• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.6
• /
• pp.734-743
• /
• 1999

In this paper, a real-time communication method using a PICNET-NP(Plant instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS(Distributed Control System) in nuclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others. In addition, the method was used to analyze the data characteristics of the DCS in existing nuclear power plant. The result shows that use of this method meets the response time requirement(100ms).

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.05a
• /
• pp.268-270
• /
• 2013

The construction project can be defined as a network combined between elements which are engineering, procurement, construction, start-up, and has a plenty of subjects and partners. For the successful project management in construction industry, it is necessary to adopt various management methods, such as lean production, information network system, SCM(Supply Chain Management), which can increase the efficiency of project management. During recent years, even though various management techniques have been applied, the SCM system has not formulated in construction industry. Especially, the Nuclear Power Plant Construction which requires high regulations of the safety and security has not applied SCM. Therefore, this study aimed to propose technology area of SCM in Korean Nuclear Power Plant Construction.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11c
• /
• pp.586-592
• /
• 2000

There is currently no satisfactory way to optimize supplemental lighting in a greenhouse-type plant factory especially concerning plant production. In a commercial plant factory, we got outside radiation data, inside radiation data and lamp running data. They have a correlation, but have much disorder. By using regression, tendency between the outside and the inside including supplemental lighting was found. We could estimate the average transmittance of this plant factory. From this estimation, we could admit the amount of inside radiation was supplied as much supplied compared to natural radiation. Then we are trying to investigate of the production amount and the supplemental lighting. Plant factory is environmentally controlled, the temperature and humidity are not actually controlled stable. We propose a design of neural network model could be useful to estimate the profit resulting from the operation of supplemental lighting.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.61-68
• /
• 2020

Nuclear power plants have a lower cost of power generation, and they are more eco-friendly than other power generation plants. Also, we need to prepare nuclear plant accidents because of their severe damage. In the event of a safety accident, such as a radiation leak, by applying a wireless sensor network to a nuclear power plant, many sensor nodes can be used to monitor radiation and transmit information to an external base station to appropriately respond to the accident. However, applying a wireless sensor network to nuclear power plants requires routing protocols that consider the sensor network size and bypass obstacles such as plant buildings. In general, the hierarchical-based routing protocols are efficient in energy consumption. In this study, we look into the problems that may occur if hierarchical-based routing protocols are applied to nuclear power plants and propose improved routing protocols to solve these problems. Simulation results show that the proposed routing protocol is more effective in energy consumption than the existing LEACH protocol.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.2
• /
• pp.127-137
• /
• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.2
• /
• pp.265-273
• /
• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.4
• /
• pp.551-560
• /
• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.8
• /
• pp.68-74
• /
• 1998

The neural network predictiv econtroller (NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.10
• /
• pp.137-145
• /
• 2010

This paper is proposed non-contact plant growth measurement system using infrared sensor based on USN(Ubiquitous Sensor Network) technologies. The proposed system has used noncontact sensors to reduce any potential damage when it measures the growth of the plant. In this system, plant growth parameters such as diameter, cross-sectional area and thickening form are measured in real-time non-contact method. The measured data are transmitted to remote server by using sensor network technologies, stored and analyzed at the server, and the analyzed data are finally provided for users. In this paper, the proposed plant growth measurement system has been designed and implemented using non-contact infrared sensor based measurement methods and devices, and its performances have been verified by actual measurement experiments at the fields.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1115-1126
• /
• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.