• Journal of Energy Engineering
• /
• v.4 no.3
• /
• pp.338-343
• /
• 1995

The objective of this study is to predict the reliability of the electronic control module at ROD control system in nuclear power plant. Maintaining of the reliability is important issue in the complext system like nuclear plower plant, military equipment, satelite system, etc., because the failure of reliability brings etravagant economic loss and deteriorates public acceptance. In addition to the prediction of reliability, the fators affect the reliability including operating condition, environment, temperature and quality factors were analyzed and simulated. The result shows that the quality factors are more critical for the higher reliability than other two factors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1013-1016
• /
• 1993

This paper describes a new type of neuron model, the inputs of which are interfered with one another. It has a high mapping ability with only single unit. The learning speed is considerably improved compared with the conventional linear type neural networks. The proposed neuron model was successfully applied to the prediction problem of chaotic time series signal.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.296-303
• /
• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

• Advances in robotics research
• /
• v.1 no.1
• /
• pp.41-59
• /
• 2014

This paper deals with the problem of steering a group of mobile robots along a reference path while maintaining a desired geometric formation. To solve this problem, the overall formation is decomposed into numerous geometric patterns composed of pairs of robots, and the state of the geometric patterns is defined. A control algorithm for the problem is proposed based on the Nash equilibrium strategies incorporating receding horizon control (RHC), also known as model predictive control (MPC). Each robot calculates a control input over a finite prediction horizon and transmits this control input to its neighbor. Considering the motion of the other robots in the prediction horizon, each robot calculates the optimal control strategy to achieve its goals: tracking a reference path and maintaining a desired formation. The performance of the proposed algorithm is validated using numerical simulations.

• Asia-Pacific Journal of Atmospheric Sciences
• /
• v.54 no.4
• /
• pp.563-573
• /
• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.4
• /
• pp.68-80
• /
• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.1
• /
• pp.140-142
• /
• 2013

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU's are satisfied.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.21 no.2
• /
• pp.46-53
• /
• 2022

This paper presents the design and manufacture of a Solid State Relay (SSR) life prediction device that can predict the lifetime of an SSR, which is a key component of a glass forming machine. The lifetime of an SSR is over when the current supplied to the relay is overcurrent (20 A or higher), and the operating time is 100,000 h or longer. Therefore, the life prediction device for the SSR was designed using DSP to accurately read the current and temperature values from the current and temperature sensors, respectively. The characteristic test of the manufactured non-contact relay life prediction device confirmed that the current and temperature were safely measured. Thus, the SSR lifetime prediction device developed in this study can be used to predict the lifetime of an SSR attached to a glass forming machine.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.27 no.2
• /
• pp.102-109
• /
• 2013

In this paper, a new torque predictive control method of interior permanent magnet synchronous motor is developed based on an extended rotor flux. Also, a duty ratio prediction method is proposed and allows the duty ratio of the active stator voltage vector to be continuously calculated. The proposed method makes it possible to relatively reduce the torque ripple under the steady state as well as to remain the good dynamic response in the transient state. With the duty ratio prediction method, the magnitude and time interval of the active stator voltage vector applied can be continuously controlled against the varying operation conditions. This paper shows a comparative study among the switching table direct torque control(DTC), the SVM-DTC, conventional torque predictive control, and the proposed torque predictive control. Simulation results show validity and effectiveness of this work.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.38 no.2
• /
• pp.20-30
• /
• 2001

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.