• Journal of the Korean Geotechnical Society
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• v.28 no.8
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• pp.55-63
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• 2012

The evaluation of shear modulus (or shear wave velocity) profile of the site is very important in various fields of geotechnical engineering and various surface wave methods have applied to determine the shear wave velocity profiles and showed good performance. Surface wave methods evaluate the dispersion curve in the field and determine the shear wave velocity profile through the inversion process. In this paper, the automated inversion process using the genetic algorithm is developed for HWAW method which is one of surface wave methods recently developed. The proposed method uses the error function based on the wavelength domain dispersion curve and can determine the reliable shear wave velocity profile not only in shallow depth but also in deep depth. To estimate the validity of the proposed method, numerical simulations and field test were performed and the proposed method was applied to determine the shear wave velocity profiles. Through the numerical simulations and field applications, the promising potential of the proposed method was verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1279-1288
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• 2007

A vehicle routing problem with time constraint is one of the must important problem in distribution and logistics. In practice, the service for a customer must start and finish within a given delivery time. This study is concerned about the development of a model to optimize vehicle routing problem under the multi-logistics center problem. And we used a two-step approach with an improved genetic algorithm. In step one, a sector clustering model is developed by transfer the multi-logistics center problem to a single logistics center problem which is more easy to be solved. In step two, we developed a GA-TSP model with an improved genetic algorithm which can search a optimize vehicle routing with given time constraints. As a result, we developed a Network VRP computer programs according to the proposed solution VRP used ActiveX and distributed object technology.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.1-7
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• 2017

We analyze Inter-Aircraft Distances between Two Closest Flying Passenger Aircrafts on a Global Scale from Real Aviation Databases. Then, We Reveal that the Distances Follow a Gamma-Pareto Distribution. Our Finding is Useful for Designing Wireless Transceivers Since it Gives the Probability Distribution Regarding the Link Distances which the Wireless Transceivers should Cover for Providing Internet Services.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.177-178
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• 2007

플라즈마 공정은 집적회로 제작을 위한 미세 박막의 증착과 패턴닝에 핵심적으로 이용되고 있다. 본 연구에서는 플라즈마공정감시와 제어에 응용될 수 있는 모델을 제안한다. 본 모델은 광방사분광기 (Optical emission spectroscopy-OES)정보와 역전파 신경망을 이용해서 개발하였다. 제안된 기법은 Oxide 식각공정에서 수집한 데이터에 적용하였으며, 체계적인 모델링을 위해 공정데이터는 통계적 실험계획법을 적용하여 수집되었다. Raw OES 정보대신, Actinometric OES 정보를 이용하였으며, 신경망의 예측성능은 유전자 알고리즘을 이용해서 증진시켰다. OES의 차수를 줄이기 위해 주인자 분석 (Principal Component Analysis-PCA)을 세 종류의 분산(100, 99, 98%)에 대해서 적용하였다. 최적화한 모델의 예측에러는 323 $\AA/min$이었다. 이전에 PCA를 적용하고 은닉층 뉴런의 함수로 최적화한 모델의 예측에러는 570 $\AA/min$이었으며, 개발된 모델은 이에 비해 43% 증진된 예측 성능을 보이고 있다.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.403-404
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• 2007

소자 수율을 향상시키기 위해서는 웨이퍼 전체에 걸쳐 플라즈마 공정특성이 균일하게 분포되어야 한다. 본 연구에서는 Actinomeric 광 반사분광기 (Otical Emission Spectroscopy) 정보를 이용하여 식각률 비균일도에 대한 모델을 개발하였다. 제안된 기법은 Oxide 식각공정에서 수집한 데이터에 적용하였으며, 체계적인 모델링을 위해 공정데이터는 통계적 실험계획 법을 적용하여 수집되었다. 신경망의 예측성능은 유전자 알고리즘을 이용해서 증진시켰다. OES의 차수를 줄이기 위해 주인자 분석을 세 종류의 분산(100, 99, 98%)에 대해서 적용하였다. 개발된 모델은 발표된 이전의 모델에 비해 17% 증진된 예측성능을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.124-126
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• 2003

