Abstract
기상레이더를 통해 취득된 데이터에는 지형에코, 파랑에코, 이상에코, 그리고 청천에코등이 존재한다. 각 에코는 여러 종류의 비강수에코이고, 이 비강수에코를 제거하기 위해 각 에코들의 특성을 분석하였다. 기상레이더 데이터는 매우 방대한 양이기 때문에 전처리 절차를 통해 분석된다. 본 논문에서는 클러스터링 기반 방사형 기저함수 신경회로망(RBFNNs : Radial Basis Function Neural Networks)과 에코 판단 모듈을 이용하여 기상레이더 데이터에서 강수에코와 비강수에코들을 구별하기 위한 에코 패턴분류기를 설계하였다. HCM(Hard C-Mean) 클러스터링 기반 RBFNNs 와 FCM(Fuzzy C-Mean) 클러스터링 기반 RBFNNs를 이용하여 출력성능은 비교 및 분석된다. Data through meteorological radar includes ground echo, sea-clutter echo, anomalous propagation echo, clear echo and so on. Each echo is a kind of non-precipitation echoes and the characteristic of individual echoes is analyzed in order to identify with non-precipitation. Meteorological radar data is analyzed through pre-processing procedure because the data is given as big data. In this study, echo pattern classifier is designed to distinguish non-precipitation echoes from precipitation echo in meteorological radar data using RBFNNs and echo judgement module. Output performance is compared and analyzed by using both HCM clustering-based RBFNNs and FCM clustering-based RBFNNs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Korean Institute of Intelligent Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
