• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.9-15
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• 2019

Sensorless electric superchargers have recently been actively developed to provide a large amount of oxygen to engines in order assist the combustion process for miniaturizing the engines and improving fuel efficiency. The model-based sensorless method for surface-mounted permanent magnet synchronous motors has a disadvantage in that the system may become unstable due to parameter variations in low-speed operation and the rapid-acceleration section. An electric supercharger requires fast response to improve the engine response delay, such as the turbocharger turbo-rack. Therefore, the responsiveness must be improved to use the model-based sensorless system. The position compensation algorithm designed in this study is controlled by converting the position error into the beta, which is the angle formed by the d-axis and the stator current during sudden speed change. In this study, we improved the response of the model-based sensorless system through the algorithm and verified the algorithm validity by applying the algorithm to an actual dual-motor supercharger.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.698-701
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• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.379-386
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• 2013

The hemispherical resonator gyroscope is a type of vibratory gyroscope, which can measure angle or angular rate, based on its operating mode. This paper deals with the case when the hemispherical resonator gyroscope is operated in angle measurement mode. In angle measurement mode, the resonator pattern angle precesses, with respect to the external rotation input, by the principle of the Coriolis effect, so that the external rotation can be estimated, by measuring the amount of precession angle. However, this pattern angle drifts, due to the manufacturing error of the resonator. Since the drift effect causes degradation of the angle estimation performance of the resonator, the corresponding drift compensation control should be performed, to enhance the estimation performance. In this paper, a mathematical model of the hemispherical resonator gyro is first introduced. By using the mathematical model, a nonlinear observer for imperfection parameter estimation, and the corresponding compensation controller are designed to operate hemispherical resonator gyros, as angle measurement sensors.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.96-108
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• 1996

In this paper, we propose an object-oriented coding method in low bit-rate channels using block-based motion vectors and residual image compensation. First, we use a 2-stage algorithm for estimating motion parameters. In the first stage, coarse motion parameters are estimated by fitting block-based motion vectors and in the second stage, the estimated motion parametes are refined by the gradient method using an image reconstructed by motion vectors detected in the first stage. Local error of a 6-parameter model is compensted by blockwise motion parameter correction using residual image. Finally, model failure (MF) region is reconstructed by a fractal mapping method. Computer simulation resutls show that the proposed method gives better performance than the conventional ones in terms of th epeak signal to noise ratio (PSNR) and compression ratio (CR).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.4
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• pp.503-510
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• 2003

A new method of induction motor drive, which requires neither shaft encoder nor speed estimator, is presented. The proposed scheme is based on decreasing current gap between a numerical model and an actual motor. By supplying the identical instantaneous voltage to both model and motor in the direction of reducing the current difference. the rotor approaches to the model speed. that is. reference value. The indirect field orientation algorithm is employed for tracking the model currents. The performance of induction motor drives without speed sensor is generally characteristic of poorness at very low speed. However, in this system, it is possible to obtain good speed response in the extreme low speed range.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.91-100
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• 2018

The setting of values on door hinge mounting compensation for door assembly tolerance is a constant quality issue in vehicle production. Generally, heuristic methods are used in satisfying appropriate door gap and level difference, flushness to improve quality. However, these methods are influenced by the engineer's skills and working environment and result an increasement of development costs. In order to solve these problems, the system which suggests hinge mounting compensation value using CAE (Computer Aided Engineering) analysis is proposed in this study. A structural analysis model was constructed to predict the door gap and level difference, flushness through CAE based on CAD (Computer Aided Design) data. The deformations of 6-degrees of freedom which can occur in real vehicle doors was considered using a stiffness model which utilize an analysis model. The analysis model was verified using 3D scanning of real vehicle door hinge deformation. Then, system model which applying the structural analysis model suggested the final adjustment amount of the hinge mounting to obtain the target door gap and the level difference by inputting the measured value. The proposed system was validated using the simulation and showed a reliability in vehicle hinge mounting compensation process. This study suggests the possibility of using the CAE analysis for setting values of hinge mounting compensation in actual vehicle production.