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기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 강수/비강수 패턴분류 시스템 설계
고준현(Jun-Hyun Ko),김현기(Hyun-Ki Kim),오성권(Sung-Kwun Oh) 대한전기학회 2015 전기학회논문지 Vol.64 No.7
In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.
FNN 기반 신경회로망을 이용한 기상 레이더 에코 분류기 설계
고준현(Jun-Hyun Ko),송찬석(Chan-Seok Song),오성권(Sung-Kwun Oh) 한국지능시스템학회 2014 한국지능시스템학회논문지 Vol.24 No.5
기상레이더에는 강수에코와 비강수 에코가 섞여 존재한다. 이런 모호한 지점의 판단이 난해함으로 정확한 일기 예보를 하기는 매우 어려운 일이다. 본 논문에서는 기상청 레이더의 UF 데이터로부터 데이터를 추출하였다. 설계하는 두 분류기의 입출력 데이터는 강수 에코와 비 강수 에코의 특성분석을 통해 구성된다. 더 좋은 성능을 나타나는 입력변수를 사용 하였으며, 에코분류기는 퍼지 뉴럴 네트워크를 기반으로 설계한다. 에코 판단 모듈 1과 판단모듈 2를 고려하여 에코분류기의 성능 비교연구를 수행 한다. There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.