• Korean Business Review
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• v.11
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• pp.149-171
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• 1998

The most important thing in the Tourist Market Segmentation is to find descriptive variables which can describe the changes of tourist demand properly. There are many descriptive variables. Among them, vital statistical variables were proved to be effective. The strongest variable but which was studied much less is the Family Life Cycle. This study will focus on the relation between Family Life Cycle and Travel Behavior of Destination Choice. In this study, I will verify the validity of Family Life Cycle as a descriptive variable of Tourist Market Segmentation, and try to find the meaningful variable at each steps. Therefore, The purpose of this study is to explain the relation between Family Life Cycle and Travel Behavior of Destination Choice, to verify the validity of Family Life Cycle as descriptive variable and to find the strategy to respond to the increase in quantity and diversity of quality of Tourist Market. The studies on the Family Life Cycle should be updated continuously according to the change of family structure and it should be understood as standard for Tourist Market Segmentation in the public and private sphere.

• Journal of the Korean Society of Food Culture
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• v.9 no.3
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• pp.289-301
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• 1994

The purpose of this study was to investigate housewives' perception, interest and consumption of organic food, and to identify the variables influencing on them. The sample was selected from 411 housewives living in Seoul from 1st of September to 20th of September in 1993. Used statistical methods were frequency, percentage, mean, Cronbach's ${\alpha}$, factor analys, ANOVA, Duncan's multiple range test, t-test, correlation, regression analysis and Path analysis. The major results are as follows: 1) Housewives' perception degree of organic food in its variety, easy availability and contribution to health was relatively high level, but price level, reliability and packing condition was somewhat low level. Out of housewives' many images of organic food, taste health care, quality and necessity factors can be implicated. 2) Housewives' interest degree of organic food is somewhat high level 3) Housewives' consumption degree of organic food was relatively low level and was affected by their perception of oraganic food. 4) Frequency to use seasonings was the major independent variable which influenced to housewives' perception degree of organic food, while recognition on enviornmental contamination was the major independent variable which influenced to their interest degree of organic food, and their total perception on organic food was the major independent variable which influenced to their consumption degree of oragnic food. Therefore, it is considered that their total perception to it can decide the demand of organic food 5) Housewives' perception and interest degree of it was the major intervening variable which influenced to their consumption degree of oragnic food.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.111-122
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• 2012

There has been a strong demand for providing diverse services to drivers utilizing existing ITS infrastructure. To this end, this study is aiming at improving the accuracy of a variable speed limit system by determining recommended speeds for the system utilizing the information from Urban Traffic Information System(UTIS) and Weather Information System(WIS). In order to determine appropriate speed limits under inclement weather conditions for the variable speed limit system, this study examined three methods: i) the method utilizing the information from WIS, ii) the method utilizing the information from UTIS, and iii) the method which combines the information from WIS and UTIS using different weights for diverse weather conditions. Finally, this study selected the third method which determines an appropriate speed limit using the relationship between the vehicle operating speed and the minimum stopping distance which is estimated using the existing speed limit, surface coefficient of friction and superelevation.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.581-591
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• 2023

Domestic processed food exports might increase due to the free trade agreement (FTA) and spread of the Korean Wave, Hallyu. However, the share of the domestic raw materials in the domestic processed food industry is very low at 31.4%, which limits the spillover effect on domestic agriculture. Therefore, we selected frozen dumplings as a representative processed food whose exports are growing rapidly and conducted a multiple regression analysis to examine the effects of Hallyu and FTA variables on the frozen dumpling exports. We tried to link them to an increase in agricultural income through the expansion of demand for the domestic raw materials. This study analyzed tariff indicator as the FTA variable, cosmetics export value indicator as the Hallyu Wave variable, and other variables expected to change the trade environment such as gross domestic product (GDP) and the relative exchange rate by country as the key explanatory variables that affect changes in the trade environment. The analysis showed that the core hypothesis, the Hallyu variable and the FTA variable, have a positive impact on frozen dumpling exports. Frozen dumpling exports are expected to increase as the FTAs and the spread of Hallyu are expected to continue for the foreseeable future. Therefore, we should seek ways to increase the proportion of domestically produced ingredients in the frozen dumplings to spread the economic impact to domestic agriculture. We reviewed previous studies and determined, the price-related factors play a major role in the use of imported ingredients, and that price stability and competitiveness are essential to increase the share of the domestically produced ingredients. Based on these conclusions, we reviewed the current status of food industry-related policies and determined measures needed to expand the use of domestically produced ingredients.

