Zobrazeno 1 - 10
of 617
pro vyhledávání: '"wu xinming"'
Publikováno v:
Cailiao gongcheng, Vol 50, Iss 10, Pp 148-156 (2022)
PEO-based solid polymer electrolytes are considered as a promising solid electrolyte in the field of solid-state lithium batteries.PEO/LiClO4 solid polymer electrolyte(SPE) was prepared through electrostatic spinning technology, in order to meet the
Externí odkaz:
https://doaj.org/article/e61062cf93b84a45b3ae865c4df67424
Seismic geobody interpretation is crucial for structural geology studies and various engineering applications. Existing deep learning methods show promise but lack support for multi-modal inputs and struggle to generalize to different geobody types o
Externí odkaz:
http://arxiv.org/abs/2409.04962
We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, large neural networks trained on massive datasets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenge
Externí odkaz:
http://arxiv.org/abs/2408.12396
Distributed Acoustic Sensing (DAS) is promising for traffic monitoring, but its extensive data and sensitivity to vibrations, causing noise, pose computational challenges. To address this, we propose a two-step deep-learning workflow with high effici
Externí odkaz:
http://arxiv.org/abs/2403.02791
While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model pre-train
Externí odkaz:
http://arxiv.org/abs/2309.02791
Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To
Externí odkaz:
http://arxiv.org/abs/2309.02320
Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep le
Externí odkaz:
http://arxiv.org/abs/2306.13918
Autor:
Wu, Xinming, Dai, Ji
Reading emotions precisely from segments of neural activity is crucial for the development of emotional brain-computer interfaces. Among all neural decoding algorithms, deep learning (DL) holds the potential to become the most promising one, yet prog
Externí odkaz:
http://arxiv.org/abs/2303.04391
Autor:
Wu, Xinming1 (AUTHOR) wxm155156@163.com, Xu, Lu1 (AUTHOR) lujiusym@163.com, Zhang, Haoyuan1 (AUTHOR) swuzhanghy@163.com, Zhu, Yong2 (AUTHOR) w0w777@163.com, Zhang, Qiang2 (AUTHOR) tibetzq@126.com, Zhang, Chengfu2 (AUTHOR) tibetzcf@126.com, E, Guangxin1 (AUTHOR) eguangxin@126.com
Publikováno v:
Animals (2076-2615). Sep2024, Vol. 14 Issue 17, p2458. 11p.
In the audio magnetotellurics (AMT) sounding data processing, the absence of sferic signals in some time ranges typically results in a lack of energy in the AMT dead band, which may cause unreliable resistivity estimate. We propose a deep convolution
Externí odkaz:
http://arxiv.org/abs/2209.13647