Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data
文献类型：期刊论文
| 作者 | Long, JY; Zhang, RX; Yang, Z ; Huang, YW; Liu, Y; Li, C
|
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2022 |
| 卷号 | 71页码:3515411 |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2022.3181894 |
| 文献子类 | Article |
| 电子版国际标准刊号 | 1557-9662 |
| 语种 | 英语 |
| WOS记录号 | WOS:000814675900003 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/299529]  |
| 专题 | 高能物理研究所_东莞分部 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Long, JY,Zhang, RX,Yang, Z,et al. Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:3515411. |
| APA | Long, JY,Zhang, RX,Yang, Z,Huang, YW,Liu, Y,&Li, C.(2022).Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,3515411. |
| MLA | Long, JY,et al."Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):3515411. |
入库方式： OAI收割
来源：高能物理研究所
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