• Title/Summary/Keyword: Co-evolution strategy

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Treatability Study on Oil-Contaminated Soils for Bioremediation Application (유류오염토양의 생물적용기술 적용타당성 검토)

  • Lee, Yeon-Hui;Seol, Mi-Jin;O, Yeong-Suk
    • 한국생물공학회:학술대회논문집
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    • 2001.11a
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    • pp.578-581
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    • 2001
  • A treatability study was conducted using a hydrocarbon-contaminated soil for the oPtimization of bioremediation strategy best fit to a given set of contamination. The applicability of nutrients, biosurfactant, and oil-degrading microorganisms were examined by monitoring $CO_2$ evolution and oil degradation The addition of inorganic nutrients in the form of slow released fertilizer accelerated the initial rate of $CO_2$ evolution by a factor of 3. The application of oil-degrading microorganisms did not significantly increased $CO_2$ evolution or biodegradation efficiency. Application of a commercial biosurfactant was most effect in terms of the total $CO_2$ evolution and the oil degradation rate. The results indicate that $CO_2$ evolution measurement was found to be a simple and reliable countermeasure of crude oil hydrocarbon mineralization for the rapid determination of the best-fit bioremediation strategy.

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Multiobjective Optimal Design Technique for Induction Motor Using Improved (1+1)Evolution Strategy (개선된 (1+1)Evolution Strategy를 이용한 유도전동기의 다중목적 최적 설계)

  • Kim, M.K.;Lee, C.G.;Park, J.T.;Jung, H.K.
    • Proceedings of the KIEE Conference
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    • 1996.11a
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    • pp.6-8
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    • 1996
  • The multiobjective optimization is presented for the optimal design of induction motors. The aim of design is to find an optimized induction motor in terms of both the efficiency and the mass. The efficiency and the mass are linearly combined using the weighting factors. Optimization process is performed by using the improved (1+1) evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify the validity of the proposed method. the method is applied to a sample design.

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High-Efficiency Light-Weight Motor Design Technique for Electric Vehicle Using Evolution Strategy ((1+1) Evolution Strategy를 이용한 유도전동기의 최적 설계)

  • Kim, M.K.;Lee, C.G.;Park, J.T.;Lee, H.B.;Jung, H.K.;Hahn, S.Y.
    • Proceedings of the KIEE Conference
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    • 1995.11a
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    • pp.9-11
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    • 1995
  • In this paper, tile squirrel case induction motors required multi-objective function are designed. As the objective function of the optimization program, we select the linear combination of loss and mass of motors by using weighting factors. Optimization process is performed by using the evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify validity of the proposed method, a sample design is tried.

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Observation of Bargaining Game using Co-evolution between Particle Swarm Optimization and Differential Evolution (입자군집최적화와 차분진화알고리즘 간의 공진화를 활용한 교섭게임 관찰)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.14 no.11
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    • pp.549-557
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    • 2014
  • Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

The Optimization Of SS-Type Deflection Yoke By Using Genetic Algorithm (유전 알고리즘을 이용한 SS형 편향코일의 형상 최적화)

  • Joo, K.J.;Yoon, I.G.;Kang, B.H.;Joe, M.C.;Hahn, S.Y.;Lee, H.B.
    • Proceedings of the KIEE Conference
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    • 1993.07b
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    • pp.971-973
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    • 1993
  • Deflection Yoke(the following, DY) is the important electric device of CRT which deflects R, G, B beans influencing magnetic field produced by yoke coils. Recently, DY is designed to the saddle/saddle type of coils, being proposed for high-definite and high-efficient CRT. This paper presents the optimization of pin-sectioned saddle coil's shape for minimizing gap between desired and practical deflections of electron beams by using Genetic Algorithm. Evolution Startegy is utilized in this paper, since evolution strategy is a kind of genetic algorithms finding the optimized values by choicing the better generation with comparing the parents and their children. Here, the children are generated by only mutations from the normal random variables. Evolution strategy has shown better powerful converge rate than the other genetic algorithms becuase of using only the mutation-operator.

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Optimization of Bobbin winding type Deflection Yoke Wire Distribution By Using Evolution Startegy (Evolution Startegy를 이용한 Bobbin형 편향코일의 권선분포 최적화)

  • Joe, M.C.;Kang, B.H.;Koh, C.S.;Joo, K.J.
    • Proceedings of the KIEE Conference
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    • 1994.11a
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    • pp.130-132
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    • 1994
  • Recently, a Deflection Yoke(DY) is designed in the bobbin-seperator-coil-winding type for high-definite CRT and high-efficient DY of wide vision TV or High Definite TV. This paper presents an optimization or bobbin-seperator-coil-winding type yoke's coil distribution for minimizing gap between desired and practical deflections of electron beams using by Evolution Strategy.

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Optimization of the Deflection Yoke Coil for Color Display Tubes

  • Im, Chang-Hwan;Jung, Hyun-Kyo;Jung, Kwang-Sig;Cho, Yoon-Hyoung
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.11B no.3
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    • pp.81-85
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    • 2001
  • Processes for optimizing the coil shape of deflection yoke are proposed A very accurate and practical winding modeler is developed and volume integral equation method (VIEM) is used for field calculation. Two steps of optimizations are done by using (1+1) evolution strategy. Those are dimensional optimization and pin-position optimization Various techniques are applied for reducing computational time for the optimization.

A Study on Co-evolution on the Formation Process of Space and Network focused on Knowledge Intensive Industry (지식집약산업의 공간과 네트워크 형성과정에 대한 공진화적 고찰)

  • Choi, HaeOk
    • Journal of the Economic Geographical Society of Korea
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    • v.15 no.4
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    • pp.628-641
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    • 2012
  • This research investigates a dynamic mechanism underlying the co-evolution between network and space by applying hype-curve model, typical phenomenon which shows how new technologies and ideas initially adapted in the society. This study analysis the knowledge intensive industry of digital contents using social network analysis (SNA) in terms of structural, spatial, and temporal aspects, year of 2000, 2005, and 2010 focused on Seoul area. First of all, network and space establish 'inter-feedback' as a result of evolution and differentiation process. Second, it happen temporal 'delay' through the learning process stage of 'peak of inflated expectation' and 'trough of disillusionment.' As a result, Seoul develops with the technology commercialized-orient strategy affect government policy. This trend changes to technology-oriented development in Seoul area in the late of 2000 established 'self-organization' with geographical proximity organizations through learning process.

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A Theoretical Study on the Coevolution Strategy of University Innovation Ecosystems (대학 혁신생태계의 공진화 전략에 대한 이론적 고찰)

  • Park, Sang-Kyu
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.12
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    • pp.268-277
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    • 2020
  • This study emphasizes that the survival strategy of universities should be a co-evolution strategy based on ecological thinking. Therefore, the purpose of the research is to present a theoretical framework for dividing the university innovation ecosystem into four stages and building a co-evolution strategy for each step, as universities play a prominent role in regional innovation ecosystems. Thus, our research method focused on literature research, and the theoretical framework for the university innovation ecosystem used Moore's Enterprise Ecosystem Model (1996). The university's ecological innovation strategy is divided into four stages of development, and a step-by-step co-evolution strategy is presented. Findings are summarized as follows. The pioneering stage involves the creation of values of the university-led innovation ecosystem. The expansion stage focuses on the establishment of critical mass. The authority stage covers maintaining authority and bargaining power. The renewal stage features continuous performance improvement. In particular, this theoretical model of the university-regional innovation ecosystem is meaningful in that it provides a theoretical basis for enhancing the effectiveness of government financial support projects, and for individual universities, it provides a framework for strategies suitable for their ecosystem building process.