• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.380-383
• /
• 2003

This paper proposes a new and simple input prediction method for robust servo system. This servo system uses robust tracking control system based on both Coprime Factorization(CF) and Zero Phase Error Tracking control system. The CF control system can be designed simply and systematically. Moreover, this system has not only stability but also robustness and disturbance rejection ability The optical disk tracking servo system can detect only the tracking error. So the new and simple input prediction system proposed in this paper estimates the reference input signal from the tracking error. Numerical simulation results show that the proposed method is effective.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.5
• /
• pp.1040-1048
• /
• 2013

In this paper, a sensorless pitch angle control method for a wind generation system is suggested. One-step-ahead prediction control law is adopted to control the pitch angle of a wind turbine in order for electric output power to track target values. And it is shown that this control scheme using the inverse dynamics of the controlled system enables us to predict current wind speed without an anemometer, to a considerable precision. The inverse input-output of the controlled system is realized by use of an artificial neural network. The proposed control and wind speed prediction method is applied to a Double-Feed Induction Generation system connected to a simple power system through computer simulation to show its effectiveness. The simulation results demonstrate that the suggested method shows better control performances with less control efforts than a conventional Proportional-Integral controller.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.622-624
• /
• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.6
• /
• pp.1159-1166
• /
• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.18 no.2
• /
• pp.245-256
• /
• 2016

Prediction of TBM performance is very important for machine selection, and for reliable estimation of construction cost and period. The purpose of this research is to analyze the evaluation process of various prediction models for TBM performance and applied methodology. Based on the solid literature review since 2000, a classification system of TBM performance prediction model is proposed in this study. Classification system suggested in this study can be divided into two stages: selection of input parameter and application of prediction techniques. We also analyzed input and output parameters for prediction model and frequency of use. Lastly, the future research and development trend of TBM performance prediction is suggested.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.1
• /
• pp.1-10
• /
• 2005

This paper proposes a new and simple input prediction method for robust servo system. A robust tracking control system for optical disk drives was proposed recently based on both Coprime Factorization (CF) and Zero Phase Error Tracking (ZPET) control. The CF control system can be designed simply and systematically. Moreover, this system has not only stability but also robustness to parameter uncertainties and disturbance rejection capability. Since optical disk tracking servo system can detect only tracking error, it was proposed that the reference input signal for ZPET could be estimated from tracking errors. In this paper, we propose a new control structure for the ZPET controller. It requires less memory than the previously proposed method for the reference signal generation. Numerical simulation results show that the proposed method is effective.

• Journal of Multimedia Information System
• /
• v.5 no.1
• /
• pp.59-62
• /
• 2018

The delay between input action and visual interface feedback ("Latency") in a touchscreen inking task reduces the user's performance. When the latency is less than 2.38ms, the user cannot perceive the latency in dragging task. This value is difficult to achieve on recent touchscreens and general purpose computers. So, methods of predicting touch points to reduce perceptible latency has been proposed. In general, touch points prediction is not perfect. When using point prediction, feedback of the predicted points is displayed on the screen, after a while, erased when the actual points are displayed. When this task is implemented by software, it causes additional latency to work to erase predicted points feedback. It therefore propose a platform for rendering point prediction feedback without additional latency by the FPGA. This platform transmits input points and HDMI signals rendering feedback of input points to the FPGA. The FPGA draws the feedback of points predicted based on the input points on the HDMI and displays the screen. Since hardware rendering changes the HDMI signal every frame, it does not require erasing work and rendering can be done within an early time regardless of the amount of rendering, so we will reduce the latency.

• Structural Engineering and Mechanics
• /
• v.16 no.1
• /
• pp.83-99
• /
• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.1041-1044
• /
• 1999

In This paper, we investigated the single step prediction for output responses of chaotic system with multi Input multi output using chaotic neural networks. Since the systems with chaotic characteristics are coupled between internal parameters, the chaotic neural networks is very suitable for output response prediction of chaotic system. To evaluate the performance of the proposed neural network predictor, we adopt for Lorenz attractor with chaotic responses and compare the results with recurrent neural networks. The results demonstrated superior performance on convergence and computation time than the predictor using recurrent neural networks. And we could also see good predictive capability of chaotic neural network predictor.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.17 no.5
• /
• pp.123-130
• /
• 2008

In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.