• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1A
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• pp.48-55
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• 2000

In this paper, an assumption to design a quantizer, is proposed that if one small region of a probability density function is represented larger probability and bigger total error than another neighbour region, then the quantizer is not optimal. It is tested when the probability functions are Gaussian, Laplacian and uniform density function by the computer simulations. A new LBG algorithm which originates from this assumption in addition to LBG algorithm, is designed for the vector quantizer. The new LBG algorithm presents better performance than the original LBG algorithm in the average error and the variance of the error.

• The Journal of the Acoustical Society of Korea
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• v.23 no.7
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• pp.548-553
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• 2004

In this paper we propose modified LBG algorithm to minimize quantization errors. When we apply conventional LBG algorithm for speaker verification system, problems that result from small amount of training data can be generated. That is, quantization error comes from fixed-sized codebook without any consideration for speaker characteristics and splitting vector in the wrong direction worsen performance of speaker verification system. So, we propose modified clustering method that has variable sized codebook according to speaker characteristics and makes right splitting direction by finding the farthest member away from mean and then find another member from the member. Simulation results show effectiveness of the proposed algorithm.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.23-36
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• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.579-582
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• 2001

Vector Transformation is a new method unified vector quantization and coding. So far, codebook generation applied to coding was LBG algorithm. But using the advantage of SOFM(Self-Organizing Feature Map) based on neural network can improve a system's performance. In this paper, we generated VTC(Vector Transformation Coding) codebook applied with SOFM algorithm and compare the result for several coding rates with LBG algorithm. The problem of Vector quantization is complicated calculation and codebook generation. So, to solve this problem, we used neural network approach method.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.107-112
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• 1996

In this paper we propose a new fast codebook training algorithm for reducing the searching time of LBG algorithm. For each training data, the proposed algorithm stores the indexes of codewords that are close to that training data in the first iteration. It reduces computation time by searching only those codewords, the indexes of which are stored for each training data. Compared to one of the previous fast training algorithm, FSLBG, it obtains a better codebook with less exccution time. In our experiment, the performance of the codebook generated by the proposed algorithm in terms of peak signal-to-noise ratio(TSNR) is very close to that of LBG algorithm. However, the codewords to be searched for each training data of the proposed algorithm is only about 6%, for a codebook size of 256 and 1.6%, for a codebook size of 1.24, of LBG algorithm.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4E
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• pp.123-127
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• 2005

In this paper, we propose a new method for reducing the number of distance calculations in the LBG (Linde, Buzo, Gray) algorithm, which is widely used method to construct a codebook in vector quantization of speech recognition system. The proposed algorithm can reduce the distance calculation between input vector and codeword by utilizing the observation that codewords are quickly stabilized as the number of iteration increases. From the simulation results, it is shown that we can reduce the running times over $43.77\%$ on average in comparison with current LBG algorithm without sacrificing the performance of codebook.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.181-185
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• 2009

When we classify underwater transient signals with frame-by-frame decision, a database design method for reference feature vectors influences on the system performance such as size of database, computational burden and recognition rate. In this paper the LBG vector quantization algorithm is applied to reduction of the number of feature vectors for each reference signal for efficient classification of underwater transient signals. Experimental results have shown that drastic reduction of the database size can be achieved while maintaining the classification performance by using the LBG vector quantization.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.2
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• pp.151-157
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• 2013

In this paper, properties of pattern using LBG (Linde-Buzo-Gray) Algorithm was explored including the exactness of K-means algorithm and process time of EM (Expectation Maximization) algorithm in order to develop analysis algorithm of partial discharge pattern in a cable using acoustic data analysis system. Partial discharge was measured by generating inner fault due to lamination of XLPE which is used for cable insulation material. Discharge pattern was analysed by changing the number of swarm article to 2, 4, and 6 in order to interpret swarm structure and properties.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.109-116
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• 2006

Many application scenarios of ultra-wideband(UWB) radio assume non-line-of-sigit(non-LoS) signal propagations. Through-material propagation of UWB signal introduces a distortion of the waveform as well as attenuation, which will introduce a decrease of the correlation coefficient between the correlator template and the received signal. A hypothesis test approach to selection of the template waveform for UWB rake receivers is posed. Linde-Buzo-Gray(LBG) algorithm is used to select the candidate waveforms which are used to setup the hypothesis test. The performance of the algorithm is tested using a set of indoor non-LoS propagation measurement data.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1737-1744
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• 2004

In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.