• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.294-299
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• 2006

This paper studies a visual feedback control scheme for robot manipulators with camera-in-hand configurations. We design a robust controller that compensates for bounded parametric uncertainties of robot mechanical dynamics. In order to reduce steady state tracking error of the robot arms due to uncertain dynamics, integral action is included in the control input. Using the Lyapunov stability criterion, the uniform ultimate boundedness of the tracking error is proved. Simulation and experimental results with a 2-1ink robot manipulator illustrate the robustness and effectiveness of the proposed control algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.55-66
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• 1995

This paper presents an explicit pole-assignment adaptive servocontrol shceme and its application to cutting force regulation for feedrate maximization. The controller structure of the suggested adaptive control scheme is based on robust control theory. This controller structure is then combined with an on-line model estimation algorithm. The whole scheme is applied to a milling process control. The results of real time cutting experimental studies show that the asymptotic regulation of milling peak cutting forces can be achieved with robust- ness against the time varying perturbations to the process model parameters, which are caused by nonlinear cutting dynamics.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.351-359
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• 2007

In this study, a hierarchical switching control scheme based on robust control theory is proposed for tracking control of vehicle longitudinal acceleration in the presence of large uncertainties. A model set consisting of four multiplicative-uncertainty models is set up, and its corresponding controller set is designed by the LMI approach, which can ensures the robust performance of the closed loop system under arbitray switching. Based on the model set and the controller set, a switching index function by estimating the system gain of the uncertainties between the plant and the nominal model is designed to determine when and which controller should be switched into the closed loop. After theoretical analyses, experiments have also been carried out to validate the proposed control algorithm. The results show that the control system has good performance of robust stability and tracking ability in the presence of large uncertainties. The response time is smaller than 1.5s and the max tracking error is about $0.05\;m/S^2$ with the step input.

• ETRI Journal
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• v.28 no.4
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.759-762
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• 1996

In previous work Kwon and Yoo [5] have shown that the FIR tracking algorithm using the input estimation technique. However, it has not solved the problem of systems with parameter uncertainties. Therefore, in this paper we propose a new robust $H_{\infty}$ FIR tracking filter to solve the target tracking problems under systems with parameter uncertainties. Also, we use here the input estimation approach to account for the possibility of maneuver. Simulation results show that the robust $H_{\infty}$ FIR tracking filter proposed here still has good tracking performance for a maneuvering target tracking problem even under all system parameter uncertainties.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.128-133
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• 1999

This paper presents a new delay-dependent robust stabilization condition for uncertain time-delay systems. An algorithm involving convex optimization is proposed to compute a suboptimal upper bound of the delay such that the system can be stabilized by the controller for all admissible uncertainties. It is illustrated by numerical examples that the proposed delay-dependent controller can be less conservative than previous results. It is also shown that the proposed delay-dependent controller can even capture the delay-independent stability of the system, which is not possible with existing delay-dependent results.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.7
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• pp.587-594
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• 2005

In this paper, a robust correlation-based video tracking is proposed to track a moving object in correlated image sequences. A correlation-based video tracking algorithm seeks to align the incoming target image with the reference target block image, but has critical problems, so called a false-peak problem and a drift phenomenon (correlator walk-off. The false-peak problem is generally caused by highly correlated background pixels with similar intensity of a moving target and the drift phenomenon occurs when tracking errors accumulate from frame to frame because of the nature of the correlation process. At first, the false-peaks problem for the ordinary correlation-based video tracking is investigated using a simple mathematical analysis. And, we will suggest a robust selective-attention correlation measure with a gradient preprocessor combined by a drift removal compensator to overcome the walk-off problem. The drift compensator adaptively controls the template block size according to the target size of interest. The robustness of the proposed method for practical application is demonstrated by simulating two real-image sequences.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.108-114
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• 2008

In this paper, we proposed robust blind watermarking algorithm using texture block coefficient based on discrete cosine transform(DCT). The proposed method embedded all of watermark signals into DC component of $8\times8$ block DCT in order to robust various external attack The texture block coefficient was composed absolute value of DCT coefficients. Experiment showed that the proposed method better than conventional methods in the invisibility and various attack such as dithering, cropping, and scaling.