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学者姓名：缪希仁
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Abstract ：
For intelligent inspection of power lines, a core task is to detect components in aerial images. Currently, deep supervised learning, a data-hungry paradigm, has attracted great attention. However, considering real-world scenarios, labeled data are usually limited, and the utilization of abundant unlabeled data is rarely investigated in this field. This study deploys a pretrained model for power line component detection based on a self-supervised pretraining approach, which exploits useful information from unannotated data. Concretely, we design a new masking strategy based on the structural characteristic of power lines to guide the pretraining process with meaningful semantic content. Meanwhile, a Siamese architecture is proposed to extract complete global features by using dual reconstruction with semantic targets provided by the proposed masking strategy. Then, the knowledge distillation is utilized to enable the pretrained model to learn both domain-specific and general representations. Moreover, a feature pyramid mechanism is adopted to capture multiscale features, which can benefit the detection task. Experimental results show that the proposed approach can successfully improve the performance of a variety of detection frameworks for power line components, and outperforms other self-supervised pretraining methods.
Keyword ：
Component detection Component detection deep learning deep learning machine vision machine vision power line inspection power line inspection self-supervised pretraining self-supervised pretraining
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| GB/T 7714 | Liu, Xinyu , Miao, Xiren , Jiang, Hao et al. Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 513-523 . |
| MLA | Liu, Xinyu et al. "Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 513-523 . |
| APA | Liu, Xinyu , Miao, Xiren , Jiang, Hao , Chen, Jing , Wu, Min , Chen, Zhenghua . Tower Masking MIM: A Self-Supervised Pretraining Method for Power Line Inspection . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 513-523 . |
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针对低压交流断路器用涡流斥力操动机构多参数、多目标优化设计最优解集分布性问题,提出一种改进型NSGA-Ⅱ算法的多目标优化设计方法.首先,分析涡流斥力操动机构原理及建立操动机构有限元分析仿真模型,根据应用需求确定涡流斥力操动机构优化目标与优化变量.其次,采用循环拥挤度排序算法替代原有的拥挤度排序算法,以提升Pareto最优解集的分布性;采用基于反三角函数的自适应策略更新交叉变异概率,以提升算法的适应性;经ZDT系列函数测试,证明其具有良好的适应性和解的分布性.最后,利用改进型NSGA-Ⅱ算法对涡流斥力操动机构进行多目标优化,获取Pareto最优前沿,取得了较好的效果.
Keyword ：
Pareto Pareto 分布性 分布性 多目标 多目标 拥挤距离 拥挤距离 涡流斥力 涡流斥力 适应性 适应性
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| GB/T 7714 | 陈翔 , 缪希仁 , 庄胜斌 et al. 涡流斥力快速操动机构改进型NSGA-Ⅱ多目标优化设计 [J]. | 电器与能效管理技术 , 2024 , (1) : 24-30 . |
| MLA | 陈翔 et al. "涡流斥力快速操动机构改进型NSGA-Ⅱ多目标优化设计" . | 电器与能效管理技术 1 (2024) : 24-30 . |
| APA | 陈翔 , 缪希仁 , 庄胜斌 , 谢海鑫 . 涡流斥力快速操动机构改进型NSGA-Ⅱ多目标优化设计 . | 电器与能效管理技术 , 2024 , (1) , 24-30 . |
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Safety monitoring of power operations in power stations is crucial for preventing accidents and ensuring stable power supply. However, conventional methods such as wearable devices and video surveillance have limitations such as high cost, dependence on light, and visual blind spots. WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges. PowerSkel utilizes self-developed CSI sensors to form a mutual sensing network and constructs a CSI acquisition scheme specialized for power scenarios. It significantly reduces the deployment cost and complexity compared to the existing solutions. To reduce interference with CSI in the electricity scenario, a sparse adaptive filtering algorithm is designed to preprocess the CSI. CKDformer, a knowledge distillation network based on collaborative learning and self-attention, is proposed to extract the features from CSI and establish the mapping relationship between CSI and keypoints. The experiments are conducted in a real-world power station, and the results show that the PowerSkel achieves high performance with a PCK@50 of 96.27%, and realizes a significant visualization on pose estimation, even in dark environments. Our work provides a novel low-cost and high-precision pose estimation solution for power operation. IEEE
Keyword ：
channel state information channel state information deep learning deep learning Electric power operation safety Electric power operation safety Feature extraction Feature extraction human pose estimation human pose estimation Monitoring Monitoring Pose estimation Pose estimation Power generation Power generation Safety Safety Sensors Sensors WiFi sensing WiFi sensing Wireless fidelity Wireless fidelity
