• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.59-64
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• 2006

The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved in order to achieve the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties). The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and design of mill requirement. To achieve this objectives, a new teaming method with neural network to improve the accuracy of rolling force prediction in hot rolling mill is developed. Also, Genetic Algorithm(GA) is applied to select the optimal structure of the neural network and compared with that of engineers experience. It is shown from this research that both structure selection methods can lead to similar results.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.3
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• pp.445-452
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• 2002

The main objective of modern reproductive technologies in pig reproduction is to increase reproductive efficiency and rates of genetic improvement. They also offer potential for greatly extending the multiplication and transport of genetic materials and the conservation of unique genetic resources in reasonably available forms for possible future use. The development and refinement of these technologies is concentrating on gamete and embryo collection, sorting and preservation, in vitro production of embryos, culturing, manipulation of embryos (splitting, nuclear transfer, production of chimeras, establishment embryo stem cells, and gene transfer) and embryo transfer. Also, the development of these novel technologies is facilitated by modern equipment for ultrasonography, microscopy, cryopreservation, endoscopy, and flow cytometry, microinjectiors, micromanipulators and centrifugation. The real impact on herd productivity will come from combining new reproductive techniques with powerful DNA technologies. The new reproductive techniques will allow a rapid turnover of generations, whereas the DNA technology can provide selection, which does not need phenotypic information when the selection decisions are made.

• Korean Management Science Review
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• v.14 no.1
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• pp.67-76
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• 1997

The most research on facility layout problems ignored the actual shapes of activity spaces and the aisles between activities. In many cases, the research also ignored the actual shape of building where the activities are to be arranged. In this paper, We present a block based layout technique that applies a genetic algorithm to search for a very good facility layout with horizontal aisles. From the extensive experiments for two different cases with respect to the shape of activity space, it was found that the proposed method generated better layouts than the ones obtained by applying Tam's algorithm in all test problems. The proposed algorithm showed about 10% improvement of performance on the average. We determined the best combination of the reproduction rule and the genetic operators with their probabilities for each test problem through the experiment.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2075-2076
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• 2006

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Backpropagation(BP). The conventional BP does not guarantee that the BP generated through learning has the optimal network architecture. But the proposed GA-based BP enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional BP. The experimental results in BP neural network optimization show that this algorithm can effectively avoid BP network converging to local optimum. It is found by comparison that the improved genetic algorithm can almost avoid the trap of local optimum and effectively improve the convergent speed.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• Korean Journal of Plant Resources
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• v.10 no.3
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• pp.258-264
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• 1997

The introduction of molecular biology methodologies to plant improvement programs offers an invaluable opportunity for extensive germplasm characterization. We have applied the developed technique of random amplification of polymorphic DNA(RAPD)to the analysis of evaluating genetic diversity among Korean wild Codonopsis lanceolata. A total of 340 polymorpic hands were gernerated on agarose- and polyacrylamide-gel by 19 primers of abitrary sequence. grouped by cluster analysis using sample matching coefficients of similarity. Among of the samples. the minimum genetic distance value was obtained between sample no. 1(Girisan) and no. 2(Girisan), and the largest value between sample no. 11(Sulaksan) and no. 17(Sulaksan).In separate cluster dendrograms based on agareose - and polyacryamide-gel. some differences were observed; In the case of agarose gel,41 samples could be devided into 7 groups at below about 0.44 level of distance. However they were divided into 6 gourps at below about 0.40 level of distance in the case of polyacrylamide gel. These results showed that polymophic data in agrose were not grouped to wild plant selected from each mountainous district except for wild plants selected from Sulaksan and Chiaksan. We believe that polyacrylamide-RAPD is a superior method for detecting DNA polymorphism compared to agarose-RAPD method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1349-1350
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• 2015

풍속은 다른 기상요소들보다 순간 변동이 심하고 국지성이 강하여 수치 예보 모델만으로 예측의 정확성을 높이기가 어렵다. 기상청의 단기 풍속 예보는 전 지구적 통합 예보모델인 UM(Unified Model)의 예측값에 MOS(Model Output Statictics)를 통한 보정을 수행하며, 보정식의 생성에 다중선형회귀분석 방법을 사용한다. 본 연구자는 유전프로그래밍(Genetic Programming)을 이용한 비선형 회귀분석 기반의 보정식 생성을 통하여 이를 개선한 바 있는데, 본 연구에서는 보다 향상된 성능을 얻기 위하여 GP 기법 측면에서 Automatically Defined Functions과 다군집(Multiple Populations) 수행을 통해 성능을 높이고자 한다.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.152-158
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• 2005

By using a combination of different feature sets extracted from input character patterns, we can improve the character recognition system performance. To reduce the dimensionality of the combined feature vector, we conduct the feature selection. This paper proposes a general framework for the feature combination and selection for character recognition problems. It also presents a specific design for the handwritten numeral recognition. Tn the design, DDD and AGD feature sets are extracted from handwritten numeral patterns, and a genetic algorithm is used for the feature selection. Experimental result showed a significant accuracy improvement by about 0.7% for the CENPARMI handwrittennumeral database.

• Journal of computational fluids engineering
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• v.10 no.2
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• pp.54-59
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• 2005

For the improvement of aerodynamic performance of the turbine blade in a turbopump for the liquid rocket engine, the optimization of turbine profile shape has been studied. The turbine in a turbopump in this study is a partial admission of impulse type, which has twelve nozzles and supersonic inflow. Due to the separated nozzles and supersonic expansion, the flow field becomes complicate and shows oblique shocks and flow separation. To increase the blade power, redesign ol the blade shape using CFD and optimization methods was attempted. The turbine cascade shape was represented by four design parameters. For optimization, a genetic algorithm based upon non-gradient search hue been selected as an optimizer. As a result, the final blade has about 4 percent more blade power than the initial shape.