• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.171-177
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• 2019

This study aims to investigate the effect of eating-out types on the acceptance intention of artificial seasoning when consumers eat out at restaurants. Eating-out types considered to be typical when customers visit restaurants, such as the food-exploratory type, health-oriented type, and convenience-seeking type, were studied. Based on the research of previous studies, three eating-out types were selected for the study, which were "food-exploratory", "convenience-seeking", "health-oriented". This study was conducted by AMOS 22.0 with 300 questionnaires, and the Structural Equation Model (SEM) was used for examining the hypotheses as statistical method in this study. As a result, eating-out types such as "food-exploratory" and "convenience-seeking" were found to significantly affect the acceptance intention of artificial seasoning. However, consumers' acceptance intention of artificial seasoning differed depending on their consumption value. The path coefficients from food-exploratory type and health-oriented type to acceptance intention were more significant in the hedonic-oriented group than the utilitarian-oriented group. The results of this study suggest eating-out types relate to acceptance intention of artificial seasoning and provide meaningful implications for consumers' psychological consumption value when they consider artificial seasoning.

• Nutrition Research and Practice
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• v.9 no.5
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• pp.554-562
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• 2015

BACKGROUND/OBJECTIVES: The aim of this study was to examine differences in nutrient intake and food consumption by the presence of metabolic syndrome in Korean adults. SUBJECTS/METHODS: Study subjects in this cross-sectional study were recruited from four outpatient clinics in and near the Seoul metropolitan area of South Korea between 2006 and 2012. A total of 668 subjects (413 men and 255 women) aged ${\geq}30y$ were included in the final data analyses. For each subject, daily nutrient intake and food consumption were calculated using three days of dietary intake data obtained from a combination of 24-hour recalls and dietary records. To evaluate food consumption, mean number of servings consumed per day and percentages of recommended number of servings for six food groups were calculated. Metabolic syndrome was defined using the National Cholesterol Education Program Adult Treatment Panel III criteria. The general linear model was performed to examine differences in nutrient intake and food consumption by sex and the presence of metabolic syndrome after adjustment for potential confounding variables. RESULTS: Nutrient intake did not differ by the presence of metabolic syndrome in both men and women. Men with metabolic syndrome had lower consumption and percentage of the recommendation for fruits compared with those without metabolic syndrome (1.6 vs. 1.1 servings/day, P-value = 0.001; 63.5 vs. 49.5%, P-value = 0.013). Women with metabolic syndrome showed lower consumption and percentage of the recommendation for dairy foods than those without metabolic syndrome (0.8 vs. 0.5 servings/day, P-value = 0.001; 78.6 vs. 48.9%, P-value = 0.001). CONCLUSIONS: Low intakes of fruits and dairy foods might be associated with the risk of having metabolic syndrome among Korean adults. Dietary advice on increasing consumption of these foods is needed to prevent and attenuate the risk of metabolic syndrome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3621-3640
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• 2016

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1849-1876
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• 2022

Wireless Sensor Network (WSN) is considered as an integral part of the Internet of Things (IoT) for collecting real-time data from the site having many applications in industry 4.0 and smart cities. The task of nodes is to sense the environment and send the relevant information over the internet. Though this task seems very straightforward but it is vulnerable to certain issues like energy consumption, delay, throughput, etc. To efficiently address these issues, this work develops a cross-layer model for the optimization between MAC and the Network layer of the OSI model for WSN. A high value of duty cycle for nodes is selected to control the delay and further enhances data transmission reliability. A node measurement prediction system based on the Kalman filter has been introduced, which uses the constraint based on covariance value to decide the scheduling scheme of the nodes. The concept of duty cycle for node scheduling is employed with a greedy data forwarding scheme. The proposed Duty Cycle-based Greedy Routing (DCGR) scheme aims to minimize the hop count, thereby mitigating the energy consumption rate. The proposed algorithm is tested using a real-world wastewater treatment dataset. The proposed method marks an 87.5% increase in the energy efficiency and reduction in the network latency by 61% when validated with other similar pre-existing schemes.

