Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Guohui Jia"'
Publikováno v:
ChemistryOpen, Vol 13, Iss 8, Pp n/a-n/a (2024)
Abstract Dynamic control of the motion in a catenane remains a big challenge as it requires precise design and sophisticated well‐organized structures. This paper reports the design and synthesis of a donor‐acceptor [2]catenane through mechanical
Externí odkaz:
https://doaj.org/article/ec0031a043a041fa968d3d18ad78c669
Publikováno v:
Electronics Letters, Vol 59, Iss 19, Pp n/a-n/a (2023)
Abstract Deep convolutional neural networks have become a popular tool for the automatic detection of pavement cracks. Despite their widespread use, the models currently available tend to emphasize pixel‐level classification accuracy for cracks, of
Externí odkaz:
https://doaj.org/article/f7f61631652243e3b7ffa51d32570379
Publikováno v:
PLoS ONE, Vol 18, Iss 6, p e0286454 (2023)
The black garlic is produced from the raw garlic by Milliard reaction at high temperature (~60-90°C) and humidity (~70-90%). In this process, the pungent odor and gastrointestinal irritation effects of the raw garlic are reduced. At the same time, u
Externí odkaz:
https://doaj.org/article/5dcd01b2663d4bec88bb77f5dbc532cc
Publikováno v:
Remote Sensing, Vol 15, Iss 7, p 1750 (2023)
The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built t
Externí odkaz:
https://doaj.org/article/27d80fe308ae4fd0b64ea2ea227664a8
Publikováno v:
IEEE Access, Vol 7, Pp 171001-171012 (2019)
Pavement crack detection and characterization is a fundamental part of road intelligent maintenance systems. Due to the high non-uniformity of cracks, topological complexity, and similar noise from crack texture, the challenge arises in this domain w
Externí odkaz:
https://doaj.org/article/345e9a9d4889428b8e23e5098ccb550b
Publikováno v:
Journal of Advanced Transportation, Vol 2020 (2020)
Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a cha
Externí odkaz:
https://doaj.org/article/1551acead79848898f8dda2761fab894
Autor:
Guohui Jia, Jie Zhou
Publikováno v:
Sensors, Vol 21, Iss 16, p 5414 (2021)
The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service prioritie
Externí odkaz:
https://doaj.org/article/2c694c3903a14798baedc0a3bc280d16
Autor:
Tianqiang Pu, Xuemin Zhong, Guohui Jiang, Zhengjun Wu, Shixu He, Yanling Long, Qiuxia Liang, Xionglong Tu, Suqiu Yao, Jian Wang, Mingfang He
Publikováno v:
Brain and Behavior, Vol 14, Iss 11, Pp n/a-n/a (2024)
ABSTRACT Introduction This study investigated the association between the fetal‐type posterior cerebral artery (FTP) and hypertensive thalamic hemorrhage (HTH). Method Patients with hypertension who met the admission criteria between January 2020 a
Externí odkaz:
https://doaj.org/article/1c015bec2eb2423e9a3dabbc1c1a5199
Publikováno v:
Electronics Letters. 55:1235-1238
Recent convolutional neural networks have made significant advancements in the detection of road cracks. However, the lack of accurate crack training data reduces the generalisation ability of the deep model. In this Letter, a semi-automatic pavement
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e28578a0861313beaf3206aa2a48af3
https://doi.org/10.1049/el.2019.2692
https://doi.org/10.1049/el.2019.2692
Autor:
Meng Liu, Jianjun Li, Ke Liu, Chaoyang Wang, Pan Peng, Guohui Li, Yongjing Cheng, Guohui Jia, Wei Xie
Publikováno v:
Knowledge-Based Systems. 251:109208
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41674bb75ada3b03ba98498ddeeeb2b6
https://doi.org/10.1016/j.knosys.2022.109208
https://doi.org/10.1016/j.knosys.2022.109208