• Journal of IKEEE
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• v.28 no.3
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• pp.451-457
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• 2024

This paper proposes a method for predicting agricultural product prices by utilizing various variables such as price, climate factors, demand, and import volume as data, and applying the Long Short-Term Memory (LSTM) model. The analysis of prediction performance using the LSTM model, which learns the long-term dependencies of time series data, showed that integrating diverse data improved performance compared to traditional methods. Furthermore, even when predicting without price data as a dependent variable, meaningful results were achieved using only independent variables, indicating the potential for further model development. Moreover, it was found that using a multi-variable model could further enhance prediction performance, suggesting that this complex approach is effective in improving the accuracy of cabbage price predictions.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.123-130
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• 2023

As part of the research on cultural factors that determine international tourism demand, this study was conducted based on regional interest and the need for understanding religion. The purpose of this study is to empirically test how religious factors affect tourism demand in Korea to find out that religious factors are important considerations in establishing tourism policies and strategies. To achieve the purpose of this study, the research target areas were selected as Thailand, Indonesia, and the Philippines, which have relatively many tourists visiting Korea among Southeast Asian countries and are well known for their religious characteristics. GDP and nominal exchange rate, which are economic factors, were selected as explanatory variables. And religious diversity was selected as a characteristic factor variable of the tourism demand model based on the characteristic theory. An empirical analysis was conducted through a gravity model. As a result of the estimation, it was found that GDP has a positive effect on tourism demand in Korea. Nominal exchange rate variables and religious diversity variables were found to have a negative effect on tourism demand in Korea. We have confirmed that religion is an important factor in choosing tourist destinations for Filipino, Thai, and Malaysian tourists visiting Korea, and they choose religiously similar destinations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.140-146
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• 2011

As the demand of Variable Message Sign(VMS) has become pervasive in fulfilling the ITS policy, the costs of maintaining the Variable Message Sign operation have also increased. This paper proposes the U-curved figure, the C-curved figure and the ventilated figure type for aerodynamic drag reduction on Variable Message Sign and shows the analysis of aerodynamic drag effects using Flow Analysis. As a results of the flow analysis for right-angled, 45 degrees from side to side and 45 degrees from up or down, the C-curved figure and the ventilated figure type show about 30% aerodynamic drag reduction in all direction. And the U-curved figure type shows vivid aerodynamic drag reduction for right-angled and 45 degree from side to side, but trivial aerodynamic drag reduction for 45 degree from up or down. It is possible to reduce not only the damage on Variable Message Sign due to typhoon because of the aerodynamic drag reduction, but also installation constraints because of lighter Variable Message Sign support structure by appling the proposed structure and analysis in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.116-124
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• 2012

This paper concerns a joint replenishment problem for a single buyer who sells multiple types of items to end-customers. The buyer periodically replenishes the inventory of each item to a preset order-up-to-level to satisfy the end customers' demands, which may be non-stationary. A joint replenishment policy characterized by variable order-up-to-levels is proposed for the buyer who wishes to minimize the expected cost of operating the retail system. The proposed policy starts each period by calculating the expected cost of ordering and not ordering action based on the information of the current inventory position and forecasted demand for the upcoming period. It then takes advantage of an integer programming model to get a cost effective joint replenishment plan. Computer experiment was performed to test efficiency of the proposed policy. When compared with the most efficient policy currently available, our policy showed a considerable cost savings especially for the problems having non-stationary demands.