• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.9
• /
• pp.569-575
• /
• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.69-72
• /
• 1996

In this paper robust tracking control scheme using the new three-segment nonlinear sliding mode technique for nonlinear rigid robotic manipulator is developed. Sliding mode consists of three segments, the promotional acceleration segment, the constant velocity segment and the deceleration segment using terminal sliding mode. Strong robustness and fast error convergence can be obtained for rigid robotic manipulators with large uncertain dynamics by using the new three-segment nonlinear sliding mode technique together with a few useful structural properties of rigid robotic manipulator. The efficiency of the proposed method for the tracking has been demonstrated by simulations for two-link robot manipulator.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.570-572
• /
• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.5
• /
• pp.2171-2185
• /
• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.1
• /
• pp.22-29
• /
• 2004

This paper studies the coordinated trajectory control of an excavator as a kind of robotic manipulators driven by hydraulic actuators. Hydraulic robot system has many non-linearity in dynamics and kinematics, and strong coupling among joints(or hydraulic cylinders). This paper proposes a combined controller frame of the adaptive robust control(ARC) and the sliding mode control(SMC) for the trajectory tracking control of the excavator to preserve the advantages of the both methods while overcoming their drawbacks, namely, asymptotic stability of adaptive system for parametric uncertainties and guaranteed transient performance of sliding mode control for both parametric uncertainties and external disturbance. The suggested control technique is applied for the tracking of a straight-line motion of end-effector of manipulators, and through computer simulations, its trajectory tracking performances and the robustness to payload variation and uncertainties are illustrated.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2016-2019
• /
• 2003

In this paper, we take some features for face recognition out of face image, using a simple type of templates. We use the extracted features to do Adaboost learning for face recognition. Using a carefully-chosen feature among these features, we can make a weak face classifier for face recognition. And doing Adaboost learning on and on with those chosen several weak classifiers, we can get a strong face classifier. By using Adaboost Loaming, we can choose particular features which is not easily subject to changes in illumination and facial expression about several images of one person, and construct face recognition system. Therefore, the face classifier bulit like the above way has robustness in both facial expression and illumination variation, and it finally gives capability of recognizing face fast due to the simple feature.

• Journal of Technology Innovation
• /
• v.10 no.2
• /
• pp.91-105
• /
• 2002

This paper extends the empirical analysis on R&D based growth model so that the nonstationary panel unit root testing methods can be used to distinguish the exogenous growth model and R&D based growth model for the 1981-1999 period with fourteen OECD economies including Korea. Our results show that first, using U.S. and Group mean as benchmarking, the stochastic R&D productivity convergence to benchmarking is not supported in our data set. Second, the empirical results for stochastic nonconvergence to the U.S. or group mean also are robustness to panel unit root methods. We, therefore, find strong support for the implications for R&D based growth model.

• Journal of the Korean Data and Information Science Society
• /
• v.11 no.2
• /
• pp.235-245
• /
• 2000

In this paper, we consider the Regression Quantiles Estimators in nonlinear regression models. This paper provides the sufficient conditions for strong consistency and asymptotic normality of proposed estimation and drives asymptotic relative efficiency of proposed estimatiors with least square estimation. We give some examples and results of Monte Carlo simulation to compare least square and regression quantile estimators.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1928-1931
• /
• 2002

Orthogonal Frequency Division Multiplexing or OFDM is a form of multi-carrier modulation technique. High spectral efficiency, robustness to channel fading, immunity to impulse interference, uniform average spectral density, capability of handling very strong echoes and less on linear distortion are among the favorite properties of OFDM. Even though there are many advantages of OFDM, t has two main drawbacks: high Peak to Average Power Ratio (PAPR) and frequency of offset. In this paper, the issue of PAPR in OFDM is discussed. A new algorithm is proposed to reduce PAPR by selecting the input sequences property using a lookup table.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1565-1572
• /
• 2015

In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.