• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Proceedings of KOSOMES biannual meeting
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• 2003.11a
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• pp.219-225
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• 2003

The present study intends to assess the long-term steric sea-level change and its prediction, and potential impacts to the sea-level rise due to the 21st global warming in the coastal zone of the Korea in which much socioeconomic activities have been occurred. The analysis of the 23 tide-gauge data near Korea reveals the overall mean sea-level trend of 2.31 mm/yr.In the satellite altimeter data (Topex/Poseidon and ERS), the sea-level trend in the East Sea is 4.6mm/yr. Both are larger than those of the global average value. However, it is quite questionable that the sea-level trends with the tide-gauge data on the neighboring seas of Korea relate to global warming because of the relatively short observation period and large spatial variability. It is also not clear whether the high trend of altimeter data in the East Sea is related to the acceleration of sea level rise in the Sea, short response time of the Sea, natural variability such as decadal variability, short duration of the altimeter. The coastal zone of Korea appears to be quite vulnerable to the 21st sea level rise such that for the I-m sea level rise with high tide and storm surge, the inundation area is 2,643 km2, which is about $1.2\%$ of total area and the population in the risk areas of inundation is 1.255 million, about $2.6\%$ of total population. The coastal zone west of Korea is appeared to be the most vulnerable area compared to the east and south. In the west of the Korea, the North Korea appears to be more vulnerable than South Korea. In order to cope with the future possible impact of sea-level rise to the coastal zone of Korea effectively, it is essential to improve scientific information in the sea-level rise trend, regional prediction, and vulnerability assessment near Korean coast.

• Nutrition Research and Practice
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• v.7 no.3
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• pp.224-232
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• 2013

The objective of this study is to identify the dietary patterns associated with the risk of hypertensions among Korean adults using data from the Korean National Health and Nutrition Examination Survey (KNHANES, 2008-2010). This study analyzes data from 11,883 subjects who participated in the health and nutrition survey, aging from 20 to 64 years. We performed factor analysis based on the weekly mean intake frequencies of 36 food groups to identify major dietary patterns. We identified three major dietary patterns in both sexes, namely "traditional", "western" and "dairy and carbohydrate" patterns. Participants in the highest quartile of western pattern scores had significantly higher blood pressure, serum total cholesterol, and triglyceride levels than those in the lowest quartile. Although not statistically significant, a trend (P for trend = 0.0732) toward a positive association between the western dietary pattern and hypertension risk was observed after adjustments for age, sex, education, income, body mass index (BMI), smoking, physical activity, and energy intake. The dairy and carbohydrate pattern was inversely related with BMI and blood pressures and positively associated with serum high-density lipoprotein (HDL)-cholesterol. After adjusting the age, sex, education, income, BMI, smoking, physical activity and energy intake, the dairy and carbohydrate pattern showed inverse associations with hypertension prevalence (OR = 0.64, 95% CI = 0.55-0.75; P for trend < 0.0001). Intakes of fiber, sodium, and antioxidant vitamins were significantly higher in the top quartile for the traditional pattern than in the lowest quartile for the traditional pattern (P for trend < 0.0001). Intakes of fiber (P for trend < 0.0001), calcium (P for trend < 0.0001), retinol (P for trend = 0.0164), vitamin $B_1$ (P for trend = 0.001), vitamin $B_2$ (P for trend < 0.0001), niacin (P for trend = 0.0025), and vitamin C (P for trend < 0.0001) were significantly increased across quartiles for the dairy and carbohydrate pattern whereas sodium (P for trend < 0.0001) intake was decreased for this pattern. In conclusion, the dairy and carbohydrate pattern may be associated with a reduced risk of hypertension whereas the western pattern may be associated with an increased risk of hypertension among Korean adults.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.607-616
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• 2020

Health care wearables are devices that are attached to or combined with the human body to improve the health care capabilities of the human body that can be safely and adjustable according to preference. This study provided direction for future research on healthcare wearables in the field of clothing science, considering trends observed in this field from 2010 to 2019. Over the last 10 years, 812 studies have been conducted on healthcare wearables in Korea. Research has increased significantly since 2015, with a large number of articles published in this field. The research for this study was broken down into the following categories: technology development, marketing analysis, and technology analysis. The results according to the research method demonstrated that development and production methods were used most frequently, followed by trend analysis, experiment and evaluation, and survey. An analysis of keywords in the articles studied revealed that device, healthcare, big data (biometric data and database), and healthcare convergence technologies were trending. Similarly, detailed research on healthcare wearable devices and related technologies was actively being conducted. However, focusing on fiber, textiles, design, and clothing articles, in relation to the field of clothing in healthcare wearables, only 81 articles were found on this topic (10.0%), which was low compared to other studies. Therefore, it was determined that more research on healthcare wearables is necessary in the field of clothing.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.785-790
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• 2019

