• International Journal of Industrial Entomology and Biomaterials
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• v.15 no.2
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• pp.145-155
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• 2007

Mulberry (Morus spp.) is an economically important tree crop being cultivated in India, China and other sericulturally important countries for its foliage to feed the silk producing insect Bombyx mori L. Genetic improvements of mulberry lag behind to the same in many other economically less important crops due to the complexity of its genetics, the breeding behavior, and the lack of basic information on factors governing important agronomic traits. In this review, the general usage and advantages of different molecular markers including isoenzymes, RFLPs, RAPDs, ISSRs, SSRs, AFLPs and SNPs are described to enlighten their applicability in mulberry genetic improvement programs. Application of DNA markers in germplasm characterization, construction of genetic linkage maps, QTL identification and in marker-assisted selection was also described along with its present status and future prospects.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.121-126
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• 2006

In the recent years, the development of computer-controlled suspension dampers and actuators has improved the trade-off between the vehicle handling and ride comfort, and has led to the development of various damper control policies. The skyhook control is an effective control strategy for suppressing vehicle vibration. In this study, a fuzzy skyhook control is proposed and tuned by a genetic algorithm to improve ride comfort. The proposed fuzzy skyhook control is applied to a quarter-car model in order to compare its performance with continuous skyhook suspensions. To obtain optimized fuzzy skyhook control, scale factors and in-out membership functions are tuned by a genetic algorithm. The simulation results show that the fuzzy skyhook control offers more effective suspension performance over the continuous skyhook control.

• Korean Journal of Ichthyology
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• v.21 no.sup1
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• pp.52-62
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• 2009

Environmental differences of each sea around the Korean Peninsula in terms of factors including topography and complexity of sea current may influence species and genetic diversity of marine fishes. Fish are naturally abundant in the frontal area where various currents or water masses meet. However, this food resource is prone to human overexploitation, threatening the marine ecosystem. New fisheries resources management strategies are needed. Such strategies require information about population structure obtained through morphological and genetic methods.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.293-301
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• 1997

Edge detection is the first step and very important step in image analysis. We cast edge detec¬tion as a problem in cost maximization. This is acheived by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for compar¬ing the performances of different detectors. We used a Genetic Algorithm for maximizing cost func¬tion. Genetic algorithms are a class of adaptive search techniques that have been intensively stud¬ied in recent years and have been prone to converge prematurely before the best solution has been found. This paper shows that carefully chosen modifications(three factors of the crossover opera¬tor) are implemented can be effective in alleviating this problem.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.174-176
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• 1997

This paper proposes a unit commitment scheduling method based on hybrid genetic algorithm(GA). When the systems are scaled up, conventional genetic algorithms suffer from computational time limitations because of the growth of the search space. So greatly reduce the search space of the GA and to efficiently deal with the constraints of the problem, priority list unit ordering scheme are incorporated as the initial solution and the minimum up and down time constraints of the units are included. The violations of other constraints are handled by integrating penalty factors. To show the effectiveness of the proposed method. test results for system of 10 units is compared with results obtained using other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.6
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• pp.53-60
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• 2000

In this paper, we design a GA-fuzzy controller for position control and anti-swing at the destination point. A genetic algorithm is used to complement the demerits such as the difficulty of the component selection of the fuzzy controller, namely, scaling factors, membership functions and control rules. Lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• Proceedings of the Korean Society of Veterinary Pathology Conference
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• 2000.09a
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• pp.19-19
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• 2000

Most renal diseases progress by consecutive cell responses such as hypertrophy, hyperplsia, proliferation, defferentiation, sclerosis, fibrosis and other cellular degenerative process. These cellular responses are mediated by the activation of various mitogens such as vasoconstrictors, growth factors, hormone, genotoxins and cytokines through mechanical, hemodynamic, immunological injury as well as metabolic abnormality. (omitted)

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.479-483
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• 2015

In case of the big city, choosing the adequate root of which we can reach the destination can affect the driver's condition and driving time. so it is quite important to find the optimal routes for arriving the destination as considering the factors, such as driving conditions or travel time and so on. In this paper, we develop route choice model with considering driving conditions and travel time, and it can search the optimal path which make drivers reduce their fatigues using genetic algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.531-535
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• 2004

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

• Journal of Genetic Medicine
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• v.12 no.1
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• pp.1-5
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• 2015

De novo variants (DNVs) can arise during parental germ cell formation, fertilization, and the processes of embryogenesis. It is estimated that each individual carries 60-100 such spontaneous variants in the genome, most of them benign. However, a number of recent studies suggested that DNVs contribute to the pathogenesis of a variety of human diseases. Applications of DNVs include aiding in clinical diagnosis and identifying disease-causing genetic factors in patients with atypical symptoms. Therefore, understanding the roles of DNVs in a trio, with healthy parents and an affected offspring, would be crucial in elucidating the genetic mechanism of disease pathogenesis in a personalized manner.