刘晓媛
职称：讲师
学历：博士研究生
学科：控制科学与工程
研究领域或方向：极地智能状态监测、复杂数据解析和工业图像识别
邮箱：liuxiaoyuanz@126.com
- 教育背景
- 主讲课程
- 学术兼职
- 学术论文
- 主持项目
- 授权专利
太原理工大学 2021.03-至今 博士后 （合作导师：孙宏斌）
东北大学 2020.09-2024.07 控制科学与工程 博士
东北大学 2018.09-2020.08 电力系统及其自动化 硕士
山西师范大学2014.09-2018.07 电子信息科学与技术 学士
东北大学 2020.09-2024.07 控制科学与工程 博士
东北大学 2018.09-2020.08 电力系统及其自动化 硕士
山西师范大学2014.09-2018.07 电子信息科学与技术 学士
智能控制理论基础
IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Industrial Informatics、IEEE Transactions on Instrumentation and Measurement
等国际SCI期刊审稿人、《综合智慧能源》青年编委
等国际SCI期刊审稿人、《综合智慧能源》青年编委
[1] Xiaoyuan Liu, Jinhai Liu, et.al. Basic-Class and Cross-Class Hybrid Feature Learning for Class-imbalanced Weld Defect Recognition, IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2022.3228702.
[2] Xiaoyuan Liu, Jinhai Liu, Huanqun Zhang and Huaguang Zhang. Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network[J]. Computers in Industry, 2024, 161: 104123.
[3] Xiaoyuan Liu, Jinhai Liu, Mingrui Fu, Huaguang Zhang, Fengyuan Zuo and Hang Xu, "A Novel Industrial Defect Recognition Method Under Label Noise Based on Parallel Hybrid Penalty Network," IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2024.3508774.
[4] Xiaoyuan Liu, Jinhai Liu, et.al, DGICR-Net: Dual-Graph Interactive Consistency Reasoning Network for Weld Defect Recognition With Limited Labeled Samples, IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-12, 2024, Art no. 4503612, DOI: 10.1109/TIM.2024.3372220.
[5] Jinhai Liu, Xiaoyuan Liu, et.al. A Defect Recognition Method for Low-quality Weld Image Based on Consistent Multi-scale Feature Mapping. IEEE Transactions on Instrumentation and Measurement. 71(4):1-11.10.1109/TIM.2022.3171609.
[6] Jiamin Wei, Xiaoyuan Liu*, He Ren, et al. Unsupervised Anomaly Detection via Multi-Frequency Structure and Visual Sampling[C]//2025 2nd International Conference on Image Processing, Multimedia Technology and Machine Learning (IPMML). IEEE, 2025: 245-249.
[7] Xiaoyuan Liu, Jinhai Liu, et.al. "Industrial Anomaly Classification Method Based on EMD Metric Loss," 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 8208-8212, doi: 10.1109/CAC53003.2021.9727730.
[8] Xiaoyuan Liu, Jinhai Liu, Fuming Qu, Hongfei Zhu, Danyu Lu. A Weld Defect Detection Method Based on Triplet Deep Neural Network [C]. 2020 Chinese Control And Decision Conference (CCDC), 2020, pp. 649-653, doi: 10.1109/CCDC49329.2020.9164549.
[2] Xiaoyuan Liu, Jinhai Liu, Huanqun Zhang and Huaguang Zhang. Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network[J]. Computers in Industry, 2024, 161: 104123.
[3] Xiaoyuan Liu, Jinhai Liu, Mingrui Fu, Huaguang Zhang, Fengyuan Zuo and Hang Xu, "A Novel Industrial Defect Recognition Method Under Label Noise Based on Parallel Hybrid Penalty Network," IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2024.3508774.
[4] Xiaoyuan Liu, Jinhai Liu, et.al, DGICR-Net: Dual-Graph Interactive Consistency Reasoning Network for Weld Defect Recognition With Limited Labeled Samples, IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-12, 2024, Art no. 4503612, DOI: 10.1109/TIM.2024.3372220.
[5] Jinhai Liu, Xiaoyuan Liu, et.al. A Defect Recognition Method for Low-quality Weld Image Based on Consistent Multi-scale Feature Mapping. IEEE Transactions on Instrumentation and Measurement. 71(4):1-11.10.1109/TIM.2022.3171609.
[6] Jiamin Wei, Xiaoyuan Liu*, He Ren, et al. Unsupervised Anomaly Detection via Multi-Frequency Structure and Visual Sampling[C]//2025 2nd International Conference on Image Processing, Multimedia Technology and Machine Learning (IPMML). IEEE, 2025: 245-249.
[7] Xiaoyuan Liu, Jinhai Liu, et.al. "Industrial Anomaly Classification Method Based on EMD Metric Loss," 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 8208-8212, doi: 10.1109/CAC53003.2021.9727730.
[8] Xiaoyuan Liu, Jinhai Liu, Fuming Qu, Hongfei Zhu, Danyu Lu. A Weld Defect Detection Method Based on Triplet Deep Neural Network [C]. 2020 Chinese Control And Decision Conference (CCDC), 2020, pp. 649-653, doi: 10.1109/CCDC49329.2020.9164549.
1. 国家自然科学基金青年基金，基于双流感知的极地冰盖表面裂隙智能识别与演化方法研究，2026.01-2028.12，30万，主持。
2. 中国博士后科学基金，2025年度国家资助博士后研究人员计划（C档）资助，介观耦合机理内嵌的极地冰裂隙双流识别与演化方法研究，2025.07-2027.07，24万，主持。
3. 国家自然科学基金联合基金项目, 管道移动机器人复杂环境自适应巡检理论与方法研究, 2022.01-2025.12, 260万元, 参与
2. 中国博士后科学基金，2025年度国家资助博士后研究人员计划（C档）资助，介观耦合机理内嵌的极地冰裂隙双流识别与演化方法研究，2025.07-2027.07，24万，主持。
3. 国家自然科学基金联合基金项目, 管道移动机器人复杂环境自适应巡检理论与方法研究, 2022.01-2025.12, 260万元, 参与
1. 基于难样本挖掘的深度度量网络的风机多故障诊断方法，CN110555478
2. 管道巡检机器人的导航方法及装置、存储介质、终端, CN117432949B
3. 针对埋地管道野外巡检机器人的自主导航系统及方法, CN114879699A
4. 两阶段超声无损检测信号高精度识别方法，CN116046906A
5. 裂纹检测方法、装置、电子设备及存储介质，CN115496167A
2. 管道巡检机器人的导航方法及装置、存储介质、终端, CN117432949B
3. 针对埋地管道野外巡检机器人的自主导航系统及方法, CN114879699A
4. 两阶段超声无损检测信号高精度识别方法，CN116046906A
5. 裂纹检测方法、装置、电子设备及存储介质，CN115496167A