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| GB/T 7714 | Yin, C. , Miao, X. , Chen, J. et al. PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station [J]. | IEEE Internet of Things Journal , 2024 , 11 (11) : 1-1 . |
| MLA | Yin, C. et al. "PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station" . | IEEE Internet of Things Journal 11 . 11 (2024) : 1-1 . |
| APA | Yin, C. , Miao, X. , Chen, J. , Jiang, H. , Yang, J. , Zhou, Y. et al. PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station . | IEEE Internet of Things Journal , 2024 , 11 (11) , 1-1 . |
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针对当前高校实验室异常用电行为，提出一种基于Stacking相异模型融合的异常行为检测方法。考虑相异基学习器挖掘实验室用电行为规律的差异性，对相异基学习器进行优选。利用随机森林作为元学习器，充分融合相异基学习器的优势，弥补各基学习器的缺陷，构建基于Stacking相异模型融合的集成学习模型。通过算例对比分析，验证了基于Stacking相异模型融合的集成学习模型能有效提升单一分类器的异常检测效果，在准确率、F_1分数、ROC曲线下面积和误检率上均优于Bagging、Voting、Adaboost等集成学习方法并能适应样本不平衡的情况。
Keyword ：
Stacking结合策略 Stacking结合策略 实验室安全 实验室安全 异常用电行为 异常用电行为 集成学习 集成学习
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| GB/T 7714 | 陈静 , 王铭海 , 江灏 et al. Stacking相异模型融合的实验室异常用电行为检测 [J]. | 实验室研究与探索 , 2024 , 43 (01) : 231-237 . |
| MLA | 陈静 et al. "Stacking相异模型融合的实验室异常用电行为检测" . | 实验室研究与探索 43 . 01 (2024) : 231-237 . |
| APA | 陈静 , 王铭海 , 江灏 , 缪希仁 , 陈熙 , 郑垂锭 . Stacking相异模型融合的实验室异常用电行为检测 . | 实验室研究与探索 , 2024 , 43 (01) , 231-237 . |
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针对新型电力系统低压台区故障准确定位要求，开发了一种基于“边端协同”架构的低压台区短路故障定位技术。提出多层级短路故障定位的台区物联网架构体系，研究采用二阶最小二乘拟合算法的台区各层级物联终端短路故障检测方法；设计基于高速电力线宽带载波通信的故障定位通信协议，面向智能配变终端，提出基于台区拓扑结构的短路故障定位策略，并开展边缘计算及其硬件化技术实现。在此基础上，通过真型低压交流系统短路故障测试实验，验证了“边端协同”物联台区短路故障定位技术的有效性。
Keyword ：
低压台区 低压台区 故障定位 故障定位 短路故障 短路故障 边端协同 边端协同 高速电力宽带载波 高速电力宽带载波
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| GB/T 7714 | 杨广辉 , 缪希仁 , 庄胜斌 et al. 基于“边端协同”架构的台区短路故障定位技术 [J]. | 南昌大学学报(工科版) , 2024 , 46 (01) : 117-124 . |
| MLA | 杨广辉 et al. "基于“边端协同”架构的台区短路故障定位技术" . | 南昌大学学报(工科版) 46 . 01 (2024) : 117-124 . |
| APA | 杨广辉 , 缪希仁 , 庄胜斌 , 赵鹏飞 . 基于“边端协同”架构的台区短路故障定位技术 . | 南昌大学学报(工科版) , 2024 , 46 (01) , 117-124 . |
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热状态参量预测是特高压变压器绝缘老化评估及故障预警的重要技术方法.然而,现有预测方法侧重高维时间序列分析以构建数据驱动模型,未计及设备内部温度潜在的空间变化规律,为此,提出一种基于时空特征挖掘的特高压变压器热状态参量预测方法.首先,综合考虑多源数据间的相关度与冗余度,提出组合特征筛选策略寻找最优特征子集;其次,结合热状态参量的最优特征子集及相关系数,构建面向热状态参量预测的时空图数据;最后,建立双重自适应图卷积门控循环单元(dual adaptive graph convolution gate recurrent unit,DA-GCGRU)模型,采用节点自适应模块强化油箱内不同部位温度变化趋势的拟合,以适应特定温升趋势;采用图自适应模块自主学习热状态参量的空间温度分布关联性,以推断空间映射关系.实验结果表明,该方法可深度挖掘特高压变压器内部温度的时空变化特性,准确预测绕组温度和顶层油温的变化趋势,具有较好的鲁棒性和泛化性.
Keyword ：
图卷积网络 图卷积网络 特高压变压器 特高压变压器 绕组温度 绕组温度 自适应 自适应 门控循环单元 门控循环单元 顶层油温 顶层油温
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| GB/T 7714 | 林蔚青 , 缪希仁 , 肖洒 et al. 基于时空特征挖掘的特高压变压器热状态参量预测方法 [J]. | 中国电机工程学报 , 2024 , 44 (4) : 1649-1661,中插33 . |
| MLA | 林蔚青 et al. "基于时空特征挖掘的特高压变压器热状态参量预测方法" . | 中国电机工程学报 44 . 4 (2024) : 1649-1661,中插33 . |
| APA | 林蔚青 , 缪希仁 , 肖洒 , 江灏 , 卢燕臻 , 邱星华 et al. 基于时空特征挖掘的特高压变压器热状态参量预测方法 . | 中国电机工程学报 , 2024 , 44 (4) , 1649-1661,中插33 . |
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Thermal state parameters (TSPs) prediction is a significant technique for insulation aging assessment and fault warning of ultra-high voltage (UHV) transformers. However, the existing forecasting methods focus on high-dimensional time series analysis to build data-driven models, and fail to take the potential spatial variation law of the inside temperature into account. Thus, a spatial-temporal features mining based prediction method for TSPs in UHV transformers is proposed. First, the combined feature screening strategy is used to find the optimal feature subset from multi-source data. Second, based on optimal feature subset and correlation coefficient of TSPs, the spatial-temporal graph data for TSPs prediction is constructed. Finally, the dual adaptive graph convolution gate current unit (DA-GCGRU) model is established. The node adaptive module is used to strengthen the fitting of temperature trends in different parts of the fuel tank to adapt to specific temperature rise trends. The graph adaptive module is used to learn the spatial temperature distribution correlation of TSPs to infer the spatial mapping relationship. The results show that the method has good robustness and generalization by deeply mining the spatial-temporal characteristics of the internal parameters in UHV transformers and precisely forecasting the winding and top oil temperature. ©2024 Chin.Soc.for Elec.Eng.
