• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.639-647
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• 2009

From development of computer science, genetic algorithm has been applied to many fields for search like non-linear problem based on various variables and optimization process. Among others, in the data mining field, there are methods to select the best input variables for model accuracy and various predict models which were merged by using the genetic algorithm. In the meantime, to improve and preserve quality of the Hanwoo (Korean cattle) which is represented the agricultural industry in our country, we need to find out outstanding economical traits of Hanwoo in having specific genotype of single nucleotide polymorphism (SNP) which is inherited to next generation. According to, This research proposed the selecting method to find genotype of SNPs marker which affects economical traits of the Hanwoo by using the genetic algorithm. And we selected the best genotypes of the principal SNPs marker by applying to real data on Hanwoo genetic.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.6
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• pp.316-325
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• 2006

In diploid organisms like human, each chromosome consists of two copies. A haplotype is a SNP(single nucleotide polymorphism) sequence information from each copy. Finding the complete map of haplotypes in human population is one of the important issues in human genome. To obtain haplotypes via experimental methods is both time-consuming and expensive. Therefore, inference methods have been used to infer haplotyes from the genotype samples. In this paper, we propose a new approach using genetic algorithm to infer haplotypes, which is based on the model of finding the minimum number of haplotypes that explain the genotype samples. We show that by doing a computational experiment, our algorithm has the correctness similar to HAPAR[1] which is known to produce good results while the execution time of our algorithm is less than that of HAPAR as the input size is increased. The experimental result is also compared with the result by the recent method PTG[2].

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.410-416
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• 2003

In the affine projection algorithm(APA), the projection dimension depends on a number of projection basis and of elements of input vector used for updating of coefficients of the adaptive filter. The projection dimension is closely related to a convergence speed of the APA, and it determines computational complexity. As the adaptive filter approaches to steady state, convergence speed is decreased. Therefore it is possible to reduce projection dimension that determines convergence speed. In this paper, we proposed the variable dimension affine projection algorithm (VDAPA) that controls the projection dimension and uses the relation between variations of coefficients of the adaptive filter and convergence speed of the APA. The proposed method reduces computational complexity of the APA by modifying the number of projection basis on convergence state. For demonstrating the good performances of the proposed method, simulation results are compared with the APA and normalized LMS algorithm in convergence speed and computational quantity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2341-2349
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• 2015

The commonly used Goldschmidt's floating-point divider algorithm performs two multiplications in one iteration. In this paper, a tentative error corrected K'th Goldschmidt's floating-point number divider algorithm which performs K times multiplications in one iteration is proposed. Since the number of multiplications performed by the proposed algorithm is dependent on the input values, the average number of multiplications per an operation in single precision and double precision divider is derived from many reciprocal tables with varying sizes. In addition, an error correction algorithm, which consists of one multiplication and a decision, to get exact result in divider is proposed. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a divider unit. Also, it can be used to construct optimized approximate reciprocal tables.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6C
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• pp.539-546
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• 2010

In next generation wireless communication systems based on OFDM, multiple-input multiple-output (MIMO) technique is adopted in order to achieve high data throughput with limited bandwidth. As one of MIMO techniques, spatial multiplexing scheme needs high performance data detection algorithm that can be performed with low computational complexity. In this paper, we propose an algorithm that can compute QRM-MLD with reduced complexity. Also, hybrid detection technique is proposed, which can reduce the complexity by selecting between MMSE and QRM-MLD according to the channel condition. The proposed algorithm provides the trade-off between performance and complexity. The computer simulations for downlink transmission in 3GPP LTE system show that less than 0.1dB performance degradation can be achieved at 0.1% BER with 59% reduction on computational complexity compared with the conventional QRM-MLD algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.69-78
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• 2002

This paper addresses a method to automatically detect out a person's face from a given image that consists of a hair and face view of the person and a complex background scene. Out method involves an effective detection algorithm that exploits the spatial distribution characteristics of human skin color via an adaptive fuzzy color classifier (AFCC), The universal skin-color map is derived on the chrominance component of human skin color in Cb, Cr and their corresponding luminance. The desired fuzzy system is applied to decide the skin color regions and those that are not. We use RGB model for extracting the hair color regions because the hair regions often show low brightness and chromaticity estimation of low brightness color is not stable. After some preprocessing, we apply convex-hull to each region. Consequent face detection is made from the relationship between a face's convex-hull and a head's convex-hull. The algorithm using the convex-hull shows better performance than the algorithm using pattern method. The performance of the proposed algorithm is shown by experiment. Experimental results show that the proposed algorithm successfully and efficiently detects the faces without constrained input conditions in color images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.453-456
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• 2005

In this paper, we propose method to improve recognition performance using the most effective algorithm selectively after clustering various face data, because recognition performance of each algorithm according to facial attribute is change. The proposed face recognition is divided into two steps. First step is the clustering integrated various data to be optimized in algorithm. Second is that classify input image by a similar cluster, select suitable algorithm and recognize the target. This thesis takes the first step towards the creation of a synthetic classifier fusiontesting environment. The effects of data correlation on three classifier fusion techniques were examined. We proposed fusion method for each recognition algorithm's result. This research explores how the degree of correlation in classification data affects the degree of accuracy in a fusion context.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.737-744
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• 2005

In this paper, we propose a three-level color clustering algerian for the binarization of text regions extracted from natural scene images. The proposed algorithm consists of three phases of color segmentation. First, the ordinary images in which the texts are well separated from the background, are binarized. Then, in the second phase, the input image is passed through a high pass filter to deal with those affected by natural or artificial light. Finally, the image Is passed through a low pass filter to deal with the texture in texts and/or background. We have shown that the proposed algorithm is more effective used gray-information binarization algorithm. To evaluate the effectiveness of the proposed algorithm we use a commercial OCR software ARMI 6.0 to observe the recognition accuracies on the binarized images. The experimental results on word and character recognition show that the proposed approach is more accurate than conventional methods by over $35\%$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.431-436
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• 2014

Cell segmentation which extracts cell objects from background is one of basic works in bio-imaging which analyze cell images acquired from live cells in cell culture. In the case of clear images, they have a bi-modal histogram distribution and segmentation of them can easily be performed by global threshold algorithm such as Otsu algorithm. But In the case of degraded images, it is difficult to get exact segmentation results. In this paper, we developed a cell segmentation system that it classify input images by the type of their histogram distribution and then apply a proper segmentation algorithm. If it has a bi-modal distribution, a global threshold algorithm is applied for segmentation. Otherwise it has a uni-modal distribution, our algorithm is performed. By experimentation, our system gave exact segmentation results for uni-modal cell images as well as bi-modal cell images.