• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.87-103
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• 2018

Purpose: This study investigates the best appointment pattern which can enhance customer's satisfaction and hospital's efficient management reflecting plastic surgery clinic's service characteristics. Methodology: The data of this study is obtained from Plastic surgery Clinic which is located in the civic center. By collecting and analyzing the data, we build the simulation model using ARENA. Based on 5 appointment patterns that was suggested in formal appointment scheduling studies, we experiment 3 simulation models; 'Basic Appointment Pattern' that has no restriction, 'Restriction on Second Customer' that restricts the number of second customer's in each slot, 'Restriction on Process Time' that restricts the number of second customer who has long process time in each slot. We can check robustness of the appointment patterns by experimenting on off-peak day and peak day, during peak season. Findings: This study confirms that these 2 restrictions can give a better result than 'Basic Appointment Pattern' that just simply distributes customers by number. Especially, the performance of Triangle-like pattern which is the best appointment pattern in the formal study has been improved by adding restrictions. Based on 'DET', 'Restriction on Second Customer' shows a better result. Meanwhile, based on 'E(WT)', 'Restriction on Process Time' shows a better result. Overall, based on 'DET+E(WT)', 'Restriction on Second Customer' shows a better result. Practical Implications: The purpose of each hospital may alter as demand for plastic surgery grows increasingly. Thus, each hospital should be always prepared to introduce appointment pattern for changed purpose. In order to respond flexibly to these changes, it is necessary for medical personnel to improve the awareness or for hospital to create an environment by constructing appointment program so that medical personnel does not need to put more labor on work.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.79-85
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• 2006

As the portable information appliance is developed, the demand of flat panel display equipments and parts are steeply increased. Most of all, the applications of LCD such as LCD TV, monitor, digital camera, CNS(car navigation system) and game machine become diversified. With the result that the number of BLU production enterprise is increased and the research on the design of backlight with the superior optical properties is persistently in progress. In this study we developed the pattern design tools for CCFL(cold cathode flourescent lamp) backlight to improve the conventional pattern design environment in which the pattern is designed manually from the experience and the trial and error. For the verification of our research, we designed the light reflection surface patterns for a real model of backlight and we measured the brightness uniformity using the BM-7. From the brightness uniformity measurement, the BLU designed using the presented tool showed the tolerable performance only in the first try of pattern design rather than the fifth try of pattern design in case of the conventional pattern design.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.79-89
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• 2014

It has been an increase in consumers who want a high-capacity and fast speed by the newly diffused mobile device(Smart phones, Ultra books, Tablet PC). As a result, the demand for Flash Memory is constantly increasing. Flash Memory is separated by a NAND-type and NOR-type. NAND-type Flash Memory speed is slow, but price is cheaper than the NOR-type Flash Memory. For this reason, NAND-type Flash Memory is widely used in the mobile market. So Fault Detection is very important for Flash Memory Test. In this paper, Proposed PMBIST for Pattern Test of NAND-type Flash Memory improved Fault detection.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1823-1831
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• 2011

When the RPS(Renewable Portfolio Standards) becomes effective in 2012, the use of renewable energy will be dramatically increased. However, there are no production simulations and demand supply programs that reflect the characteristics of the renewable energy. This paper analyzes correlations of the domestic wind power and solar power generation pattern in different areas and those of these sources' output and load pattern. Based on the regional correlation analysis, an appropriate method that uses a average output of the renewable energy or another modeling that takes account of uncertainty could be selected. Because it's output is dependent on weather condition, we can not control the generation of renewable energy, that is the reason why the correlation between the load and output pattern of sources can be helpful to determine whether the renewable energy is modeled as a generator or load modifier. Through this analysis, a basis will be provided in order to properly model the renewable energy source.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.32-40
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• 2009

In order to reduce time-to-market, the demand for fast fault diagnosis has been increased. In this paper, a fault dropping technique with fault candidate ordering and test pattern ordering for fast fault diagnosis is proposed. Experimental results using the full-scanned ISCAS 89 benchmark circuits show the efficiency of the fault dropping technique with fault candidate ordering and test pattern ordering.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1109-1121
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• 2022

As the demand for quality sleep increases in modern society, the importance of sleep technology has increased. Recently, development of sleep environment improvement products and research on the user's sleep improvement have been activated. Representatively, user sleep pattern analysis research is being conducted through the existing polysomnography, but it is difficult to use it in the sleep environment of daily life. Therefore, in this paper, we propose a system design that can provide a customized deep sleep service to users by detecting sleep disturbance factors in a motion bed environment. In order to improve the user's sleep satisfaction, a logistic regression-based sleep pattern analysis model is proposed and accuracy and significance are verified through experiments. And to improve user's sleep satisfaction, we propose a logistic regression-based sleep pattern analysis model and verify accuracy and significance through experiments. The proposed system is expected to improve the user's sleep quality and effectively prevent and manage sleep disorders.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.370-375
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• 2013

This paper attempts to estimate the city gas demand function in Korea over the period 1981-2012. As the city gas demand function provides us information on the pattern of consumer's city gas consumption, it can be usefully utilized in predicting the impact of policy variables such as city gas price and forecasting the demand for city gas. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the city gas demand function. The results show that short-run price and income elasticities of the city gas demand are estimated to be -0.522 and 0.874, respectively. They are statistically significant at the 1% level. The short-run price and income elasticities portray that demand for city gas is price- and income-inelastic. This implies that the city gas is indispensable goods to human-being's life, thus the city gas demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for city gas is price- and income-elastic in the long-run.

• Journal of Energy Engineering
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• v.22 no.3
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• pp.245-249
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• 2013

This paper attempts to estimate the kerosene demand function in Korea over the period 1981-2012. As the kerosene demand function provides us information on the pattern of consumer's kerosene consumption, it can be usefully utilized in predicting the impact of policy variables such as kerosene price and forecasting the demand for kerosene. We apply least absolute deviations and least median squares estimation methods as a robust approach to estimating the parameters of the kerosene demand function. The results show that short-run price and income elasticities of the kerosene demand are estimated to be -0.468 and 0.409, respectively. They are statisitically significant at the 1% level. The short-run price and income elasticities portray that demand for kerosene is price- and income-inelastic. This implies that the kerosene is indispensable goods to human-being's life, thus the kerosene demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for kerosene is price- and income-elastic in the long-run.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Journal of Energy Engineering
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• v.22 no.1
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• pp.1-7
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• 2013

This paper attempts to estimate the residential electricity demand function, using survey data of 521 households in Korea. As the residential electricity demand function provides us information on the pattern of consumer's electricity consumption, it can be usefully utilized in predicting the impact of policy variables such as electricity price and forecasting electricity demands. We apply least absolute deviation(LAD) estimation as a robust approach to estimating parameters. The results showed that price and income elasticities are -0.68 and 0.14 respectively, and statistically significant at the 10% levels. The price and income elasticities portray that residential electricity is price- and income-inelastic. This implies that the residential electricity is indispensable goods to human-being's life, thus the residential electricity demand would not be promptly adjusted to responding to price and/or income change.