机构

• 地质与地球物理研究所 [2]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2023 [1]
• 2015 [2]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 An anomaly aware network embedding framework for unsupervised anomalous link detection 期刊论文  OAI收割 DATA MINING AND KNOWLEDGE DISCOVERY, 2023, 页码: 34 作者:  Duan, Dongsheng;  Zhang, Cheng;  Tong, Lingling;  Lu, Jie;  Lv, Cunchi   |  收藏  |  浏览/下载：17/0  |  提交时间：2023/12/04 Anomalous link detection  Network embedding  Graph auto-encoder  Graph convolution network   Optimized finite-difference operators based on Chebyshev auto-convolution combined window function 期刊论文  OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2015, 卷号: 58, 期号: 2, 页码: 628-642 作者:     |  收藏  |  浏览/下载：28/0  |  提交时间：2018/09/26 Finite-difference  Numerical dispersion  Window functions  Main and side lobe  Auto-convolution  Weighted combination   Optimized finite-difference operators based on Chebyshev auto-convolution combined window function 期刊论文  OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2015, 卷号: 58, 期号: 2, 页码: 628-642 作者:  Wang Zhi-Yang;  Liu Hong;  Tang Xiang-De;  Wang Yang   |  收藏  |  浏览/下载：29/0  |  提交时间：2018/09/26 Finite-difference  Numerical dispersion  Window functions  Main and side lobe  Auto-convolution  Weighted combination   A fast target recognition algorithm based on MSA and MSR (EI CONFERENCE) 会议论文  OAI收割 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23, 2012 - August 25, 2012, Xi'an, China 作者:  Wang Y.;  Liu G.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：30/0  |  提交时间：2013/03/25 This paper presents a new fast target recognition algorithm  the proposed method is based on Multi-scale Auto convolution(MSA) and Multi-scale Retinex(MSR). As shown by the comparison with original MSA  it appears that this new technique solves the problem that MSA algorithm is sensitive to illumination and the computational load is significantly reduced to 1/8th of that of the original MSA algorithm  it is also robust to affine transform  light projective transform  noise  thin fog  occlusion and illumination change. the performed experiments show that it has fast searching speed  and can accurately recognize and locate target in real scenes. 2012 IEEE.