• Journal of the Korean Society of Food Culture
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• v.32 no.4
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• pp.303-310
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• 2017

Kimchi cabbage is in demand all year, but there is an unbalance in its supply and demand due to climate reasons, requiring practical methods for extending storage without high cost. Therefore, this study aimed to assess available storage methods. 'Choongwang' Kimchi cabbages cultivated in Pyeongchang, Gangwon-do were on June 14 harvested and packed in plastic boxes. Control group was treated by loading four to five heads. Moisturized paper was applied as a liner inside the box to prevent dehumidification and damage to the cut root parts, and a small loading amount (three heads) was applied for better air circulation. Weight loss rates after 12 weeks of storage were 13.83% in the control group, 12.57% in the first group, and 13.38% in the second group. Trimming loss rates after 9 weeks of storage were 14.96% in the control group, 12.29% in the first group, and 12.55% in the second group. As a result of the sensory test, the control group lost its marketability after 6 weeks of storage, while the second group maintained it until 9 weeks and the first group maintained it until 12 weeks and scored higher than 6 points. Therefore, the tested methods were effective for extending the freshness of Kimchi.

• ETRI Journal
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• v.31 no.2
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• pp.173-181
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• 2009

The growth of the World Wide Web and web-based applications is creating demand for high performance web servers to offer better throughput and shorter user-perceived latency. This demand leads to widely used cluster-based web servers in the Internet infrastructure. Load balancing algorithms play an important role in boosting the performance of cluster web servers. Previous load balancing algorithms suffer a significant performance drop under dynamic and database-driven workloads. We propose an estimation-based load balancing algorithm with admission control for cluster-based web servers. Because it is difficult to accurately determine the load of web servers, we propose an approximate policy. The algorithm classifies requests based on their service times and tracks the number of outstanding requests from each class in each web server node to dynamically estimate each web server load state. The available capacity of each web server is then computed and used for the load balancing and admission control decisions. The implementation results confirm that the proposed scheme improves both the mean response time and the throughput of clusters compared to rival load balancing algorithms and prevents clusters being overloaded even when request rates are beyond the cluster capacity.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.391-395
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• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.189-196
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• 2009

We propose a hybrid broadcast scheduling based on a combination of periodic and on-demand data scheduling methods for mobile data broadcasting in convergence networks from communication and broadcasting. We consider an environment in which the forward channel is for data broadcasting and the reverse channel is for sending data requests via cellular phones, WLAN, WiBro, etc. Collecting statistics of requests from clients, the server partitions the data items into hot-item and cold-item sets. Hot items are sent based on a push-based scheduling. An on-demand scheduling method is applied to cold items. Performance evaluation from simulations shows that our proposed scheduling algorithm yields small response time with high successful response ratio.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Korea journal of population studies
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• v.25 no.1
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• pp.257-289
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• 2002

In this study we analyse how the tobacco prices have an effect on the national health. The level of tobacco price will fluctuate its consumers demand and eventually affect the national health status. We estimate tobacco consumption function as well as households'demand system in which tobacco and health expenditure functions are included. Demand elasticities are estimated and evaluated in order to find future policies to improve the national health by controlling the national tobacco consumption There are two econometric approaches app1ied in this study. The lent tobacco demand analysis method is mm tobacco consumption function model. Using national indices of tobacco price, tobacco consumption and other related variables, tobacco consumption function is estimated. The other is micro demand system analysis by using Korean urban households expenditure data during the period of 1991 to 1999. The own price elasticity which is estimated from national tobacco demand per capita is -0.19 for all people and -0.176 for the adults over 18, which means 100% price increase will cause decease of tobacco demand at 19% and 17.6% per each The cross vice elasticity which is estimated between tobacco and health expenditure of urban households demand system is -0.2328, which implies 100% of tobacco price increase will decrease 23.28% of health expenditure. The low price elasticities imply that tobacco price increase will increase total tobacco sales volume. 100% of tobacco price increase will bring about 79% increase of total tobacco sales volume according to our scenario. Korea's tobacco demand is negatively responsive to fluctuations in its price. The health expenditure is also negatively relatedto the tobacco price fluctuation. These empirical outputs could be utilized as the basis of government's tax policy to control national tobacco consumption in the future.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.51-58
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• 1993

This paper presents a design of the fuzzy decision maker which infers set value for fuel rate in the rotary kiln of making CaO. The fuzzy decision maker proposed are divided into two groups whose functions are different each other. The one operates when production demand is constant. The other deals with the status of varying production demand. We have chosen several variables used for composing condition and action part by investigating ingerent features of the rotary kiln and skilled operators`manual method of inferring fuel rate. Membership function of each variable was designed by analyzing experimental data and field data collected during two months. On-line operation with fuzzy rules suggested was done safely like human operators' action.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.938-946
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• 2005

We study the firm's strategy to price its products and plan the spare parts manufacturing so as to maximize its profit and at the same time to fulfill its commitment to providing the customers with the key parts continuously over the relevant decision time horizon, i.e., the production plus warrantee period. To examine the research question, we developed and solved a two-stage optimal control theory model. Our analysis suggests that if the cost to produce the spare part during the warrantee period is more expensive than that during the production period, the firm should increase its sales price gradually throughout the production period to control its sales. In addition, during the production period it is optimal for the firm to produce the spare parts more than needed so that the overproduced spare parts can be used to partially meet the demand during the warrantee period. We conducted numerical analysis to investigate the sensitivity dynamics among key variables and parameters such as inventory holding cost, unit spare part production costs, part failure rate, and parameters in the demand function.

• Magazine of the Korean Society of Agricultural Engineers
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• v.33 no.4
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• pp.45-51
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• 1991

The objective of this paper is to show how the fuzzy sets theory can be applied to the reservoir operation guidelines for agricultural purposes. The concepts of the theory has been resented as a new tool for the decision problems which contains fuzziness and it's application can be found in operations research, expert systems, robotics, fuzzy computers, and pattern recognition. The fuzzy control system for the reservoir operation composed of a set of reservoir operation rules and a fuzzy inference engine was built. Water demand for paddy fields, water availability, and inflow to a reservoir were selected as main factors which determine the magnitude of reservoir release. The behavior of the control system was evaluated for different level of water demand and the results seemed to be reasonable.