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Patent Agency Ranking1.发明公开유전 알고리즘을 활용하여 다중 지표 물리 과정 조합이 가능한 지면모델에서의 지표 물수지 시뮬레이션을 개선하는 방법 有权使用NOLH土地表面模型与多种物理选项和微观遗传算法的表面水模拟的改进方法
公开(公告)号：KR1020140066423A
公开(公告)日：2014-06-02
申请号：KR1020120133619
申请日：2012-11-23
Applicant: 이화여자대학교 산학협력단
CPC classification number: Y02A90/16 , G06F17/5009 , G06N3/126
Abstract: The present invention relates to a method for optimally improving a surface water simulation for physics options by using a micro genetic algorithm of a genetic algorithm in a Noah multi-physics options model. The present invention not only improves the accuracy of the model but also maximizes the accuracy of the model in a wide target area for water variables with large deviation according to the difference of a coating state and surface soil like evapotranspiration and runoff.
Abstract translation: 本发明涉及通过在诺亚多物理选项模型中使用遗传算法的微遗传算法来最佳地改善物理选项的表面水模拟的方法。 本发明不仅提高了模型的精度，而且根据涂层状态和表面土壤的差异，如蒸散量和径流量，在较大偏差的水分变量的广泛目标区域中最大化模型的精度。
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公开(公告)号：KR1020140066351A
公开(公告)日：2014-06-02
申请号：KR1020120133461
申请日：2012-11-23
Applicant: 이화여자대학교 산학협력단
Abstract: The present invention relates to a method for obtaining a best independent model and improving simulation accuracy which maximizes the simulation accuracy for a single or a plurality of target variables in an available physics parameterization technique by applying a model optimization algorithm to a multi-physics model. The present invention can use a minimized computing system resource capable of maximizing the simulation accuracy of the single or the plurality of target variables among the physical parameterization schemes.
Abstract translation: 本发明涉及一种用于获得最佳独立模型并提高模拟精度的方法，其通过将模型优化算法应用于多物理模型，使可用物理参数化技术中的单个或多个目标变量的模拟精度最大化。 本发明可以使用能够最大化物理参数化方案中的单个或多个目标变量的模拟精度的最小化计算系统资源。
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4.发明授权유전 알고리즘을 활용하여 다중 지표 물리 과정 조합이 가능한 지면모델에서의 지표 물수지 시뮬레이션을 개선하는 방법 有权使用具有多物理选项的诺亚地表模型进行地表水模拟的改进方法和微遗传算法
公开(公告)号：KR101460413B1
公开(公告)日：2014-11-12
申请号：KR1020120133619
申请日：2012-11-23
Applicant: 이화여자대학교 산학협력단
CPC classification number: Y02A90/16
Abstract: 본 발명은 Noah 다중 물리 지표모델에 유전 알고리즘 중 마이크로 유전 알고리즘을 이용하여 지표 물리 옵션에 대한 최적의 지표 물수지 시뮬레이션을 개선하는 방법에 관한 것으로, 모델의 정확도를 개선할 수 있을 뿐만 아니라, 증발산이나 유출량 같은 지표의 토양 및 피복상태의 차이에 따라 지역적 편차가 큰 물수지 변수들의에 대하여 광범위한 대상지역에서도 모델 정확도를 극대화 할 수 있다.
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公开(公告)号：KR101410770B1
公开(公告)日：2014-06-24
申请号：KR1020130035823
申请日：2013-04-02
Applicant: 이화여자대학교 산학협력단 , 한국해양과학기술원
IPC: G06F19/00
Abstract: According to the present invention, an annual vegetation predicting method includes: a step of separately analyzing a correlation between a plurality of meteorological factors and the main ingredients of a plant activity phase mode (PM) and a plant activity intensity mode (AM) which are disassembled from the past vegetation index through an empirical orthogonal function analyzing method; a step of separately selecting at least one predictive meteorological factor determined to have a statistically meaningful correlation among the meteorological factors based on the correlative analysis of each prediction of plant activity intensity and plant activity phase; and a step of separately predicting the plant activity intensity and the plant activity phase based on the past data relating to the plant activity intensity and the plant activity phase, and the present and past data for the selected prediction meteorological factor.
Abstract translation: 根据本发明，年度植被预测方法包括：分别分析多个气象因子与植物活动相模式（PM）和植物活动强度模式（AM）的主要成分之间的相关性的步骤，其为 通过经验正交函数分析方法从过去植被指数中拆除; 基于对植物活动强度和植物活动期的每个预测的相关分析，分别选择确定为具有气象因素之间具有统计意义的相关性的至少一个预测气象因子的步骤; 以及基于与植物活动强度和植物活动期相关的过去数据以及所选预测气象因子的当前和过去数据分别预测植物活动强度和植物活动期的步骤。
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