• Title/Summary/Keyword: Environment Condition

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Length-weight Relationship and Condition Factor of Zacco platypus in the Lake Hoengseong (횡성호에 분포하는 피라미 (Pale chub: Zacco platypus) 개체군의 Length-weight Relationship 및 Condition Factor)

  • Jang, Young-Su;Choi, Jae-Seok;Lee, Kwang-Yeol;Seo, Jin-Won;Kim, Bom-Chul
    • Korean Journal of Ecology and Environment
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    • v.40 no.3
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    • pp.412-418
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    • 2007
  • The dynamics of Zacco platypus population of upstream and downstream in the lake Hoengseong, Korea were investigated from April to October 2005. Length-weight relationship, condition factor (K) and relative condition factor $(K_n)$ of Z. platypus were compared by the study stations. The equations based on length-weight relationship in the lake Hoengseong were $Log(T_w)=-2.2s+3.18{\cdot}Log(T_L)\;(r^2=0.99)$. The result in comparison of variations of Z. platypus populations, in lake was more remain to better than in upstream and downstream them. Also the b value, assessed by Length-weight relationship in lake was 3.36, in upstream and downstream were 3.09, 3.15, respectively indicating the fish in lake better than stream. The slopes of population condition controlled by K factor also showed positive relationship. It was higher in lake environment than in stream sample, reflecting that population of Z. platypus distributed in lake Hoengseong was favorable and stable condition. The lake environment seems to be providing more favorable condition for Z. platypus population.

A FILTERING CONDITION AND STOCHASTIC ADAPTIVE CONTROL USING NEURAL NETWORK FOR MINIMUM-PHASE STOCHASTIC NONLINEAR SYSTEM (최소위상 확률 비선형 시스템을 위한 필터링 조건과 신경회로망을 사용한 적응제어)

  • Seok, Jin-Wuk
    • Proceedings of the KIEE Conference
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    • 2001.11c
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    • pp.18-21
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    • 2001
  • In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

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Growth Simulation of Ilpumbyeo under Korean Environment Using ORYZA2000: I. Estimation of Genetic Coefficients

  • Lee Chung-Kuen;Shin Jae-Hoon;Shin Jin-Chul;Kim Duk-Su;Choi Kyung-Jin
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2004.04a
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    • pp.100-101
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    • 2004
  • [ $\bigcirc$ ] In the growth simulation using genetic coefficients calculated with fooled data under various condition, WAGT was not higher and LAI, WLVG, WSO were higher, but WST was similar before grain-filling stage after the became lower because of higher translocation of carbohydrates than in the growth simulation using genetic coefficients calculated with data under high nitrogen applicated condition. $\bigcirc$ Genetic coefficients should be calculated with data showing potential in ORYZA2000, but under 180 kg and 240 kg N condition in 2003, plants were infected by panicle blast and also yield was not higher than under 120 kg N condition showing not potential condition and therefore not appropriate for genetic coefficients estimation compared with pooled data from various condition.

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