• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1518-1524
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• 2010

Measures on poor process should be identified by reviewing analysis of planned progress and actual progress for successful performance of Process Control. However, the existing Process Control only performances follow-up measures on poor process but it cannot prevent poor process which is not occurred. To solve these problems, this study suggests the three types of methods of process prediction(Regression Analysis) by using a progress rate which consists of planned progress rate and Actual progress rate.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.3
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• pp.312-319
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• 2004

The optimum dosage control is presumably the goal of every water treatment plant. However it is difficult to determine the dosage rate of coagulant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the real number genetic-fuzzy system was used in determining the dosage rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population which consists of codings of parameter set. To apply this algorithms, we made the real number rule table and membership function from the actual operation data of the water treatment plant. We determined optimum dosages of coagulant(LAS) using the fuzzy operation and compared them with the dosage rate of the actual operation data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.311-316
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• 2007

This paper proposes the algorithm which estimates moment of the inertia and friction coefficient of friction for high performance speed control of electric motor. The proposed algorithm finds the moment of inertia and friction coefficient of friction by observing the speed error signal generated by the speed observer and using Recursive Least-Mean-Square method(RLS). By feedbacking the estimated inertia and estimated coefficient of friction to speed controller and full order speed observer, then the errors of the inertia and coefficient of friction and speed due to the inaccurate initial value are decreased. Inertia and coefficient of friction converge to the actual value within several times of speed changing. Simulation and actual experiment results are given to demonstrate the effectiveness of the proposed parameter estimator.

• Transactions of Materials Processing
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• v.8 no.2
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• pp.200-207
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• 1999

To increase dimensional accuracy of strip width, model to compensate width spread during roughing mill has been developed. To measure the amount of width spread during the production rolling more precisely, special roughing rolling procedures for the horizontal rolling and dog-bone rolling were designed in the actual production mill. From the operational data analysis, it is known that the prime factors influencing on the width spread were strip width, strip thickness, edging amount, number of pass and type of edger roll etc., Based upon the statistical analysis of rolling data, new models which can predict width spread during horizontal rolling and dog-bone rolling were developed and tested on the actual processing conditions for the reliability. The application test showed that newly developed model gave fairly accurate predictions on the width spread during roughing passes.

• Journal of Welding and Joining
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• v.15 no.6
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• pp.116-123
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• 1997

In GMA welding processes, bead contour and penetration patterns are criterion to estimate weld quality. Bead geometry is commonly defined with width, height and depth. When weaving is taken into account, selection of welding conditions is known to be difficult. Thus, empirical or trial-and-error method are usually introduced. This study examined the correlation of welding process variables including weaving parameters with bead geometry using srtificial neural networks(ANN). The main task of the Ann estimator is to realize the mapping characteristics from the sampled welding process variables to the actual bead geometry through training. After the neural network model is constructed, welding process variables for desired bead geometry is selected by inverse model. Experimental varification of the inverse model is conducted through actual welding.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.948-953
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system are possible solutions to settle this problem. In this study. the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested, then the method of forecasting the cooling load using fuzzy logic is suggested by simulating that the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated, and it is shown that the forecasted data approach to the actual data. Operating the ice-storage system by the forecast of cooling load with night electric power will improve the ice-storage system efficiency and reduce the peak electric power load during the summer season as a result.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.531-536
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• 2007

This paper presents a power quality assessment model based on the Analytic hierarchy process. This model can assess unified power quality index which provide an overall performance of the distribution system. To obtain the unified power quality index, we propose the use of the AHP model which has three states: [Ideal]-[Actual]-[Worst]. The proposed method is especially useful and effective for planning. We have applied the proposed method to an actual relatively large system, and verified the usefulness.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.39-44
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• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.755-762
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• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.