• Korean Journal of Culture and Social Issue
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• v.18 no.4
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• pp.481-503
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• 2012

This study aims to investigate the effects of the Big Five Aspects on psychological adjustment and the practical use of the Big Five Aspects in personality studies. Correlations between psychological adjustment(subjective well-being, life satisfaction, anxiety, depression) and the Big Five Aspects were compared to correlations between the Big Five and psychological adjustment. The results showed distinction between two aspects within each of the Big Five that major personality traits that are actually related to psychological adjustment were found. Multiple regressions were used with subjective well-being, life satisfaction, anxiety, and depression as criterion variables to investigate the effect of the Big Five Aspect on psychological adjustment and the results are as followed. Big Five Aspects accounted for 66.2% of the variance in subjective well-being and withdrawal, compassion, industriousness, enthusiasm, assertiveness, openness significantly predicted subjective well-being. Life satisfaction and depression were significantly predicted by withdrawal and enthusiasm. Withdrawal was the only variable that significantly predicted anxiety. Multiple regression also showed that withdrawal and enthusiasm were the most consistent, accountable variables in predicting overall psychological adjustment. This findings indicate that individual's personality traits played a significant role in predicting subjective well-being and mental health as consistent with past findings, and that the Big Five Aspects can offer more detailed and specific description than the Big Five can. Finally, the research discusses implications, limitations and suggestions for further studies.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.639-649
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• 2020

This study investigates consumer opinions of clothing care and provides fundamental data to decision-making for oncoming development of clothing care system. Textom, a web-matrix program, was used to analyze big data collected from Naver and Daum with a keyword of "clothing care" from March 2019 to February 2020. A total of 22, 187 texts were shown from the big data collection. Collected big data were analyzed using text-mining, network, and CONCOR analysis. The results of this study were as follows. First, many keywords related to clothing care were shown from the result of frequency analysis such as style, Dryer, LG Electronics, Product, Customer, Clothing, and Styler. Consumers were well recognizing and having an interest in recent information related to the clothing care system. Second, various keywords such as product, function, brand, and performance, were linked to each other which were fundamentally related to the clothing care. The interest in products of the clothing care system were linked to product brands that were also naturally linked to consumer interest. Third, the keywords in the network showed similar attributes from the result of CONCOR analysis that were classified into 4 groups such as the characteristics of purchase, product, performance, and interest. Lastly, positive emotions including goodwill, interest, and joy on the clothing care system were strongly expressed from the result of the sentimental analysis.

• Convergence Security Journal
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• v.13 no.4
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• pp.69-75
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• 2013

Customer is the base factor of income for some corporations, so that effective CRM (Customer Relationship Management) is very important to develop the business. In order to use CRM efficiently, we should figure out customers' demands and provide services or products that the customers want. However, it is getting difficult to comprehend customers' demands because they have complicated form and getting more diverse. Recently, social media like Twitter and Facebook let customers to express their demands, and using big data is a very effective method for efficient CRM. This research suggests how to utilize big data for hotel CRM, which considers customer itself as asset of business. In addition, we discuss security problems of big data service and propose the solution for that.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.303-304
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• 2021

In the 21st century, as we enter the Fourth Industrial Revolution, research in various fields utilizing big data is being conducted, and innovative and useful technologies are constantly emerging in the world. Among several technologies recently in the big data era, among various fields utilizing some algorithms of artificial intelligence, it shines in the field of finance and is used for pin tech, financial fraud detection and risk management, etc., and recently Even in the booming stock market, it is used for investment prediction and investment factor analysis using artificial intelligence algorithm models. In this paper, we plan to investigate various research cases and investigate trends in how they are used in the stock market through artificial intelligence that utilizes big data.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1014-1015
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• 2023

많은 장애아 부모들은 양육에 대한 스트레스, 미래에 대한 걱정으로 심리적으로 상당한 중압감을 느낀다. 이에 비해 매년 증가하는 장애인 수에 비해 장애아 부모 및 가족의 심리적·정신적 문제를 해결하기 위한 프로그램이 부족하다.[1] 이를 해결하고자 본 논문에서는 감정분석 소통 플랫폼을 제안한다. 제안하는 플랫폼은 KoBERT 모델을 fine-tunning 하여 사용자의 일기 속 감정을 분석하여 장애아를 둔 부모 및 가족 간의 소통을 돕는다. 성능평가는 제안하는 플랫폼의 주요 기능인 KoBERT 기반 감정분석의 성능을 확인하기위해 텍스트 분류 모델로 널리 사용되고 있는 LSTM, Bi-LSTM, GRU 모델 별 성능지표들과 비교 분석한다. 성능 평가결과 KoBERT 의 정확도가 다른 분류군의 정확도보다 평균 31.4% 높은 성능을 보였고, 이 외의 지표에서도 비교적 높은 성능을 기록했다.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.250-257
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• 2019

Recently, 357 water-friendly space were established near the main streams of the country through the Four Major Rivers Project, which was used as a resting and leisure space for the citizens, and the river environment and ecological health were improved. We are working hard to reduce the number of points and plan and manage the water-friendly space. In particular, attempts are being made to utilize mobile big data to make more scientific and systematic research on the number of users. However, when using mobile big data compared to the existing method of conducting field surveys, it is possible to easily identify spatial user movement patterns, but it is different from the actual amount of use, so various verifications are required to solve this problem. Therefore, this study evaluated the accuracy of estimating the number of users using mobile big data by comparing the number of visitors using mobile big data and the number of visitors using drone for Samrak ecological park located in the mouth of Nakdong River. As a result, in the river hydrophilic district, it was difficult to accurately estimating the usage pattern of each facility due to the low precision of pCELL, and it was confirmed that the usage patterns in the park could be distorted due to the signals stopped at roads and parking lots. Therefore, it is necessary to improve the number of pCELLs in the water-friendly space and to estimate the number of visitors excluding facilities such as roads and parking lots in future mobile big data processing.

• Smart Media Journal
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• v.5 no.4
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• pp.124-130
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• 2016

Big data technologies are increasing the need for big data in many areas of the world. Recently, the health care industry has become increasingly aware of the importance of disease and health care services, as it has become increasingly immune to prevention and health care. To do this, we need a Big data system to collect and analyze the personal biometric data. In this paper, we design the biometric big data system using low cost wearable device. We collect basic biometric data, such as heart rate, step count and physical activity from Mi Band, and store the collected biometric data into MongoDB. Based on the results of this study, it is possible to build a big data system that can be used in actual medical environment by using Hadoop etc. and to use it in real medical service in connection with various wearable devices for medical information.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.73-99
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• 2014

Recently, big data have been studied ways to use in various fields. Big data refers to huge amounts of data that could not be addressed by conventional methods. Big data has attracted attention for improving accuracy of decision-making, forecasting in the near future, and creation of new business. In this study, it is an object to develop the utilization plan for big data in the field of technology commercialization. For this reason, we conducted study like case studies, literature review and focus group interview. We have derived the big data utilization plan based on this in the technology commercialization field. It, the data utilization plan, combines with the technology commercialization process of Jolly and it has five sub processes (Imagining, Incubating, Demonstrating, Promoting, Sustaining). In this paper, there is a significance that has emphasized the possibility for big data utilization in the technology commercialization. However, there is a limit to the general interpretation for our study. And we hope to contribute to the expansion of areas of technology commercialization information analysis through this research.