• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.457-460
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• 2000

주가를 예측하는 것은 이미 오래 전부터 여러 가지 방법으로 시도되어 왔었다. 기업의 본질가치를 보는 기본적 분석부터 과거의 자료를 가지고 미래를 예측하는 기술적 분석까지 많은 연구가 있었으나 실제로 모든 예측이 그렇듯이 많이 적중을 했다는 것을 일부의 정형화된 분석방법을 제외하고는 찾지 못하였다. 그럼에도 불구하고 이번 연구에서는 기술적 분석에서 많은 요인들 중에서 기존에 많이 연구해 보지 못한 시계열적인 인자를 가지고 단기간의 주가를 예측하고자 한다. 주식이 상한가에 도달하였을 경우 그 상한가격의 잔량과 그 주식의 일일거래량을 비교하여 그 서로 두 관계가 다음날 주가에 어느 정도의 영향을 미치는지 회귀분석을 통하여 상관성을 분석하고 통계적 자료를 토대로 단기간의 주가를 상한 잔량 대비 일일거래량에 비추어 의사결정 지표를 제시하려고 한다. 적절한 예측결과가 나오게 되면 주식에 대해 매수를 희망하는 사람 뿐 아니라 주식을 보유하고 있는 사람에게 어느 정도 정보효과가 미치게 될 것이라 기대한다.

• Journal of the military operations research society of Korea
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• v.10 no.2
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• pp.51-59
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• 1984

Military personnel encounter a variety of noise environments. During exercises, high intensity noise levels are often encountered. Twenty-four subjects were required to respond to symbols presented under two levels of task difficulty, two levels of presentation rate, two levels of display orientation, and three levels of noise intensity. The purpose of the experiment was to determine whether noise intensity and display orientation had any effect on a short-term memory task. Results showed that continuous white noise at intensity levels of 30, 85, and 105 db had no effect on the shortterm memory task. Presentation rate and task difficulty demonstrated a significant relationship with task performance as did their two-way interaction. This two-way interaction between presentation rate and task difficulty exhibited a different pattern for the two levels of display orientation.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.4
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• pp.11-27
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• 2002

This paper is aimed at surveying the method that supports logical and theoretical back grounds of electricity service pricing, to investigate whether the ESPM can reflect comprehensively the various interests of parties and persons concerned with electricity supply and demand, and analyzing the practical applicability of the model in short-term perspectives. The major findings of this study can be summarized as fellows. First, the ESPM explains what process the equilibrium price is attained through, which is the essential concept and object in evaluating the value of public enterprises or utilities and the price of electricity Second, the ESPM provides the logics and methods that can objectify the discrete price by each electricity user. Third, the ESPM presents theoretical logics and practical methods that can calculate the basic price and the variable price per electricity unit which are key concepts in the two-part tariff. Fourth, the ESPM has powerful practical applicabilities in the reasonable electricity pricing and in the explanation for the balance between parties and persons interested with electricity supply and demand.

• Journal of the military operations research society of Korea
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• v.9 no.2
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• pp.23-30
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• 1983

A simulation study was conducted to determine optimum capacity of Navy drydock facility using GASP-IV, an advanced FORTRAN-based simulation language, under demands of regular overhauls and emergency repairs by ships of an hypothetical fleet composition. Three year dock usage data was analyzed to produce probability distributions underlying drydock repair demands. The present facility size of two drydocks was simulated and was found to be somewhat short of adequate service capability, showing excessive average waiting time and average queue length. The simulation model was then modified to include an additional drydock of similar size as the other two and a year's simulation was again conducted. All repair needs were quite satisfactorily met and all docks showed very high utilization factor (0.98). This contributed to an increase in the fleet's ship availability from 0.95 to 0.99. This study illustrates the usefulness of simulation technique as a tool for analyzing policy alternatives in military long-term investment areas.

• Journal of the military operations research society of Korea
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• v.16 no.2
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• pp.151-167
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• 1990

Considerable research has concentrated on medium-range production planning model in that it can contain long and short-term decision making in contents. Since medium-range production planning deals in situations with setup time or/and costs, solution methodology is classified by (1) optimization method using a sort of integer programming approach and network formulation (2) heuristic method offering an computationally easy, approximate solution. But, solution methodology is different in type of demand generation, existence of capacity and dependant demand. Therefore this paper reviewed the medium range production planning according to above mentioned three factors, and suggested for further work direction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.149-150
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• 2009

The problem of vehicle routing problem(VRP) with pair operations of pick up and delivery are well-known in real applications in logistics networks, as in planning the routes for automatic guided vehicles(AGVs) in an automatic container terminal(ACT), warehouses or in some just-in-time services. This paper will present a formulation to modeling the problem mathematically which can be used to generate optimal routes of carried vehicles in the field to reduce the incurred cost of moving goods. This selected model could be used in (semi-)automatic short-term planning systems for vehicle fleet working in ACT, or in modern warehouses which the long list of requests is parted and sorted in preprocessing orders systems.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Journal of Information Technology Services
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• v.17 no.4
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• pp.75-83
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• 2018

Enterprises need to monitor systems for reliable IT service operations to quickly detect and respond to events affecting the service, thereby preventing failures. Events in non-critical systems can be seen as a precursor to critical system incidents. Therefore, event relationship analysis in the operation of IT services can proactively recognize and prevent faults by identifying non-critical events and their relationships with incidents. This study used the Recurrent Neural Network and Long Short Term Memory techniques to create a model to analyze event relationships in a system and to verify which models are suitable for analyzing event relationships. Verification has shown that both models are capable of analyzing event relationships and that RNN models are more suitable than LSTM models. Based on the pattern of events occurring, this model is expected to support the prediction of the next occurrence of events and help identify the root cause of incidents to help prevent failures and improve the quality of IT services.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.