• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2451-2453
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• 2002

In this paper, we present an infrared sensors aided map building method for mobile robot using genetic algorithm and fuzzy logic. Existing Bayesian update model using ultrasonic sensors only has a problem of the quality of map being degraded in the wall with irregularity which is caused by the wide beam width of sonar waves and Gaussian probability distribution. In order to solve this problem we propose an improved method of map building using supplementary infrared sensors. In the method, wide beam width of sonar waves is divided by infrared sensors and probability is distributed according to infrared sensors' information using fuzzy logic and genetic algorithm.

• Molecules and Cells
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• v.27 no.1
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• pp.21-37
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• 2009

Map-based cloning to find genes of interest, marker-assisted selection (MAS), and marker-assisted breeding (MAB) all require good genetic maps with high reproducible markers. For map construction as well as chromosome assignment, development of single copy PCR-based markers and map integration process are necessary. In this study, the 132 markers (57 STS from BAC-end sequences, 13 STS from RFLP, and 62 SSR) were newly developed as single copy type PCR-based markers. They were used together with 1830 markers previously developed in our lab to construct an integrated map with the Joinmap 3.0 program. This integrated map contained 169 SSR, 354 RFLP, 23 STS from BAC-end sequences, 6 STS from RFLP, 152 AFLP, 51 WRKY, and 99 rRAMP markers on 12 chromosomes. The integrated map contained four genetic maps of two interspecific (Capsicum annuum 'TF68' and C. chinense 'Habanero') and two intraspecific (C. annuum 'CM334' and C. annuum 'Chilsungcho') populations of peppers. This constructed integrated map consisted of 805 markers (map distance of 1858 cM) in interspecific populations and 745 markers (map distance of 1892 cM) in intraspecific populations. The used pepper STS were first developed from end sequences of BAC clones from Capsicum annuum 'CM334'. This integrated map will provide useful information for construction of future pepper genetic maps and for assignment of linkage groups to pepper chromosomes.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.4
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• pp.54-60
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• 2006

A method for generating the compressor map from some performance measuring data using the hybrid intelligent technique was newly proposed. In order to improve accuracy of the traditional scaling method, a method to generate the compressor map using the GAs(Genetic Algorithms) was previously proposed, but the method has a drawback that it can not find correctly surge and choke points of the compressor map. However, the proposed hybrid intelligent method can determine obviously those points as well as improve the accuracy of the compressor map through complementarily using the GAs and the scaling method.

• Structural Engineering and Mechanics
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• v.63 no.2
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.4
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• pp.341-343
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• 2001

Molecular markers have become fundamental tools for crop genome study. The objective of this study was to construct a genetic linkage map for cowpea with PCR-based molecular markers. Five hundred and twenty random RAPD primers were screened for parental polymorphism. Ninety RAPD markers from sixty primers was segregated in 75 F2 mapping population derived from the cross of local cultivars GSC01 and GSC02. 70 RAPD markers were found to be genetically linked and formed 11 linkage groups. Linkage map spanned 474.1 cM across all 11 linkage groups. There are six linkage groups of 40 cM or more, and five smaller linkage groups range from 4.9 to 24.8 cM. The average linkage distance between pairs of markers among all linkage groups was 6.87 cM. The number of markers per linkage group ranged from 2 to 32. The longest group 1 spans 190.6 cM, while the length of shortest group 11 is 4.9 cM. This map is further needed to be saturated with the various markers such as RFLP, AFLP, SSR and more various populations and primers. In addition, morphological markers and biochemical markers should be united to construct a comprehensive linkage map.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.330-336
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• 2005

Existing Bayesian update method using ultrasonic sensors only for mobile robot map building has a problem of the quality of map being degraded in the wall with irregularity, which is caused by the wide beam distribution. For improving this problem, an infrared sensors aided map building method is presented in this paper. Information of obstacle at each region in ultrasonic sensor beam is acquired using the infrared sensors and the information is used to get the confidence of ultrasonic sensor information via fuzzy inference system and genetic algorithm. Combining the resulting confidence with the result of Bayesian update method, an improve map is constructed. The proposed method showed good results in the simulations and experiments.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.79-87
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• 2004

In this paper, we propose a probability map based genetic algorithm to detect lips from portrait image. The existing genetic algorithm used to get an optimal solution is modified in order to get multiple optimal solutions for lips detection. Each individual consists of two chromosomes to represent coordinates x, y in space. Also the algorithm introduce a preserving zone in the population, a modified uniform crossover, a selection without individual duplication. Using probability map of H, 5 components, the proposed algorithm has adaptability in the segmentation of objects with similar colors. In experiments, we analyzed relationships of primary parameters and found that the algorithm can apply to the detection of other ROIs easily

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.23-28
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• 2014

For the reconstruction of three-dimensional (3D) shape of microscopic objects through shape from focus (SFF) methods, usually a single focus measure operator is employed. However, it is difficult to compute accurate depth map using a single focus measure due to different textures, light conditions and arbitrary object surfaces. Moreover, real images with diverse types of illuminations and contrasts lead to the erroneous depth map estimation through a single focus measure. In order to get better focus measurements and depth map, we have combined focus measure operators by using genetic algorithm. The resultant focus measure is obtained by weighted sum of the output of various focus measure operators. Optimal weights are obtained using genetic algorithm. Finally, depth map is obtained from the refined focus volume. The performance of the developed method is then evaluated by using both the synthetic and real world image sequences. The experimental results show that the proposed method is more effective in computing accurate depth maps as compared to the existing SFF methods.

• Journal of Crop Science and Biotechnology
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• v.11 no.1
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• pp.7-12
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• 2008

Quantitative trait loci, a genetic concept for explaining the inheritance of non-Mendelian traits in 1940s, have been realized as particular fragments of chromosome even unique genes in most crops in 21st century. However, only very a small portion of QTL has been screened out by geneticists comparing to a great number of genes underneath the quantitative traits. These identified QTL even have been seldom used into breeding program because crop breeders may not find the QTL in their breeding populations in their field station. Several key points will be proposed to meet the challenges of QTL analysis today: a fine mapping population and the related reference genetic map, QTL evaluation in multiple environments, recognizing real QTL with small genetic effect, map integration.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.699-704
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• 2000

The present paper deals with a multiobjective optimization method based on the co-evolutionary genetic strategy. The co-evolutionary strategy carries out the multiobjective optimization in such way that it optimizes individual objective function as compared with each generation's value while there are more than two genetic evolutions at the same time. In this study, the designs that are out of the given constraint map compared with other objective function value are excepted by the penalty. The proposed multiobjective genetic algorithms are distinguished from other optimization methods because it seeks for the optimized value through the simultaneous search without the help of the single-objective values which have to be obtained in advance of the multiobjective designs. The proposed strategy easily applied to well-developed genetic algorithms since it doesn't need any further formulation for the multiobjective optimization. The paper describes the co-evolutionary strategy and compares design results on the simple structural optimization problem.