• Korea journal of population studies
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• v.25 no.2
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• pp.271-291
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• 2002

According to a system-wide analysis utilizing the raw data of Korean urban households survey, the expenditure elasticity estimate of alcoholic demand is around 0.71, which implies the demand for alcoholic consumption is relatively necessary The own price elasticity estimates are pretty elastic between -1.79 and 2.10. The trend of price elasticity estimates shows to be more elastic recently from the past.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.442-443
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• 2022

As the recent trend of factories has changed from analog to smart factory, there are various functions that conveniently use smart factory. This paper introduces various techniques for predicting demand within smart factories among the functions of smart factories.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.47-57
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• 2016

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City's O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.

• Human Ecology Research
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• v.53 no.3
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• pp.309-318
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• 2015

This study investigates to investigate the ageing effect on household demand for clothing, food, housing and medical care commodities in Korea using a demand system model. The cross-sectional and time-series data from Statistics Korea on urban household expenditures and age projection analyzed household demands of consumption commodities. The household head age and elderly population ratio were employed for proxy variables of ageing. Ageing variable elasticities of commodity demands were estimated. Study results show that ageing variables significantly influenced on a household demand for commodities; clothing and food consumption decreases; however, housing and medical care consumption increases with ageing. The elasticities of total consumption expenditures and price variables were estimated in the demand analysis; these two variables significantly impacted almost all of the household consumption for the studied commodities. This study provides an opportunity to examine how ageing influences household consumption for clothing, food, housing and medical care commodities as Korean society experiences a rapid ageing. It is also meaningful that this study conducted a quantitative measuring of the household demands for commodities that was different from past research on the household consumption expenditures for commodities.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.3
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• pp.89-100
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• 2017

In order for the international aviation community to efficiently and safely manage the gradual increase of air passenger demand, direction suggestions of airport traffic prediction based on future airport capacity requirements, airport design and infrastructure establishment is utilized by airport traffic data that is m comparable internationally. It is a global trend to pursue more efficient airport operating system structure to accept air passenger demand through more realistic comparable data in order to escape from the structure of reckless airport establishment and infrastructure composition based on passenger demand predictions referring to simple statistical data that has existed in the past. This study aimed to seek effective operational measures for the New Jeju airport scheduled to be opened in 2025 by time-series analysis. This study also analysed airport operation strategies, air traffic distribution strategies, cargo volume increase rates and its effectiveness of airports adopting the multi-airport system that have similar operational practices and geographical conditions. This study sought the most appropriate multi airport system application measures for New Jeju airport to promote efficiency and international competitiveness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.557-566
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• 2022

This study aims to investigate Jinju silk companies, production items, and silk industry supporting projects from 2019 to 2021 in order to discuss the current status of the silk industry. The following are this study's methods: First, a list of Jinju silk companies that have been operating for the past three years (2019-2021) was prepared to investigate the current status of the Jinju silk industry. Second, an investigation was conducted into the representative products produced in Jinju over the past three years; this investigation was conducted using direct interview. Third, an investigation was conducted on the projects that supported the Jinju silk industry over the past three years, and the list of members of the Gyeongnam Textile and Jinju Silk Industry Cooperative Association-a facility of Gyeongsangnam-do Province, the Jinju City Hall brochure (2019), and the SMINFO(SMall business status INFOrmation System) were utilized for this purpose. The following are the results: First, Jinju silk companies are classified into four categories, namely weaving, dyeing, twisting, and designing companies. According to data from 2021, 83% (34 of 41) of silk companies were weavers. Second, the demand for solid fabrics has increased over the past three years. The demand for patterned jacquard fabrics in producing Hanbok and Western-style clothing has decreased. Third, support for the Jinju silk industry could be classified into five categories: support for the operation of silk research institutions, support for the diversification of Jinju silk, support for the promotion of Jinju silk, support for the operation of silk manufacturers, and others.

• Journal of Fashion Business
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• v.15 no.6
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• pp.176-203
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• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• Korea Journal of Hospital Management
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• v.26 no.3
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• pp.56-67
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• 2021

There are few domestic studies on medical services in medically vulnerable areas where medical use is not met due to a lack of medical resources. The past studies on smart medicine targeting medically vulnerable areas grasp only the overall satisfaction level, or the sub-dimensions of satisfaction are not classified clearly. Also, it lacks consideration of the patient's needs. This study aims to analyze the effect of users' experience of the smart medicine pilot project conducted in medically vulnerable areas on satisfaction and demand. The user's experience was measured by variables in the dimensions of structure, process, and outcome. Among the pilot project participants, 282 subjects responded to the 2019 survey. Using the hierarchical regression method, we tried to find out the determinants of satisfaction and service demands. Experience factors affecting satisfaction were found to be accessibility, certainty, effectiveness, and efficiency. In addition, it was found that the demand in their 60s was high and that accessibility, certainty, effectiveness, and efficiency had a statistically significant effect on the demand. It is expected that the smart medicine pilot project will be effectively operated by well utilizing the factors influencing satisfaction and demand revealed in this study.