Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Daohua Zhan"'
Autor:
Daohua Zhan, Renbin Huang, Kunran Yi, Xiuding Yang, Zhuohao Shi, Ruinan Lin, Jian Lin, Han Wang
Publikováno v:
Micromachines, Vol 14, Iss 9, p 1737 (2023)
To address the challenges of complex backgrounds, small defect sizes, and diverse defect types in defect detection of wire bonding X-ray images, this paper proposes a convolutional-neural-network-based defect detection method called YOLO-CSS. This me
Externí odkaz:
https://doaj.org/article/58bafb49575d412282ee04307955f4e5
Autor:
Yinghong Zhou, Zhiliang Wu, Nian Cai, Daohua Zhan, Shaoqiu Xu, Meiyun Chen, Guang Zhou, Han Wang
Publikováno v:
Micromachines, Vol 14, Iss 8, p 1555 (2023)
To automatically measure the surface profile of a cylindrical workpiece, a high-precision multi-beam optical method is proposed in this paper. First, some successive images for the cylindrical workpiece’s surface are acquired by a multi-beam angle
Externí odkaz:
https://doaj.org/article/7ef2216456154e629267e0663779ec14
Autor:
Renbin Huang, Daohua Zhan, Xiuding Yang, Bei Zhou, Linjun Tang, Nian Cai, Han Wang, Baojun Qiu
Publikováno v:
Micromachines, Vol 14, Iss 7, p 1375 (2023)
In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after
Externí odkaz:
https://doaj.org/article/d9470ae273bf4b61a24f4d1ef73e23be
Autor:
Daohua Zhan, Jian Lin, Xiuding Yang, Renbin Huang, Kunran Yi, Maoling Liu, Hehui Zheng, Jingang Xiong, Nian Cai, Han Wang, Baojun Qiu
Publikováno v:
Micromachines, Vol 14, Iss 6, p 1119 (2023)
Integrated circuit (IC) X-ray wire bonding image inspections are crucial for ensuring the quality of packaged products. However, detecting defects in IC chips can be challenging due to the slow defect detection speed and the high energy consumption o
Externí odkaz:
https://doaj.org/article/d4e3da056f274ce6bd9ef9f699a893d4
Autor:
Qiu, Renbin Huang, Daohua Zhan, Xiuding Yang, Bei Zhou, Linjun Tang, Nian Cai, Han Wang, Baojun
Publikováno v:
Micromachines; Volume 14; Issue 7; Pages: 1375
In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::f92ff5b46f2f8f42dc1b43551a701be6
Autor:
Jiarong Zhang, Han Wang, Xiangyou Zhu, Honghui Yao, Shaomu Zhuo, Shuaijie Ma, Daohua Zhan, Nian Cai
Publikováno v:
Materials; Volume 15; Issue 14; Pages: 4848
For small aspherical molds, it is difficult for the existing polishing method to take into account the correction of the surface error and the control of the uniformity of the surface roughness (SR) distribution, because the polishing tool is always
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4513020e3060db07103e78204dd491a8
Publikováno v:
IEEE Sensors Journal. 21:12048-12056
Flexible pressure sensors have been extensively employed in a range of fields, such as electronic skin (E–skin), humanoid robots, and personal health care. Laser–induced graphene (LIG) is an ideal active material to produce flexible sensors due t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::219b5ab05ea996f3d3da48801022ad79
https://doi.org/10.1109/jsen.2021.3054985
https://doi.org/10.1109/jsen.2021.3054985
Autor:
Han Wang, Xiuding Yang, Bei Zhou, Zhuohao Shi, Daohua Zhan, Renbin Huang, Jian Lin, Zhiheng Wu, Danfeng Long
Publikováno v:
Materials; Volume 16; Issue 7; Pages: 2811
In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::655c4bdff7b4886bd2d0d131471546e3
https://doi.org/10.3390/ma16072811
https://doi.org/10.3390/ma16072811
Publikováno v:
Frontiers in Energy Research, Vol 11 (2023)
Introduction: This paper proposes a power system fault prediction method that utilizes a GA-CNN-BiGRU model. The model combines a genetic algorithm (GA), a convolutional neural network (CNN), and a bi-directional gated recurrent unit network Bidirect
Externí odkaz:
https://doaj.org/article/e60e3a8e9310400993855b62ba684c55
Publikováno v:
Frontiers in Energy Research, Vol 11 (2023)
A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor
Externí odkaz:
https://doaj.org/article/b6d49c0ce40d46d2b9fab29ce4ba7726