• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.175-182
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• 2023

The purpose of this study, the trend of renewable energy, domestic and foreign renewable energy policies, and the flow of the legal system related to renewable energy location were identified, and a location analysis using public data was studied when solar energy was located. First, renewable energy is leading to energy conversion by reducing the proportion of existing fossil fuel-centered energy sources in the global trend and increasing the proportion of renewable energy, an eco-friendly energy source, and changing the institutional and market structure. Second, large-scale solar energy power plants are installed and operated in rural areas where there is no change in insolation and land prices are cheaper than in urban areas where there are many changes in insolation due to surrounding high-rise buildings and street trees. Third, if a preliminary location review is conducted using public data at this time, it will be easy to identify the optimal location for area and size calculation. Fourth, the solar energy location functional area was studied in area A, and the total area of the target area was 624.5km², with 392.7km² and 62.9% of the avoidance area where solar power cannot be located.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.538-553
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• 2021

As interest in the sustainable fashion industry continues to increase along with climate issues, it is necessary to identify research trends in sustainable fashion and seek new development directions. Therefore, this study aims to analyze research trends on sustainable fashion. For this purpose, related papers were collected from the KCI (Korean Citation Index) and Scopus, and 340 articles were used for the study. The collected data went through data transformation, data preprocessing, topic modeling analysis, core topic derivation, and visualization through a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: consumer clothing consumption behavior and environment, upcycle product development, product types by environmental approach, ESG business activities, materials and material development, process-based approach, lifestyle and consumer experience, and brand strategy. Topics were related to consumption, production, and education of sustainable fashion, respectively. KCI analysis results and Scopus analysis results derived eight topics but showed differences from the comprehensive analysis results. This study provides primary data for exploring various themes of sustainable fashion. It is significant in that the data were analyzed based on probability using a research method that excluded the subjective value of the researcher. It is recommended that follow-up studies be conducted to examine social trends.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.61-81
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• 2021

In the era of untact, the "Chabak" using cars as accommodation spaces is attracting attention as a new form of travel. Due to the advantages, including low costs, convenience, and safety, as well as the characteristics of the vehicle enabling independent travel, the demand for Chabak is continuously increasing. Despite the rapid growth of the market and related industries, little academic has investigated this trend. To establish itself as a new type of travel culture and to sustain the growth of related industries, it is essential to understand the public perception of Chabak. Therefore, based on the marketing mix theory and big data analysis, this study analyzes the public perception of Chabak. The results showed that Chabak has established itself as a consumer-led travel culture, contributing to the aftermarket growth of the automobile industry. Additionally, consumers were found to be increasingly inclined to enjoy travel economically and wisely, and actively share information through social media. This initial study on the new travel trend of Chabak is significant in that it employs big data analysis on a theoretical basis.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.6
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• pp.23-29
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• 2011

This study was conducted to identify the effect of lapse rate application according to elevation on the estimation of large scale watershed rainfall. For the Han river basin (26,018 $km^2$), the 11 years (2000-2010) daily rainfall data from 108 AWS (Automatic Weather Station) were collected. Especially, the 11 heavy rain and typhoon events from 2004 to 2009 were selected for trend analysis. The elevation effect by IDW (Inverse Distance Weights) interpolation showed the change up to +62.7 % for 1,200~1,600m elevation band. The effect based on 19 subbasins of WAMIS (Water Resources Management Information System) water resources unit map, the changes of IDW and Thiessen were -8.0 % (Downstream of Han river)~ +19.7 % (Upstream of Namhan river) and -5.7 %~+15.9 % respectively. It showed the increase trend as the elevation increases. For the 11 years rainfall data analysis, the lapse rate effect of IDW and Thiessen showed increase of 9.7 %~15.5 % and 6.6 %~9.6 % respectively.

