• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.127-129
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• 2005

Construction of wind power plant is increasing rapidly because Jeju island is known as the most suitable place for wind power plant. Rut wind power plant is difficult electric power control and it has a rapid electric power fluctuation. Such a problem has a bad influence on electric power network in small electric network like Jeju. Therefore, we forecast the amount of wind power plant construction by weather information and the rate of utilization for existing facility. We investigate the contribution degree for electric Power demand, economic effect, the case of power network influence. So we forecast influence of wind power plant for Jeju power network's operation in the near future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.112-120
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• 2003

This paper reports the development work carried out in respect of a proposed application of Neural Network approach for the Korean Next generation Reactor (KNGR) now referred as APR-1400. The emphasis is on establishing the methodology and the approach to be adopted towards realizing this application in the next generation reactors. Keeping in view the advantages and limitation of Artificial Neural Network Approach, the role of ANN has been limited to plant status or to be more precise plant transient monitoring. The simulation work carried out so far and the results obtained shows that artificial neural network approach caters to the requirements of plant status monitoring and qualifies to be incorporated as a part of proposed operator support systems of the referenced nuclear power plant.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.81-83
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• 2021

본 논문에서는 주로 이미지 분할의 목적으로 활용되고 있는 end-to-end 방식의 fully convolutional network 기반의 모델인 U-Net을 사용하여 식물이 포함된 이미지에서 식물과 배경을 분할하는 방법을 제안한다. 네트워크의 훈련을 위해 수동으로 식물을 배경과 분할시킨 이진 영상들을 사용하였다. 다양한 실험을 통하여 U-Net은 식물 영상에서 식물을 정확하게 분할 가능한 것을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.588-590
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• 1997

In this paper, a highly reliable communication network for DCS in nuclear power plant is designed. The structure and characteristics of DCS in nuclear power plant is briefly explained. The features needed for a communication network for DCS in nuclear power plant is described. According to the abovo features, the layer structure of the communication network is determined and each layer is designed in detail.

• 전자공학회논문지B
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• 제30B권1호
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.

• 전기학회논문지
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• 제61권6호
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• pp.859-864
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• 2012

The computer network plays an important role in modern digital controllers within a safety system of a nuclear power plant. For the reliable and realtime data communication between controllers, this paper proposes a modified high-reliable MVB(multi-function vehicle bus) as a main control network for a safety system of a nuclear power plant. The proposed network supports the state-based communication in order to ensure the deterministic communication latency, and very fast network recovery when the bus master fails compare to the standard MVB. This paper also shows the implementation results using a FPGA-based testbed.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2005년도 추계학술대회 및 한일 식물생명공학 심포지엄
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• pp.81-83
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• 2005

The Plant Signaling Network Research Center (SigNet) is a government-funded (by Korea's Ministry of Science and Technology (MOST)/ Korea Science and Engineering Foundation (KOSEF)) research center established at the School of Life Sciences and Biotechnology of Korea University in 2003. The SigNet conducts plant biological studies, especially in the field of developmental and defense biology. The research purpose of SigNet is dissection and analysis of plant development and defense signaling network through multiscientific approaches. Knowledge acquired from SigNet research scientists will provide new integrated view of understanding and potential application of plant development and defense mechanism. The other important mission of the SigNet is nurturing Center of Excellence for future outstanding research scientists of Korea. The SigNet will continue to expend every effort to achieve the goals for the future. Through passionate research endeavor of each laboratory and partnerships within inside and outside laboratories, we will continue to develop world-leading plant research group and to educate new generations of innovative researchers. As the SigNet looks toward the future, the SigNet will try to achieve its mission of research, education and service to the community. And the defense response research of our lab will be presented at later part.

• 대한전기학회논문지
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• 제40권2호
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• pp.183-191
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• 1991

This paper develops a pole-zero assignment self-tuning regulator utilizing the method of a neural network in the plant parameter estimation. An approach to parameter estimation of the plant with a Hopfield neural network model is proposed, and the control characteristics of the plant are evaluated by means of a simulation for a second-order linear time invariant plant. The results obtained with those of Exponentially Weighted Recursive Least Squares(EWRLS) method are also shown.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.