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兰州理工大学 [4]

西安交通大学 [3]

沈阳自动化研究所 [2]

清华大学 [1]

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	Recognition of weld defects from X-ray images based on improved convolutional neural network 期刊论文 Multimedia Tools and Applications, 2022, 卷号: 81, 期号: 11, 页码: 15085-15102 作者: Hu, Ande; Wu, Lijian; Huang, Jiankang; Fan, Ding; Xu, Zhenya 收藏 \| 浏览/下载：29/0 \| 提交时间：2022/06/20 Chemical activation Convolution Convolutional neural networks Defects Image enhancement Image segmentation Processing Welds Activation functions Convolution neural network Convolutional neural network Exponential linear unit function Exponentials Linear units Pooling Unit functions Weld defects Weld defects recognition
	X-ray image defect recognition method for pipe weld based on improved convolutional neural network 期刊论文 Hanjie Xuebao/Transactions of the China Welding Institution, 2020, 卷号: 41, 期号: 1, 页码: 7-11 作者: Fan, Ding; Hu, Ande; Huang, Jiankang; Xu, Zhenya; Xu, Xu 收藏 \| 浏览/下载：16/0 \| 提交时间：2020/11/14 Chemical activation Convolution Convolutional neural networks Defects Extraction Feature extraction Image enhancement Image recognition Pixels Welds Activation functions Convolution neural network Local information Network parameters Recognition accuracy Region of interest Saturation region Segmentation algorithms
	Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory 期刊论文 Journal of Intelligent Manufacturing, 2019, 卷号: 30, 页码: 2013-2024 作者: Jiang, Hongquan; Wang, Rongxi; Gao, Zhiyong; Gao, Jianmin; Wang, Hongye 收藏 \| 浏览/下载：27/0 \| 提交时间：2019/11/19 Accuracy of classifications Analytical Hierarchy Process D S evidence theory Evidence theories Frequency histograms Manufacturing industries Radiographic testing Recognition rates
	Research on the Defects Identify of the Weld-line's Image of the In-service Pipeline Based on BP Neural Network 会议论文 1st International Conference on Electronics Instrumentation & Information Systems (EIIS), Harbin, China, June 3-5, 2017 作者: Yuan, Gao; Yin J(尹健); Wang ZW(王照伟); Ceng P(曾鹏); Li D(李栋) 收藏 \| 浏览/下载：17/0 \| 提交时间：2018/06/19 Welding-line defects BP neural network Image recognition
	Research on the Defects Identify of the Weld-line's Image of the In-service Pipeline Based on BP Neural Network 会议论文 Harbin, China, June 3-5, 2017 作者: Yin J(尹健); Yuan, Gao 收藏 \| 浏览/下载：0/0 \| 提交时间：2018/06/19 Welding-line Defects BP Neural Network Image Recognition
	Self-adaptive characteristics segmentation optimized algorithm of weld defects based on flooding 会议论文作者: Dang Changying; Gao Jianmin; Wang Zhao; Chen Fumin 收藏 \| 浏览/下载：4/0 \| 提交时间：2019/12/10 defect segmentation flooding algorithm defect-seed point self-adaptive characteristics Radiographic testing
	Computer extraction and recognition method research on weld defect 会议论文 Beijing, China, April 1, 2013 - April 2, 2013 作者: Zhang, Peng Lin; Wu, Zheng Bin; Niu, Xian Ming; Zhao, Zhi Qiang 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/11/15 Extraction Image processing Pattern recognition Welds Defect extraction Defect recognition Feature parameters Image preprocessing Recognition methods REmove noise Weld defects Weld image
	A diagnosis model for appearance defects of joints in RSW 期刊论文 Hanjie Xuebao/Transactions of the China Welding Institution, 2011, 卷号: 32, 期号: 4, 页码: 5-8 作者: Zhang, Pengxian; Zhang, Zhifen; Chen, Jianhong; Wang, Xiaoe 收藏 \| 浏览/下载：0/0 \| 提交时间：2020/11/14 Binary images Joints (structural components) Pattern recognition Spot welding Support vector machines Appearance qualities Characteristic parameters Evaluation modeling Image information Resistance spot welding Sources of informations Verification results Welding parameters
	焊缝X射线图像缺陷的自动提取与分割期刊论文 2010, 2010 杨静; 王明泉; YANG JING; WANG MINGQUAN 收藏 \| 浏览/下载：2/0
	Recognition of weld defect types 期刊论文 Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2010, 卷号: 44, 期号: [db:dc_citation_issue], 页码: 100-103 作者: Shen, Qingming; Gao, Jianmin; Li, Cheng 收藏 \| 浏览/下载：3/0 \| 提交时间：2019/12/10 Constrained optimi-zation problems Feature vectors Multi layer perceptron Multi-class support vector machines Radiographic images Recognition accuracy Small training Weld defects

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