• Korean Journal of Human Ecology
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• v.15 no.5
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• pp.835-847
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• 2006

The aim of this study is to examine the tween generation's (11 to 15 years-old) clothing purchase tendency. Based on the questionnaire, the data were collected from 187 elementary school students(11 to 12 years-old) and from 293 middle school students(13 to 15 years-old) in Jeonbuk. The data were analyzed with the factor analysis, Chi-square analysis, t-test, F-test. The research shows: the tween generation rarely if ever buy their clothing by themselves, and they usually accompany their mothers when they purchase their clothes. In the 'size fitness', girl tweenage group prefers a perfect fit to an easy one. But boy tweenage group shows a different tendency from them. In the 'fashion adaption', the tweenage group of 13 to 15 year-olds responds in a sensitive way. The 'style', 'design' and 'color' of clothing are the most decisive factors in their purchasing trend. Especially, the boy tweenage group prioritize the factors in the order of 'brand value', 'price' and 'trend'.

• Journal of the Korean Home Economics Association
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• v.45 no.5
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• pp.39-49
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• 2007

The purpose of this study was 1) to segment the middle-aged and elderly women's fashion market based on the clothing benefits sought by the buyer and 2) to compare the purchase criteria and fashion information sources among the segmented markets. The data were collected using a self-administered questionnaire in Seoul and its surrounding suburban areas. Factor analysis, ANOVA, Duncan test, and Dunnett's T3 tests were used to conduct the data analysis from 285 out of 300 questionnaires. The middle-aged and elderly women's fashion market was segmented into four groups; value-oriented, social status/trend-oriented, uniqueness-oriented, and protection/ convenience-oriented. All four groups were significantly different in terms of purchase criteria and fashion information sources. The social status/trend-oriented group used external purchase criteria, such as country of origin or brand and obtained fashion information from personal experience, advice from the salesperson, and celebrities. The protection/convenience-oriented group sourced fashion information from newspapers, the internet, and the radio. The uniqueness-oriented group put less importance on practical use/convenience criteria. Marketing strategies for these segmented markets were discussed.

• Journal of Child Welfare and Development
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• v.16 no.2
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• pp.67-85
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• 2018

The purpose of this study was to analyze the trends in the studies on safety education of infant-early childhood teachers in order to provide basic data for a variety of studies in the field of child education. We consulted a total of 59 dissertations and journals articles on the safety education of infant-early childhood teachers published from 2008 to 2018. Our results were as follows: First, we found that the largest number of studies concerned safety awareness, followed by status and awareness, safety actions, safety education analysis, safety accidents and countermeasures, emergencies, development of programs, and teacher education. Second, we found that the most popular targets were child-care of teachers, followed by teacher-trainees, and center directors. Third, among the types of studies, we found that investigative quantitative studies were the most prevalent, followed by correlational studies, qualitative studies, and experimental studies. In terms of methods of gathering data, we found that questionnaires were the most commonly used, followed by qualitative studies, literature reviews, qualitative/quantitative studies, and quantitative literature reviews.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.1-13
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• 2023

This study forecasted the manpower demand of eco-friendly smart shipbuilding, whose importance and weight are increasing according to the environmental regulations of the IMO and the spread of the 4th industrial revolution technology. It predicted the shipbuilding industry manpower by applying various models of trend analysis and time series analysis based on data from 2000 to 2020 of Statistics Korea. It was found that the prediction applying geometric mean had the smallest gap among the trend and time series analysis methods in comparing between forecast results and actual data for the past 5 years. Therefore, the demand for manpower in the shipbuilding industry was predicted by using the geometric mean method. In addition, the manpower demand of smart eco-friendly ships wast forecasted by using the 2018 and 2020 manpower survey results of the Ministry of Trade, Industry and Energy and reflecting the trend of manpower increase in the shipbuilding industry. The result of forecasting showed that 62,001 person in 2025 and 85,035 people in 2030. This study is expected to contribute to the adjustment of manpower supply and demand and the training professional manpower in the future by increasing the accuracy of forecasting for high value-added eco-friendly smart ships.