• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.339-341
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• 2006

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor current it is modeled by a neural network that is configured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which fast inputs and outputs are used to calculate the current output. Using the model, effective estimator to compensate the effects of disturbance has been designed. The effectiveness of the proposed current estimator is verified through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.140-145
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• 1994

In this paper, we consider a problem to estimate process parameters using input-output data collected from the process operating in closed-loop control system. When orders and delay-time of the process are known correctly, under some conditions of identifying experiments, it is reported that accurate identification results can be obtained by applying prediction error method. To get accurate estimates, it is necessary to know orders and delay-time of the process. It is difficult to determine them in closed-loop identification, because ill-condition for identification are easily caused by selection of unsuitable order or delay time. Furthermore, the procedures to select orders and delay-time in open-loop identification aren't always available in closed-loop identification. The purpose of this paper is to determine a delay-time under suitable assumption that order of the process are known as the first step.

• Journal of the Korean Ceramic Society
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• v.41 no.5
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• pp.353-358
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• 2004

Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribu-tion and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network (ANN) approaches, namely the Back-Propagation Multiplayer Perceptron (BPMLP) algorithm and the Radial Basis Function (RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

• Journal of Forest and Environmental Science
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• v.28 no.2
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• pp.63-67
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• 2012

Crown ratio is the ratio of live crown length to tree height. It is often used as an important predictor variable for tree growth equation. It indicates tree vigor and is a useful parameter in forest health assessment. The objective of the study was to develop crown ratio prediction models for Tectona grandis. Based on the data set from the temporary sample plots, several non linear equations including logistics, Chapman Richard and exponential functions were tested. These functions were evaluated in terms of coefficient of determination ($R^2$) and standard error of the estimate (SEE). The significance of the estimated parameters was also verified. Plot of residuals against estimated crown ratios were observed. Although the logistic model had the highest $R^2$ and the least SEE, Chapman-Richard and Exponential functions were observed to be more consistent in their predictive ability; and were therefore recommended for predicting crown ratio in the stand.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.140-142
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• 2007

This paper presents an effective method of precise velocity control at low speed with a low resolution encoder. Multirate observer to estimate the velocity at every DSP control period is used except a constant velocity mode. The observer corrects the estimation error when detects pulse signal. Unlike the conventional methods, the multirate estimator is stable at a low speed. However, the multirate estimator shows ripples at a constant velocity. Thus, in this paper we use a velocity prediction method which uses the present velocity from the previous average velocity to reject the ripple. In a summary, at a constant speed mode, the predicted velocity is used. Otherwise, the estimated velocity by the multirate obvserver is used. The effectiveness of the multirate observer and ripple rejection at low speed is verified through various simulations.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.891-901
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• 2009

In this paper, we investigate the approach to estimate VaR under the transformed GARCH model. The time series are transformed to approximate to the underlying distribution of error terms and then the parameters and the one-sided prediction interval are estimated with the transformed data. The back-transformation is applied to compute the VaR in the original data scale. The analyses on the asset returns of KOSPI and KOSDAQ are presented to verify the accuracy of the coverage probabilities of the proposed VaR.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.433-436
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• 2000

In this paper, we present two types of digital image stabilization (DIS) schemes for mobile video communications. In the first scheme, the DIS system, which is used as a preprocessor of the video encoder, compensates the camera’s undesirable shakes before encoding. This method can reduce the bit rate of encoded video sequence by attenuating the prediction error to be encoded. In the second proposed scheme, the DIS system is coupled with the video decoder. The second scheme uses the K-means clustering algorithm to estimate the camera motion using motion vectors decoded from the received video stream. Simulation results show that the first scheme improves coding efficiency, while the second scheme is computationally efficient since it does not require motion estimation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.589-595
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• 2007

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor CUlTent it is modeled by a neural network that is contigured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a state feedback controller to compensate the effects of disturbance has been designed. The controller is implemented by a 16-bit microprocessor and the effectiveness of the proposed control method is verified through experiments.

• Steel and Composite Structures
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• v.33 no.2
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• pp.181-194
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• 2019

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.