Zobrazeno 1 - 10
of 423
pro vyhledávání: '"XiangYun Hu"'
Publikováno v:
Geo-spatial Information Science, Pp 1-23 (2024)
In the era of increasingly advanced Earth Observation (EO) technologies, extracting pertinent information (such as water-bodies) from the Earth’s surface has become a crucial task. Deep Learning, especially via pre-trained models, currently offers
Externí odkaz:
https://doaj.org/article/f2a5eb70bef048ebbdbde3586edcccee
Autor:
Zhipeng Cao, Liangcun Jiang, Peng Yue, Jianya Gong, Xiangyun Hu, Shuaiqi Liu, Haofeng Tan, Chang Liu, Boyi Shangguan, Dayu Yu
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 5, Pp 1489-1508 (2024)
Artificial Intelligence (AI) Machine Learning (ML) technologies, particularly Deep Learning (DL), have demonstrated significant potential in the interpretation of Remote Sensing (RS) imagery, covering tasks such as scene classification, object detect
Externí odkaz:
https://doaj.org/article/35b755753428416f8762e33772c09a68
Publikováno v:
Heliyon, Vol 10, Iss 17, Pp e36431- (2024)
Drivers are more likely to feel fatigue when driving on the desert highway due to its single line, monotonous road side landscape, and small traffic volume, etc., so the method of setting fatigue warning signs on desert highway should be studied for.
Externí odkaz:
https://doaj.org/article/78c2054b09684838af0c5ed897ae57ad
Publikováno v:
地质科技通报, Vol 43, Iss 3, Pp 341-350 (2024)
Objective In geothermal exploration, a clay cap is a typical hydrothermal geothermal system, and its depth and distribution range can provide crucial information for delineating the scope of geothermal resources and determining the location of geothe
Externí odkaz:
https://doaj.org/article/0804e306b4564c979994151b16613471
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8434-8449 (2024)
Intelligent interpretation of remote sensing images using deep learning is heavily reliant on large datasets, and models trained in one domain often struggle with crossdomain application. Pretraining the backbone network via masked image modeling can
Externí odkaz:
https://doaj.org/article/40924470fc254556ae7e946ff677a63b
Publikováno v:
Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 119-127 (2023)
In this study, a deep learning algorithm was applied to two-dimensional magnetotelluric (MT) data inversion. Compared with the traditional linear iterative inversion methods, the MT inversion method based on convolutional neural networks (CNN) does n
Externí odkaz:
https://doaj.org/article/a6c5ef077aed4a0d896065cce7e6f942
Publikováno v:
Earth, Planets and Space, Vol 75, Iss 1, Pp 1-12 (2023)
Abstract Vertical magnetic transfer functions (tippers) estimated at a global/continental net of geomagnetic observatories/sites can be used to image the electrical conductivity structure of the Earth’s crust and upper mantle (down to around 200 km
Externí odkaz:
https://doaj.org/article/7232e31b4f354b26a5f2582953e51158
Autor:
Qi Han, XiangYun Hu
Publikováno v:
Earth and Planetary Physics, Vol 7, Iss 4, Pp 499-512 (2023)
With the increase in the coverage area of magnetotelluric data, three-dimensional magnetotelluric modeling in spherical coordinates and its differences with respect to traditional Cartesian modeling have gradually attracted attention. To fully unders
Externí odkaz:
https://doaj.org/article/03d2567f6a7747498de2745e6aa39f95
Publikováno v:
Geo-spatial Information Science, Vol 26, Iss 3, Pp 289-301 (2023)
ABSTRACTHigh Spatial and Spectral Resolution (HSSR) remote-sensing images can provide rich spectral bands and detailed ground information, but there is a relative lack of research on this new type of remote-sensing data. Although there are already so
Externí odkaz:
https://doaj.org/article/06a5fa79af95471392cf252ac0f3cec3
Autor:
Huimin Xu, Qian Wen, Hangqi Hu, Sihao Yang, Lingyun Lu, Xiangyun Hu, Hao Li, Xianhao Huang, Ning Li
Publikováno v:
Microbial Biotechnology, Vol 17, Iss 2, Pp n/a-n/a (2024)
Abstract Severe acute pancreatitis (SAP) onset and development are closely associated with intestinal barrier injury. Evidence from clinical practice and research has shown that electroacupuncture (EA) at the Zusanli (ST36) acupoint can improve intes
Externí odkaz:
https://doaj.org/article/e945e1b06d364091b69d0900504116fb