to provide some information on the development of oral health education programs. The subjects in this study were 499 maritime police officers. After a survey was conducted from March to September, 2013, it's found that just 104 respondents(22.8%) had experience of receiving oral health education. In terms of general knowledge, the respondents who received that education were different from the others who didn't in the level of knowledge on the items related to temporomandibular joint(p=0.026), and there were no differences between the two in knowledge of periodontal health. As for prevention-related knowledge, they had a good knowledge of fluorine. Concerning needs for oral health education, 67.1 percent considered oral health professional manpower to be necessary, and 77.9 percent of the respondents who received oral health education gave this reply(p=0.004). Regarding preference for educational content, the right toothbrushing method was most preferred, followed by oral counseling, the use of oral hygiene supplies, the selection of dentifrice, and nutrition/anti-smoking education. The findings of the study suggest that the development of oral health education programs geared toward the cluster of special occupations such as maritime police is required.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.314-324
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• 2014

Aerospace industry is a combination of high technologies which has several characteristics such as product reliability, precision, light weight, and energy efficiency. Nowadays, each country is trying to invigorating knowledge and information sharing between the companies for the synergy effect of aerospace industry. However, the research and empirical analysis on the vitalization of aerospace industry cluster are insufficient. Therefore, this study aims to firstly classify the supporting functions of government for aerospace industry cluster into five types by analyzing existing literatures and status reports issued by government. Secondly, companies are surveyed on the five classified types of supporting functions by questionnaire. Questionnaire survey responded by 30 aerospace companies in Gyeongnam aerospace industry cluster are analyzed. Quantitative analysis methods were used for statistical analysis. Based on the analysis, improvement directions of government supporting functions are suggested. The results of this study is expected to help policy making for invigorating the aerospace industry cluster.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.41-50
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• 2020

Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.