• KDI Journal of Economic Policy
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• v.36 no.4
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• pp.171-205
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• 2014

This paper investigates the manner in which house prices affect macroeconomic variables through a house price channel by applying the method of Iacoviello (2005) to Korean data, and establishing a DSGE model with complementarity. This paper found that higher LTV ratio coupled with stronger complementarity results in the co-movement in both consumption and housing. For instance, the results show that when the LTV ratio and complementarity stands respectively at 50% and 0.42, an 1% rise in house prices increases consumption by 0.057%, and when the complementarity parameter increases to 0.52 with LTV remains unchanged at 50%, consumption rises by 0.047% per 1% increase in house prices. An increase in house prices leads credit constraints for borrowers to become more loose as value of a house rises as a collateral. The increase in household credit enables more consumer spending, eventually leading to increased consumption. A key link in which house prices are connected to macroeconomic variables is change in consumption. To put it simply, a rise in house prices leads to an increase in consumption, which consequently impacts the overall macro-economy. At this point, complementarity is found, in that the elasticity of intra-temporal substitution between housing and consumption is estimated at 0.42, which plays an important role in the house price channel by amplifying the effects of house prices on consumption.

• Korea journal of population studies
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• v.8 no.2
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• pp.93-99
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• 1985

There have been numerous studies in Korea and other countries of which the major hypotheses are identifying and dearibing the conditions under which the value of children has been formed. The present study proposes to view the formation of the value of children as a correlate of family's consumption-saving and work4eisure behavior pattern. The objectives of the proposed study are to identify the socio-economic and demographic factors determining the family's consumption-saving and work-leisure behavior pattern and to analyse the relationship between the value of children and this behavior pattern. The conceptual framwork of the analysis is that an individual family's socio-economic and demographic factors influence and shape the consumption-saving and work-leisure behaviors and these behaviors in turn influence and reflect the correlates and proximate determinants of the family'sfertility. In this paper, regression model is used to analyse the hypothesized relationship among the various variables. The regression methods used are first and second stage multiple regressions. In addition, MONOVA has been used to show the interactions. Data used are collected from the government publicactions. The major findings from this study are as follows: As the living Standard improves, n individual family's consumption of necessities and its working hours decline, while savings and leisure activities increase. The phenomena result in the fertility reduction as can be seen in the more developed conntries. Child-bearing and rearing activities are found to be the important component to determine the condumption-saving and work-leisure activies. The married women's labor participation, and the investment in education and health are also found to be the factors reducing fertility rate.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.03b
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• pp.23-23
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• 2010

Because of power consumption increase, global warming, and limitation of installation, not only high reliability and interruption capability but also compact and light power apparatuses are needed. In this paper, various models that short and long gap distance were used to analyze E field of each model. Calculation value was estimated of flashover position. As a result, short and long gap distance that vacuum interrupter inner between move electrode and fix electrode not coincided flashover position of each model. short gap distance estimated flashover position at electrode edge. but long gap distance model confirmed $E_{max}$ value at center shield. in this paper was compared electric field value. and estimated of flashover position from electric field calculation.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.255-289
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• 2011

This study made linked Energy IO Table 00-05-08 of 76 sectors in intermediate sectors and analyzed structural decomposition analysis in energy consumption change in industrial sector with both by aggregate data and micro data. Structural decomposition analysis focused value added level change, value added share change of each industry, output structural change of each industry and energy intensity change of each industry as factors. Supply side model based on Ghosh inverse matrix was applied as empirical model because Korea has export driven industrial structure. Empirical results with aggregate data showed that value added change increased energy consumption and output structural change of each industry decreased energy consumption in both 2000~2005 and 2005~2008. However value added share change and energy intensity change caused opposite direction in energy consumption change with time. Policy based on aggregate data can not evaluate effort of each industry in energy efficiency and make effective results because aggregate data delete character of each industry.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.123-132
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• 2019

As the one-person households with economic power have increased, the consumption culture changed as well. The primary purpose of this study is to investigate the on-line shopping motivation of one-person households in terms of consumer value. Economic value, emotional value, convenience value, social value were identified as affecting factors of satisfaction and intention to re-use of on-line shopping purchasing based on prior studies of on-line shopping behavior. This study tested the hypothesized model targeting 244 one-person households who have purchased products in on-line shopping mall. According to the results of analysis by using R, economic value, emotional value are significantly related to the consumer satisfaction but convenience value, social value are not. Consumer satisfaction of online purchasing was also shown to be related to the intention to re-use. However no difference between men and female was shown in shopping motivations. The research result can provide useful guidelines and strategies for one-person households with online shopping malls.