최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.36 no.5 pt.3, 2020년, pp.1037 - 1051
이주현 (울산과학기술원 도시환경공학과) , 유철희 (울산과학기술원 도시환경공학과) , 임정호 (울산과학기술원 도시환경공학과) , 신예지 (울산과학기술원 도시환경공학과) , 조동진 (울산과학기술원 도시환경공학과)
The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
Chaudhuri, S., D. Dutta, S. Goswami, and A. Middey, 2013. Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification, Natural Hazards, 65(1): 97-113.
Combinido, J. S., J. R. Mendoza, and J. Aborot, 2018. A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images, Proc. of 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, Beijing, China, Aug. 20-24, pp.1474-1480.
Dvorak, V. F., 1975. Tropical cyclone intensity analysis and forecasting from satellite imagery, Monthly Weather Review, 103(5): 420-430.
Feng, B., 2005. A neural network regression model for tropical cyclone forecast, Proc. of 2005 International Conference on Machine Learning and Cybernetics, IEEE, Guangzhou, China, Aug. 18-21, pp.4122-4128.
Grossman, M., and M. Zaiki, 2009. Reconstructing typhoons in Japan in the 1880s from documentary records, Weather, 64(12): 315-322.
Guo, Y., J. Cai, B. Jiang, and J. Zheng, 2018. Cnnbased real-time dense face reconstruction with inverse-rendered photo-realistic face images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6): 1294-1307.
Ham, Y.-G., J.-H. Kim, and J.-J. Luo, 2019. Deep learning for multi-year ENSO forecasts, Nature, 573(7775): 568-572.
Han, H., S. Lee, J. Im, M. Kim, M.-I. Lee, M. H. Ahn, and S.-R. Chung, 2015. Detection of convective initiation using Meteorological Imager onboard Communication, Ocean, and Meteorological Satellite based on machine learning approaches, Remote Sensing, 7(7): 9184-9204.
Hoegh-Guldberg, O., D. Jacob, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K. Ebi, F. Engelbrecht, and J. Guiot, 2018. Impacts of 1.5 C global warming on natural and human systems, Global warming of 1.5 $^{\circ}C$ . An IPCC Special Report, http://hdl.handle.net/10138/311749, Accessed on Sep. 25, 2020.
Huang, X., Z. Guan, L. He, Y. Huang, and H. Zhao, 2016. A PNN prediction scheme for local tropical cyclone intensity over the South China Sea, Natural Hazards, 81(2): 1249-1267.
Kayalibay, B., G. Jensen, and P. van der Smagt, 2017. CNN-based segmentation of medical imaging data, arXiv preprint arXiv:1701.03056, Accessed on Sep. 25, 2020.
Kim, Y., G.-H. Kwak, K.-D. Lee, S.-I. Na, C.-W. Park, and N.-W. Park, 2018. Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size, Korean Journal of Remote Sensing, 34(5): 811-827 (in Korean with English abstract).
Lee, J., J. Im, D.-H. Cha, H. Park, and S. Sim, 2020. Tropical cyclone intensity estimation using multidimensional convolutional neural networks from geostationary satellite data, Remote Sensing, 12(1): 108.
Liu, X., Y. Lu, G. Zhu, Y. Lei, L. Zheng, H. Qin, C. Tang, G. Ellison, R. McCormack, and Q. Ji, 2013. The diagnostic accuracy of pleural effusion and plasma samples versus tumour tissue for detection of EGFR mutation in patients with advanced non-small cell lung cancer: comparison of methodologies, Journal of Clinical Pathology, 66(12): 1065-1069.
Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012. The impact of climate change on global tropical cyclone damage, Nature Climate Change, 2(3): 205-209.
Menzel, W. P., and J. F. Purdom, 1994. Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites, Bulletin of the American Meteorological Society, 75(5): 757-782.
Nwe, T. L., T. H. Dat, and B. Ma, 2017. Convolutional neural network with multi-task learning scheme for acoustic scene classification, Proc. of 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, Kuala Lumpur, Malaysia, Dec.12-15, pp.1347-1350.
Olander, T. L., and C. S. Velden, 2007. The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery, Weather and Forecasting, 22(2): 287-298.
Pradhan, R., R. S. Aygun, M. Maskey, R. Ramachandran, and D. J. Cecil, 2017. Tropical cyclone intensity estimation using a deep convolutional neural network, IEEE Transactions on Image Processing, 27(2): 692-702.
Qiu, Z., T. Yao, and T. Mei, 2017. Learning spatiotemporal representation with pseudo-3d residual networks, Proc. of the IEEE International Conference on Computer Vision, Venice, Italy, Oct. 22-29, pp. 5533-5541.
Ritchie, E. A., K. M. Wood, O. G. Rodreiguez-Herrera, M. F. Pineros, and J. S. Tyo, 2014. Satellitederived tropical cyclone intensity in the North Pacific Ocean using the deviation-angle variance technique, Weather and Forecasting, 29(3): 505-516.
Ruder, S., 2017. An overview of multi-task learning in deep neural networks, arXiv preprint arXiv:1706.05098, Accessed on Sep. 25, 2020.
Schmetz, J., S. Tjemkes, M. Gube, and L. Van de Berg, 1997. Monitoring deep convection and convective overshooting with METEOSAT, Advances in Space Research, 19(3): 433-441.
Sim, S., J. Im, S. Park, H. Park, M. H. Ahn, and P.-w. Chan, 2018. Icing detection over East Asia from geostationary satellite data using machine learning approaches, Remote Sensing, 10(4): 631.
Song, A., and Y. Kim, 2017. Deep learning-based hyperspectral image classification with application to environmental geographic information systems, Korean Journal of Remote Sensing, 33(6-2): 1061-1073 (in Korean with English abstract).
Velden, C. S., C. M. Hayden, S. J. W. Nieman, W. Paul Menzel, S. Wanzong, and J. S. Goerss, 1997. Upper-tropospheric winds derived from geostationary satellite water vapor observations, Bulletin of the American Meteorological Society, 78(2): 173-196.
Yoo, C., D. Han, J. Im, and B. Bechtel, 2019. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS Journal of Photogrammetry and Remote Sensing, 157: 155-170.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.