Zobrazeno 1 - 10
of 1 682
pro vyhledávání: '"CHEN Junhong"'
Autor:
Bi Ruomeng, Ou Meihao, Zhou Siru, Geng Shichen, Zheng Yixian, Chen Junhong, Mo Ruijie, Li Yuan, Xiao Gezhi, Chen Xingyu, Zhai Shiyi, Zhang Aihui, Fang Baishan
Publikováno v:
Synthetic and Systems Biotechnology, Vol 8, Iss 2, Pp 302-313 (2023)
The past 50 years have witnessed a massive expansion in the demand and application of pesticides. However, pesticides are difficult to be completely degraded without intervention hence the pesticide residue could pose a persistent threat to non-targe
Externí odkaz:
https://doaj.org/article/a89eeed8ed3841238f9f275d913b1ad7
Autor:
CHEN Junhong, WU Yi, WANG Yingqiao, LI Weibin, CHEN Yuhong, WANG Yali, ZHANG Xiaopeng, ZHENG Xue, ZHANG Chunguang, XUAN Weimin, YAO Lieying, TAN Hao, LUO Wenwu, ZHOU Peihai, SONG Xianming, LIU Shaoxuan, SUN Zequn, CONG Zijian, YANG Enwu, GE Xingxin, GAO Xiang
Publikováno v:
He jishu, Vol 47, Iss 5, Pp 050013-050013 (2024)
BackgroundENN Science and Technology Development Co., Ltd. (ENN Fusion Technology R&D Center) is upgrading its compact fusion research facility EXL-50 to EXL-50U. Both devices are the conventional conductor tokamak, on which the magnet power supply s
Externí odkaz:
https://doaj.org/article/8eeb66772555436e90de4dd624ddcbaf
Publikováno v:
Measurement Science Review, Vol 22, Iss 6, Pp 283-292 (2022)
The intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its
Externí odkaz:
https://doaj.org/article/0d7b5c5b6b794d6a8b024396f1057d48
Publikováno v:
Di-san junyi daxue xuebao, Vol 44, Iss 5, Pp 495-498 (2022)
Objective To investigate the clinical features, diagnosis and treatment of congenital inner ear deformity with spontaneous cerebrospinal fluid leakage in children. Methods Clinical data of 9 children suffering from inner ear deformity with spontaneou
Externí odkaz:
https://doaj.org/article/302e55e114c24e84bea3e3efea8bdedc
Due to the visual properties of reflection and refraction, RGB-D cameras cannot accurately capture the depth of transparent objects, leading to incomplete depth maps. To fill in the missing points, recent studies tend to explore new visual features a
Externí odkaz:
http://arxiv.org/abs/2408.00337
Autor:
Ding, Rui, Liu, Jianguo, Hua, Kang, Wang, Xuebin, Zhang, Xiaoben, Shao, Minhua, Chen, Yuxin, Chen, Junhong
Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study introduces a novel, multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex
Externí odkaz:
http://arxiv.org/abs/2407.04877
Accurate prediction of drug molecule solubility is crucial for therapeutic effectiveness and safety. Traditional methods often miss complex molecular structures, leading to inaccuracies. We introduce the YZS-Model, a deep learning framework integrati
Externí odkaz:
http://arxiv.org/abs/2406.19136
Autor:
Jang, Hyun-June, Joung, Hyou-Arm, Goncharov, Artem, Kanegusuku, Anastasia Gant, Chan, Clarence W., Yeo, Kiang-Teck Jerry, Zhuang, Wen, Ozcan, Aydogan, Chen, Junhong
This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity cha
Externí odkaz:
http://arxiv.org/abs/2402.17209
Autor:
Lo, Frank P. -W., Qiu, Jianing, Wang, Zeyu, Chen, Junhong, Xiao, Bo, Yuan, Wu, Giannarou, Stamatia, Frost, Gary, Lo, Benny
Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potentially inaccurate, and time-inte
Externí odkaz:
http://arxiv.org/abs/2312.08592
Autor:
Wang, Meng, Lin, Tian, Wang, Lianyu, Lin, Aidi, Zou, Ke, Xu, Xinxing, Zhou, Yi, Peng, Yuanyuan, Meng, Qingquan, Qian, Yiming, Deng, Guoyao, Wu, Zhiqun, Chen, Junhong, Lin, Jianhong, Zhang, Mingzhi, Zhu, Weifang, Zhang, Changqing, Zhang, Daoqiang, Goh, Rick Siow Mong, Liu, Yong, Pang, Chi Pui, Chen, Xinjian, Chen, Haoyu, Fu, Huazhu
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We established an uncertainty-inspired ope
Externí odkaz:
http://arxiv.org/abs/2304.03981