그리드 컴퓨팅은 광범위 분산 컴퓨팅 시스템(wide area distributed computing system)으로, 고성능의 유휴 컴퓨팅 자원을 서로 공유하여 효율적으로 작업을 수행하는 것을 목적으로 한다. 그리드 컴퓨팅에서 사용자가 요구하는 자원의 검색, 선택, 할당하는 문제는 시스템 성능에 큰 영향을 미친다. 그리드 컴퓨팅을 지원하는 대표적인 미들웨어인 글로버스(Globus Toolkit)에서는 위와 같은 과정들이 사용자에 의해 수동적으로 이루어지며, 검색된 후보 자원의 최적 선택 방법은 제공하지 않고 있다. 본 논문에서는 글로버스에서 사용자의 요구에 의해 검색된 후보 자원들 중 최적화된 자원 선택과 할당 요청을 담당하는 최적 자원 선택 브로커를 설계하였다. 이 브로커는 유전자 알고리즘을 이용하여 최적 자원을 선택하므로 사용자의 임의적 자원 선택으로 인한 시스템의 성능 저하를 막아준다. 자원 검색, 선택, 할당 요청이 하나의 브로커에서 이루어짐으로써 작업 수행 시 발생하는 사용자의 불필요한 관여를 막아 작업 수행에 대한 편의성을 제공한다.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.707-716
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• 2009

Wireless Sensor Networks are consisted of sensor nodes that communicated with each other to transmit information. Because sensor nodes have physically many limits, wireless sensor networks are hard to adopt for traditional networks. Transmissions are consumed most energy of sensor nodes. That's why energy-efficient transmission techniques and QoS support techniques for different kind of data are most important in wireless sensor networks. The thesis proposes the agent based framework for energy distribution and QoS in wireless sensor networks. Agents have its own behavior policy by means of a gene, which is optimized by genetic operations. Agents behavior to distribute energy consumption over sensor nodes. Simulation results show that the enhanced framework extends the lifetime of sensor nodes. Successful transmission ratios of emergency data and non emergency data are increased by 27% and 14%, respectively. Also, the results demonstrate that Qos of networks are improved.

• Journal of Life Science
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• v.25 no.9
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• pp.1007-1013
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• 2015

A COG (Cluster of Orthologous Groups of proteins) algorithm was applied to detect conserved genes in 711 prokaryotes. Only COG0080 (ribosomal protein L11) was common among all the 711 prokaryotes analyzed and 58 COGs were common in more than 700 prokaryotes. Nine COGs among 58, including COG0197 (endonuclease III) and COG0088 (ribosomal protein L4), were conserved in a form of one gene per one organism. COG0008 represented 1356 genes in 709 of the prokaryotes and this was the highest number of genes among 58 COGs. Twenty-two COGs were conserved in more than 708 prokaryotes. Of these, two were transcription related, four were tRNA synthetases, eight were large ribosomal subunits, seven were small ribosomal subunits, and one was translation elongation factor. Among 58 conserved COGs in more than 700 prokaryotes, 50 (86.2%) were translation related, and four (6.9%) were transcription related, pointing to the importance of protein-synthesis in prokaryotes. Among these 58 COGs, the most conserved COG was COG0060 (isoleucyl tRNA synthetase), and the least conserved was COG0143 (methionyl tRNA synthetase). Archaea and eubacteria were discriminated in the genomic analysis by the average distance and variation in distance of common COGs. The identification of these conserved genes could be useful in basic and applied research, such as antibiotic development and cancer therapeutics.

• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.114-130
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• 2006

This paper deals with complexity theory to describe amphibious warfare situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. EINSTein model is an agent-based artificial "laboratory" for exploring self-organized emergent behavior in land combat. Many studies have shown that existing Lanchester equations used in most war simulation models does not describe changes of combat. Future warfare will be information warfare with various weapon system and complex combat units. We have compared and tested combat results with Lanchester models and EINSTein model. Furthermore, the EINSTein model has been applied and analyzed to amphibious warfare model such as amphibious assault and amphibious sudden attack. The results show that the EINSTein model has a possibility to apply and analyze amphibious warfare more properly than Lanchester models.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.385-391
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• 2013

Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.