Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Fengxiang Yang"'
Autor:
Wenlong Guan, Xialin Zhang, Changfeng Xi, Xiaochun Wang, Fengxiang Yang, Xiaorong Shi, Qiu Li
Publikováno v:
Petroleum Research, Vol 3, Iss 2, Pp 165-179 (2018)
As the vertical-well fire flooding technology is industrially applied in the steam-injection old heavy oil areas of Xinjiang and Liaohe oilfields, its enhanced oil recovery potential is gradually clear. According to laboratory experiment, field test
Externí odkaz:
https://doaj.org/article/dfce4d142b5847ff83e067d25f8764ef
Publikováno v:
Computer Vision – ACCV 2022 ISBN: 9783031263507
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc256c520fac86af146f8f50ba973968
https://doi.org/10.1007/978-3-031-26351-4_29
https://doi.org/10.1007/978-3-031-26351-4_29
Autor:
Fengxiang Yang, Juanjuan Weng, Zhun Zhong, Hong Liu, Zheng Wang, Zhiming Luo, Donglin Cao, Shaozi Li, Shin'ichi Satoh, Nicu Sebe
Recent studies show that deep person re-identification (re-ID) models are vulnerable to adversarial examples, so it is critical to improving the robustness of re-ID models against attacks. To achieve this goal, we explore the strengths and weaknesses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3366419b156b5cb6e082a64d2680b25
https://hdl.handle.net/11572/377289
https://hdl.handle.net/11572/377289
Autor:
Changfeng Xi, Fengxiang Yang, Tayfun Babadagli, Zhang Xialin, Guan Wenlong, Hetaer Mu, Hongyang Zhan, Huazhou Li, Xiao Rong Shi, Zhao Fang, Hongzhuang Wang
Publikováno v:
Journal of the Japan Petroleum Institute. 64:76-83
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::476484741cea305a0ecc8ac7292e965d
https://doi.org/10.1627/jpi.64.76
https://doi.org/10.1627/jpi.64.76
Autor:
Jin Huo, Ping Wang, Quan Shi, Jianxun Wu, Shengfei Zhang, Fengxiang Yang, Yahe Zhang, Xinge Sun
Publikováno v:
Energy & Fuels. 35:473-478
Heavy oils are commonly found in China; however, the Karamay heavy oil is unique among all. It has very low asphaltene content and is an excellent feedstock for producing high-quality lubricant bas...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cce1862b87de2457d0f3fe515b443869
https://doi.org/10.1021/acs.energyfuels.0c03639
https://doi.org/10.1021/acs.energyfuels.0c03639
Autor:
Fengxiang Yang, Zhiming Luo, Hao Cheng, Xing Sun, Rongrong Ji, Zhun Zhong, Shaozi Li, Ke Li, Xiaowei Guo, Feiyue Huang
Publikováno v:
AAAI
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08005145024fed1eb0cd5bea2d53bf4d
https://doi.org/10.1609/aaai.v34i07.6950
https://doi.org/10.1609/aaai.v34i07.6950
Publikováno v:
CVPR
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f00327efc78f7290e41e068ee7e5e2f
Publikováno v:
CVPR
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to train a new model for a new domain by accessing data. Due to public priv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6d2128205093776586f4af8edc28e91
https://doi.org/10.1109/cvpr46437.2021.00621
https://doi.org/10.1109/cvpr46437.2021.00621
Autor:
Shi Xiaorong, Hongzhuang Wang, Tayfun Babadagli, Huazhou Li, Youwei Jiang, Hetaer Mu, Fengxiang Yang, Changfeng Xi, Zhao Fang, Guan Wenlong, Zhang Xialin
Publikováno v:
Day 2 Tue, October 27, 2020.
We evaluated the performance of a field-wide post-steam in-situ combustion (ISC) project conducted in a complex heavy oil reservoir in China using laboratory one-dimensional combustion experiments, reservoir simulation outputs, and data collected fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27b23a089c3e7c2e3ee434d4bbc223c9
https://doi.org/10.2118/201815-ms
https://doi.org/10.2118/201815-ms