Convergence and advancement between technologies such as Artificial Intelligence, Big Data, and the Internet of Things have a significant impact on the regional flagship industry. All technical fields are used as a converged technology by connecting between technology and industry. In order to understanding the recent technical trend, it is possible to easily realized the technical trend research and analysis through keyword search using patent information. The purpose of this study is to identify patent trends applied to convergence technology in the 4th Industrial Revolution age in radiation technology development and to present patent trends and analysis for strengthening and utilizing radiation-related industrial technology competitiveness and to apply them to demand technology and forecast future promising technologies.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.33-38
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• 2021

The convergence trend is the core of the 4th industrial revolution, and due to such expectations and possibilities, various countermeasures are being sought in diverse fields. This study conducted a quantitative analysis to identify the trend of convergence research over the past 10 years. Specifically, major research keywords were extracted, word cloud techniques were applied, and visualized to identify trends in academic research on convergence. To this end, research papers from 2012 to 2020 published in journal of digital convergence were investigated. The analysis period was divided into two periods: the former 4 years(2012-2015) and the latter 4 years(2016-2019) to confirm the difference in research trends. In addition, the research papers of 2020 were analyzed in order to more clearly understand the changes in the research trend of the last year due to the COVID-19. The results of this study are significant in that they can be used as useful basic data for future research and to understand research trends as keywords in the field of convergence.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.331-333
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• 2007

Product reliability is important to quality and competitiveness. Much management and engineering efforts go into evaluating reliability, assessing new designs and changes of manufacturing processes, identifying cause of failure. Major methods are based on environmental test. In this study, we analysis the environmental test data of the electrical component, unit, set. These data were gathered for 7 years.

• Journal of Astronomy and Space Sciences
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• v.27 no.3
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• pp.231-238
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• 2010

We analyzed global positioning system (GPS)-derived precipitable water vapor (PWV) trends of the Korea Astronomy and Space Science Institute 5 stations (Seoul, Daejeon, Mokpo, Milyang, Sokcho) where Korea Meteorological Administration meteorological data can be obtained at the same place. In the least squares analysis, the GPS PWV time series showed consistent positive trends (0.11 mm/year) over South Korea from 2000 to 2009. The annual increase of GPS PWV was comparable with the 0.17 mm/year and 0.02 mm/year from the National Center for Atmospheric Research Earth Observing Laboratory and Atmospheric InfraRed Sounder, respectively. For seasonal analysis, the increasing tendency was found by 0.05 mm/year, 0.16 mm/year, 0.04 mm/year in spring (March-May), summer (June-August) and winter (December-February), respectively. However, a negative trend (-0.14 mm/year) was seen in autumn (September-November). We examined the relationship between GPS PWV and temperature which is the one of the climatic elements. Two elements trends increased during the same period and the correlation coefficient was about 0.8. Also, we found the temperature rise has increased more GPS PWV and observed a stronger positive trend in summer than in winter. This is characterized by hot humid summer and cold dry winter of Korea climate and depending on the amount of water vapor the air contains at a certain temperature. In addition, it is assumed that GPS PWV positive trend is caused by increasing amount of saturated water vapor due to temperature rise in the Korean Peninsula. In the future, we plan to verify GPS PWV effectiveness as a tool to monitor changes in precipitable water through cause analysis of seasonal trends and indepth/long-term comparative analysis between GPS PWV and other climatic elements.

• Journal of the Korea Fashion and Costume Design Association
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• v.15 no.4
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• pp.129-142
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• 2013

Fabrics created unique features through the change in various ways depending on the elements including fabric structure, threads, thickness, weaving, patterns and processing. This study examined the difference between texture and image depending on components of fabrics. In order to understand the trend of components of fabrics, the actual components of fabrics was analyzed by year, using the women's apparel trend books, Nelly Rodi Fabrics and Promostyl Fabrics. This study analyzed Spring/Summer season and Fall/Winter season from 2002 to 2012. Frequency analysis, cross-tabulation analysis, multiple response cross tabulation analysis were implemented using SPSS 18.0. The followings are the analysis results. For fabric composition, Spring/Summer season used cotton fabric the most. Cotton fabric accounted for relatively high usage in 2005, 2007 and 2008. Fall/Winter season used wool the most in 2002, 2003, 2004 and 2005. In case of fiber blend by fabric composition, cotton was blended with other fibers in Spring/Summer season except wool. The fiber blending ratio of wool was the highest in Fall/Winter season. Wool was blended in most fabrics. More than two patterns were mixed for fabric patterns. The fabric patterns were applied the most in 2012. For fabric processing, the fabrics by sensitivity-functional processing were continuously used every year and the surface finish showed relatively high usage in 2002 and 2011. In conclusion, this study will build the systematic data for 11 years including fabric trends in the past. It can improve specialization, systematization and efficiency in fabric planning.