• Development and Reproduction
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• v.24 no.4
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• pp.327-335
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• 2020

Genomic DNA (gDNA) extracted from two populations of natural and cultured river pufferfish (Takifugu obscurus) was amplified by polymerase chain reaction (PCR). The complexity of the fragments derived from the two locations varied dramatically. The genetic distances (GDs) between individuals numbered 15 and 12 in the cultured population was 0.053, which was the lowest acknowledged. The oligonucleotide primer OPC-11 identified 88 unique loci shared within each population reflecting the natural population. The OPC-05 primer identified 44 loci shared by the two populations. The average band-sharing (BS) values of individuals in the natural population (0.683±0.014) were lower than in those derived from the cultured population (0.759±0.009) (p<0.05). The shortest GD demonstrating a significant molecular difference was found between the cultured individuals # 15 and # 12 (GD=0.053). Individual # 02 of the natural population was most distantly related to cultured individual # 22 (GD=0.827). A cluster tree was built using the unweighted pair group method with arithmetic mean (UPGMA) Euclidean GD analysis based on a total of 578 various fragments derived from five primers in the two populations. Obvious markers identified in this study represent the genetic structure, species security, and proliferation of river pufferfish in the rivers of the Korean peninsula.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2158-2165
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• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.64-69
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• 1993

The aim of the present paper is to show the effectiveness of Genetic Algorithm for data classification problems in which the classification criteria are not the Euclidean distance. In particular, in order to improve a search performance of Genetic Algorithm, we introduce a concept of the degree of population diversity, and propose construction of genetic operators and the method of calculation for the fitness of an individual using the degree of population diversity. Then, we investigate their performances through numerical simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.61-71
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• 1998

Genetic Algorithm(GA) is a method of approaching optimization problems by modeling and simulating the biological evolution. GA needs large time-consuming, so ti had better do on a parallel computer architecture. Our proposed system has a VLSI-oriented interconnection network, which is constructed from a viewpoint of fractal geometry, so that self-similarity is considered in its configuration. The approach to Parallel Genetic Algorithm(PGA) on our proposed system is explained, and then, we construct the classifier system such that the set of samples is classified into weveral classes based on the features of each sample. In the process of designing the classifier system, We have applied PGA to the Traveling Salesman Problem and classified the sample set in the Euclidean space into several categories with a measure of the distance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.31-41
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• 2003

This paper deals with a cell formation problem for a set of m-machines and n-processing parts. Generally, a cell formation problem is known as NP-completeness. Hence the cell formation problem with multiple objectives is more difficult than single objective problem. The paper considers multiple objectives; minimize number of intercell movements, minimize intracell workload variation and minimize intercell workload variation. We propose a multiple objective genetic algorithms(MOGA) resolving the mentioned three objectives. The MOGA procedure adopted Pareto optimal solution for selection method for next generation and the concept of Euclidean distance from the ideal and negative ideal solution for fitness test of a individual. As we consider several weights, decision maker will be reflected his consideration by adjusting high weights for important objective. A numerical example is given for a comparative analysis with the results of other research.

• International Journal of Industrial Entomology and Biomaterials
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• v.29 no.2
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• pp.169-178
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• 2014

In this study, we investigated genetic diversity of 22 microsporidian isolates infecting tropical tasar silkworm, Antheraea mylitta collected from various geographical forest locations in the state of Jharkhand, India, using polymerase chain reaction (PCR)-based marker assay: random amplified polymorphic DNA (RAPD). A type species, NIK-1s_mys was used as control for comparison. The shape of mature microsporidians was found to be oval to elongate, measuring 3.80 to $5.10{\mu}m$ in length and 2.56 to $3.30{\mu}m$ in width. Of the 20 RAPD primers screened, 16 primers generated reproducible profiles with 298 polymorphic fragments displaying high degree of polymorphism (97%). A total of 14 RAPD primers produced 45 unique putative genetic markers, which were used to differentiate the microsporidians. Calculation of genetic distance coefficients based on dice coefficient method and clustering with un-weighted pair group method using arithmetic average (UPGMA) analysis was conducted to unravel the genetic diversity of microsporidians infecting tasar silkworm. The similarity coefficients varied from 0.059 to 0.980. UPGMA analysis generated a dendrogram with four microsporidian groups, which appear to be different from each other as well as from NIK-1s_mys. Two-dimensional distribution based on Euclidean distance matrix also revealed considerable variability among different microsporidians identified from the tasar silkworms. Clustering of few microsporidian isolates was in accordance with the geographic origin. The results indicate that the RAPD profiles and specific/unique genetic markers can be used for differentiating as well as to identify different microsporidians with considerable accuracy.

• International Journal of Industrial Entomology and Biomaterials
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• v.30 no.1
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• pp.6-16
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• 2015

This study established the genetic characterisation of 10 microsporidian isolates infecting non-mulberry silkworms (Antheraea assamensis and Samia cynthia ricini) collected from biogeographical forest locations in the State of Assam, India, using PCR-based markers assays: inter simple sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD). A Nosema type species (NIK-1s_mys) was used as control for comparison. The shape of mature microsporidian spores were observed oval to elongated, measuring 3.80 to $4.90{\mu}m$ in length and 2.60 to $3.05{\mu}m$ in width. Fourteen ISSR primers generated reproducible profiles and yielded 178 fragments, of which 175 were polymorphic (98%), while 16 RAPD primers generated reproducible profiles with 198 amplified fragments displaying 95% of polymorphism. Estimation of genetic distance coefficients based on dice coefficients method and clustering with un-weighted pair group method using arithmetic average (UPGMA) analysis was done to unravel the genetic diversity of microsporidians infecting Indian muga and eri silkworm. The similarity coefficients varied from 0.385 to 0.941 in ISSR and 0.083 to 0.938 in RAPD data. UPGMA analysis generated dendrograms with two microsporidian groups, which appear to be different from each other. Based on Euclidean distance matrix method, 2-dimensional distribution also revealed considerable variability among different identified microsporidians. Clustering of these microsporidian isolates was in accordance with their host and biogeographic origin. Both techniques represent a useful and efficient tool for taxonomical grouping as well as for phylogenetic classification of different microsporidians in general and genotyping of these pathogens in particular.

• Journal of agriculture & life science
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• v.45 no.3
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• pp.25-33
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• 2011

This study was conducted to examine genetic variation on leaf characteristics of Machilus thunbergii populations. Ten populations were subjected to multivariate analysis for 9 characteristics of leaf morphology. Average length of leaf blade, leaf width, petiole length, vein number were 9.8cm, 4.0cm, 1.8cm, 8.4 respectively, while angle of leaf base and leaf apex were $67.9^{\circ}$ and $78^{\circ}$ respectively. The coefficient of variation (C.V.) on leaf characteristics was 20% which indicate similar features among the populations. Nested analysis showed statistically signigicant differences among populations as well as among individuals within populations. Genetic relationship between populations using complete linkage method showed four groups to Euclidean distance 1.2 and did not show a tendency to cluster into the same group. There were three principal components that had a meaningful eigenvalue over 1.0 among the 9 components. The explanatory power of the top three main components on the total variation was 92.8%. The first principal component (PC) was explained about 40.3% which is mainly correlated with maximum leaf width and the second PC was explained about 28.7% which is correlated with leaf blade length. The third PC was explained about 23.8% which is correlated with petiole length ($X_3$). These characters were important factors for analysis of the relationship among natural populations of M. thunbergii.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.