• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.107-116
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• 2003

This paper presents a systematic design methodology for fault tolerant controller against a fault in actuators and sensors of linear stochastic systems with uncertainties. The scheme is based on fault detection and diagnosis(isolation and estimation) using a bank of robust two-stage Kalman filters, and accommodation of the actuator fault by eigenstructure assignment and immediate compensation of the sensor's faulty measurement. In order to clarify the fault feature in test statistics of residual, noise reduction method is given by multi-scale discrete wavelet transform. The effectiveness of our approach Is shown via simulations for a VTOL(vertical take-off and landing) aircraft subjected to parameter variations, external disturbances, process and sensor noises.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.58-62
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• 1999

This paper proposes use of acoustic parameters to improve the discriminability among digit models in Korean connected digit recognition. The proposed method used the logarithmic values of energy ratio between the predetermined frequency bands as additional feature parameters, based on the acoustic-phonetic knowledge. The results of our experiment show that the proposed method reduced the error rate by 46% in comparison with the baseline system. And incorporation of channel compensation technique in the proposed method yielded error reduction of about 69%.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.51-55
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• 1990

In this study, a peripheral auditory model used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean placenames and syllables. As a result of this study, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation. The recurrent network in this study reflects well time information of temporal speech signal.

• 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.

• MALSORI
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• no.53
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• pp.103-118
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• 2005

The performance of speech recognition system is degraded by mismatch between training and test environments. Recently, Stereo-based Piecewise LInear Compensation for Environments (SPLICE) was introduced to overcome environmental mismatch using stereo data. In this paper, we propose several methods to improve the conventional SPLICE and evaluate them in the Aurora2 task. We generalize SPLICE to compensate for covariance matrix as well as mean vector in the feature space, and thereby yielding the error rate reduction of 48.93%. We also employ the weighted sum of correction vectors using posterior probabilities of all Gaussians, and the error rate reduction of 48.62% is achieved. With the combination of the above two methods, the error rate is reduced by 49.61% from the Aurora2 baseline system.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.33-36
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• 2007

In this paper, we investigate a fast speaker adaptation method based on eigenvoice in several noisy environments. In order to overcome its weakness against noise, we propose a noisy environment clustering method which divides the noisy adaptation utterances into utterance groups with similar environments by the vector quantization based clustering using a cepstral mean as a feature vector. Then each utterance group is used for adaptation to make an environment dependent model. According to our experiment, we obtained 19-37 % relative improvement in error rate compared with the simultaneous speaker adaptation and environmental compensation method

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1053-1059
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• 2010

This paper treats the DIS (Digital Image Stabilization) problem subject to base motions such as translation, rotation and zoom. For the local motion estimation from a raw image, the Harris corner detection algorithm is exploited to extract feature points, and comparing those of consecutive images, the zoom ratio (scale factor) is computed. For the global motion estimation, an equivalent model is derived to account for a 3-dimensional composite motion from which the center point and Euler angle can be determined. Finally, the motion compensation follows. To show the effectiveness of the present DIS scheme, experimental results for synthetic images are illustrated.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.157-162
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• 1990

In this study, a peripheral auditory model is used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean place names and syllables. In the case of using the general learning rule, it is found that the weights are diverged for a long sequence because of the characteristics of the node function in the hidden and output layers. So, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation and to use long data. The recognition results are considerably good, even if time worping and endpoint detection are omitted and learning patterns and test patterns are made of average length of data. The recurrent network used in this study reflects well time information of temporal speech signal.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.7-10
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• 2006

본 논문에서는 음성의 모델을 이용하여 확률적인 기반으로 잡음의 마스킹 정도를 측정하는 방법에 대해서 제시한다. 잡음의 마스킹 정도를 측정하는 기준으로서 '잡음 마스킹 확률'을 구하는 방법에 대해서 설명하고 이의 특성에 대해서 알아본다. 그리고 잡음에 대한 '잡음 마스킹 확률'을 이용하여 잡음 환경에서의 음성인식 특징벡터의 성능 향상에 대해 적용해 보았다. 제안된 방법은 ETSI 에서 음성인식 표준실험으로 제시한 Aurora2 데이터베이스 상에서 실험해 보았다. 그 결과 기존의 알고리즘에 비해 16.58%의 성능 향상을 이루어 낼 수 있었다.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.2
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• pp.84-92
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• 2008

Free-form shapes which were once considered as an aesthetic feature are now an important functional requirement. CNC industries are looking for a compact solution for reproducing free-form shapes as conventional interpolation models are inadequate, The parametric curve interpolator developed in the last decade has clearly emerged as favorite among its contemporaries in recent years, At present intense research has been done on parametric curve interpolators and interesting developments are reported. Out of the various parametric representations for curves and surfaces, NURBS has been standardized and widely used in free-form shape design. This paper presents a review of various methods of parametric interpolation for NURBS and discusses the salient features, problems and solutions. Recent approaches on variable feedrate interpolation, parameter compensation are also reviewed and research trends are addressed finally.