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공공누리This item is licensed Korea Open Government License

dc.date.accessioned
2018-11-02T04:57:09Z
dc.date.available
2018-11-02T04:57:09Z
dc.date.issued
2017-12
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/11436
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/report/reportSearchResultDetail.do?cn=TRKO201800002490
dc.description
funder : 과학기술정보통신부
dc.description
funder : Ministry of Science and ICT
dc.description.abstract
□ 전지구 위성영상 빅데이터를 활용한 데이터 기반 태풍진로 예측 딥러닝 모델 개발
◦ 위성영상 기반 태풍 중심 탐지 시스템 개발 (GlobeNet)
◦ 시계열 위성영상 예측 시스템 개발 (PSIque)
◦ 위성영상 학습 오토인코더 개발
◦ 수치모델 기반 태풍진로예측 딥러닝 모델 개발 (DeepTC)
□ 대용량 위성영상 분석에 최적화된 분산 딥러닝 프레임워크 설계 및 테스트베드 구성
◦ 분산 Tensoflow 활용 딥러닝 프레임워크 개발
◦ 30노드 이상의 분산 딥러닝 프레임워크 운용

(출처 : 보고서 초록 3p)
dc.description.abstract
IV. Results of the study
◦ Development of tropical cyclone center detection system based on satellite image (GlobeNet)
- Construction of convolution neural network and linear regression application model
- Locations of multiple tropical cyclone centers (up to 6) and confidence probability simultaneous recognition
◦ Development of time-series satellite prediction system (PSIque)
- Construction of sequence to sequence prediction model for SkipConx based memory network
◦ Development of auto encoder learning satellite image
- Development of learning module for latent vector of cloud satellite image
- Improved performance after using as additional information of deep learning model
◦ Development of a numerical model-based tropical cyclone trajectory prediction deep-learning model (DeepTC)
- Establishment of WRF (Weather Research and Forecast) data utilization model using ensemble technique
◦ Development of distributed deep learning framework and development of operation technology
- Multi-node distributed deep learning framework using distributed Tensorflow

(출처 : SUMMARY 7p)
dc.publisher
한국과학기술정보연구원
dc.publisher
Korea Institute of Science and Technology Information
dc.title
End-to-End 딥러닝 기법을 적용한 데이터 기반 태풍 진로 예측 연구
dc.title.alternative
A Research for Typhoon Track Prediction using End-to-End Deep Learning Technique
dc.identifier.doi
10.23000/TRKO201800002490
dc.identifier.localId
TRKO201800002490
dc.identifier.url
http://www.ndsl.kr/ndsl/commons/util/ndslOriginalView.do?dbt=TRKO&cn=TRKO201800002490
dc.subject.keyword
태풍
dc.subject.keyword
진로예측
dc.subject.keyword
딥러닝
dc.subject.keyword
데이터 기반
dc.subject.keyword
종단간 모델
dc.subject.keyword
Tropical Cyclone
dc.subject.keyword
Trajectory Prediction
dc.subject.keyword
Data-driven
dc.subject.keyword
End-to-End Model
dc.type.local
1단계보고서
dc.identifier.koi
KISTI2.1015/RPT.TRKO201800002490
Appears in Collections:
7. KISTI 연구성과 > 연구보고서 > 2017
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