• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.337-340
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• 2000

In this paper, the effects of the uncertain hourly load demand are stochastically analyzed especially by the consideration of the average over generation of the Unit Commitment(UC) results. In order to minimize the effects of the actual load profile change, a new UC algorithm is proposed. The proposed algorithm calculates the UC results with the lower load level than the one generated by the conventional load forecast. In case of the worse load forecast, the deviation of the conventional UC solution can be overcome with the lower load level and the more hourly reserve requirements. The proposed method is tested with sample systems, which shows that the proposed method can be used as the basic guideline for selecting the potimal load forecast applying to UC problem.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.247-254
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• 2020

The recent Internet environment is characterized by the explosion of certain types of data, as the data that people want is affected by certain issues. In this paper, we propose a load balancing technique that considers the data generation forms. The concept of this technique is to prioritize some type of data when it suddenly explodes. This is a technique to build an add-on middle box on a switch to monitor packets and give priority to a queue for load balancing. This technique worked when certain types of data exploded. SDN(Software Defined Networking) has the advantage of efficiently managing a number of network equipment. However, load balancing in the SDN environment has not been studied much. Applying the proposed load balancing technique in the SDN environment can save time and budget and easily implement our policies. When the proposed load balancing technique is applied to the SDN environment, it has been found that the techniques we want can be easily applied to the network systems, and that efficient data processing is possible when certain types of data explosion.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.11a
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• pp.35-39
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• 2004

In a shipyard, there exist various critical decision making components pertaining to various production hindrances. The most prominent one is best-fit spatial arrangement for the minimal spatial occupancy with better pick-ability for the erection of the ship in the dock. During the present research, a concept have been conceived to evade the gap between the identification oj inter-relationships among a set of blocks to be included on a pre-erection area, and a detailed graphical layout of their positions, is called an Optimal Block Relationship Diagram A research has been performed on generation of optimal (or near Optimal) that is, with minimal scrap area. An effort has been made in the generation of optimal (or near-optimal) Optimal Block Relationship Diagram with the Goldberg's Genetic Algorithms with a representation and a set of operators are 'trained' specifically for this application. The expected result to date predicts very good solutions to test problems involving innumerable different blocks to place. The suggested algorithm could accept input from an erection sequence generator program which assists the user in defining the nature and strength of the relationships among blocks, and could produce input suitable for use in a detailed layout stage.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3236-3240
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• 2007

This paper describes the results of steam turbine performance tests. The objectives of performance test is to exactly evaluate the degradation(decrease in performance) of the coal-fired steam turbine generator in order to provide plant information to help performance engineers identify problems, improve performance, and make economic decisions about scheduling maintenance and optimizing operation. To achieve these goals, the periodic thermal performance tests have been carried out since the initial operation period, 1997. We made the calculation program and guidelines for the tests and developed the performance index of the turbine cycle on the basis of the ASME PTC. By comparing the performance changes throughout the whole operation period, we confirmed the performance reliabilities of the turbine and its conditions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.364-365
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• 2016

컴퓨터 생성 홀로그램(CGH)는 방대한 계산 량을 가지고 있어, 고해상도의 홀로그램을 생성하기 위하여 고속 홀로그램 생성 방법이 필요하다. 본 논문에서는 다중 GPGPU의 스케쥴링 기법을 이용하여 고속화 하는 방법을 제안한다. 첫 번째로는 커널 내에서 공유 메모리를 이용한 스케쥴링 기법을 통하여 고속화를 하고, 두 번째로는 GPGPU간의 P2P(peer-to-peer)데이터 전송을 이용한 스케쥴링을 했다. nVidia의 GTX680 2개 GPGPU를 이용하여 기존의 방법보다 약 50%의 속도 향상을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.213-214
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• 2017

홀로그램을 생성하기 위해서 많은 양의 계산을 필요하기 때문에 고속 홀로그램 생성 방법이 필요하다. 본 논문에서는 다중 프로세서와 다중 GPGPU의 스케줄링을 이용하여 고속화 하는 방법을 제안하고 구현하였다. 다중 프로세서를 이용하여 입력과 출력부분을 나누어 동기화 동작을 줄이고, 버퍼를 이용하여 커널과 커널 사이의 대기 시간을 줄일 수 있도록 스케줄링 하였다. nVidia사의 GTX680(Kepler구조) 2개를 이용하여 구현하였을 때, 이전 연구에서 제안한 방법에 비하여 약 70% 정도 계산시간을 줄일 수 있다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.162-173
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• 1993

항공기 운항계획은 항공사의 계획업무 중 핵심적인 문제로 운항편수와 항공기 출발시간 그리고 운항할 항공기의 기종을 결정하는 문제이다. 본 연구에서는 이익을 최대로 하는 운항계획을 수립하기 위한 새로운 최적해법을 제안한다. 일반적으로 항공기 운항계획은 대규모의 정수 선형계획 문제이기 때문에 기존의 정수 최적해법으로 최적해를 계산하기가 쉽지 않다. 본 연구에서는 모든 결정변수를 사용하지 않고 필요에 따라 일부분의 결정변수만을 생성하여 선형 최적해를 계산하는 열 생성 최적해법을 제안한다. 이 해법을 이용하여 대규모 정수계획인 운항계획의 최적해를 매우 효율적으로 계산할 수 있는 해법을 제안한다. 실제 항공기 운항계획에 본 연구에서 제안하는 최적해법을 적용한 결과를 보여 그 효율성이 월등함을 보인다.