• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.397-401
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• 1997

본 논문은 신경회로망과 Gabor변환을 홍채인식에 대한 연구이다. 현재 재발되고 있는 신원확인을 위한 여러 가지 인식 시스템 중 홍채인식의 특성과 비교우위적 장점을 소개하고, LVQ 신경회로망을 효과적인 초기화 방법과 Gabor변환을 이용한 홍채테이터의 특징추출에 대하여 논한다.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.241-252
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• 2000

In this paper, a translation, rotation and scale invariant system for the recognition of closed 2-D images using the bispectrum of a contour sequence and a weighted fuzzy classifier is derived and compared with the recognition process using one of the competitive neural algorithm, called a LVQ( Loaming Vector Quantization). The bispectrum based on third order cumulants is applied to the contour sequences of an image to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to the represent two-dimensional planar images and are fed into a weighted fuzzy classifier. The experimental processes with eight different shapes of aircraft images are presented to illustrate a relatively high performance of the proposed recognition system.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.478-481
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• 2000

In this paper, we propose a joint coding system for still images using source coding and powerful error correcting code schemes. Our system comprises an LVQ (lattice vector quantization) source coding for wavelet transformed images and turbo coding for channel coding. The parameters of the image encoder and channel encoder have been optimized for an n-D (dimension) cubic lattice (D$_{n}$, Z$_{n}$), parallel concatenation fur two simple RSC (recursive systematic convolutional code) and an interleaver. For decoding the received image in the case of the AWGN (additive white gaussian noise) channel, we used an iterative joint source-channel decoding algorithm for a SISO (soft-input soft-output) MAP (maximum a posteriori) module. The performance of transmission system has been evaluated in the PSNR, BER and iteration times. A very small degradation of the PSNR and an improvement in BER were compared to a system without joint source-channel decoding at the input of the receiver.ver.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1026-1035
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• 1997

In this paper, we studied for phoneme recognition using GPFN and PNN as a kind of RBFN. The structure of RBFN is similar to a feedforward networks but different from choosing of activation function, reference vector and learnign algorithm in a hidden layer. Expecially sigmoid function in PNN is replaced by one category included exponential function. And total calculation performance is high, because PNN performs pattern classification with out learning. In phonemerecognition experiment with 5 vowel and 12 consant, recognition rates of GPFN and PNN as a kind of RBFN reflected statistic characteristic of speech are higher than ones of MLP in case of using test data and quantizied data by VQ and LVQ.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.109-112
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• 2006

본 논문은 클래스들의 대표값들과 입력 벡터와의 거리를 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 이 새로운 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류 역전파 신경회로망과 LVQ 알고리즘보다 성능이 우수하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.81.3-81
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• 2002

$\textbullet$ Contents 1 Introduction $\textbullet$ Contents 2 EMG Signals and Measurement $\textbullet$ Contents 3 Intelligent Algorithm $\textbullet$ Contents 4 Improved LVQ $\textbullet$ Contents 5 Experiment $\textbullet$ Contents 6 Conclusion

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.21-24
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• 2002

We proposed the intelligent system to control prosthetic robot. The proposed intelligent system was used competitive network, SOFM and LVQ, and consisted of pre-processing part and associative part. A pre-processing part was processed EMG signal and associative part was outputted signal to control prosthetic robot. To verify the effectiveness, we adapted to 2 link manipulator for korean consonant.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1017-1018
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• 2006

We measured EEG, extracted the feature vectors using alpha and beta rhythm from the measured EEG and pattern recognition was simulated by using the feature vector and the algorithms which are conventional LVQ and Forward only Counter Propagation Networks. And then the successful rate of pattern class of EEG data had about 76 %.