Keyword ：
Convolution Convolution Forecasting Forecasting Oil filled transformers Oil filled transformers Oil tanks Oil tanks Thermal insulation Thermal insulation Time series analysis Time series analysis Transformer windings Transformer windings UHV power transmission UHV power transmission Winding Winding
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Xiao, Sa et al. Forecasting Method for Thermal State Parameters in Ultra-high Voltage Transformers Based on Spatial-temporal Features Mining [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (4) : 1649-1661 . |
| MLA | Lin, Weiqing et al. "Forecasting Method for Thermal State Parameters in Ultra-high Voltage Transformers Based on Spatial-temporal Features Mining" . | Proceedings of the Chinese Society of Electrical Engineering 44 . 4 (2024) : 1649-1661 . |
| APA | Lin, Weiqing , Miao, Xiren , Xiao, Sa , Jiang, Hao , Lu, Yanzhen , Qiu, Xinghua et al. Forecasting Method for Thermal State Parameters in Ultra-high Voltage Transformers Based on Spatial-temporal Features Mining . | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (4) , 1649-1661 . |
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Fault detection of electrical poles is part of the daily operation of power utilities to ensure the sustainability of power transmission. This paper develops a method for intelligent detection of fallen poles based on the improved YOLOX. The hyper-parameters in this method are optimized automatically by Particle Swarm Optimization (PSO) including batch size and input resolution. During parameter optimization, a specific comprehensive evaluation metric is presented as the fitness function to obtain optimal solutions with low labor cost and high method performance. In addition, virtual pole images are generated by 3D Studio Max to overcome the imbalance problem of normal and fault data. The results show that the proposed method can achieve 95.7% of recall and 98.9% of precision, which demonstrates the high accuracy of the method in fallen pole detection. In the comparative experiment, the proposed PSO-YOLOX method is superior to the existing methods including original YOLOX and Faster R-CNN, which verifies the effectiveness of automatic optimization and virtual data augmentation.
Keyword ：
Fallen poles detection Fallen poles detection Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) UAV inspection UAV inspection YOLOX YOLOX
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| GB/T 7714 | Jiang, Hao , Wang, Ben , Wu, Li et al. Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2024 . |
| MLA | Jiang, Hao et al. "Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization" . | MULTIMEDIA TOOLS AND APPLICATIONS (2024) . |
| APA | Jiang, Hao , Wang, Ben , Wu, Li , Chen, Jing , Liu, Xinyu , Miao, Xiren . Fallen detection of power distribution poles in UAV inspection using improved YOLOX with particle swarm optimization . | MULTIMEDIA TOOLS AND APPLICATIONS , 2024 . |
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In the field of multi-style textile defect detection, a common challenge is the difficulty of adapting the inherent detection model to different styles of textile defects. Changes in the color or style of the textile often result in a decrease in the accuracy of defect detection. Relying solely on the model for fine-tuning inspections can lead to catastrophic forgetting, which significantly impacts the performance of the textile defect detector. To address these challenges, a multi-task correlation distillation (MTCD) anomaly detection method based on knowledge distillation and representative sampling is proposed to detect multi-style textile defects. To enable MTCD to detect defects of new-style textiles while maintaining the detection of old-style textiles, two main modules are introduced. The distillation adaptation module (DAM) explores the intra-feature correlation in the feature space of the target detector, allowing the student model to acquire knowledge of new-style textile defect detection while inheriting the teacher model's detection ability for old-style textile defects. The representative sampling module (RSM) stores representative knowledge of textile defect detection for old-style textiles, facilitating the transfer of knowledge learned from detecting new-style textile defect styles and maintaining the ability to detect defects in old-style textiles. This increases the detection accuracy of the student model for new-style textile defects. The results show that the proposed MTCD method can adapt to the new textile defect detection while maintaining the accuracy of the old textile defect detection and avoiding the problem of catastrophic forgetting. Furthermore, it offers a better balance between stability and plasticity, making it a promising solution for defect detection of multi-style textiles in industrial production environments. © 2024 SPIE and IS&T.
Keyword ：
Anomaly detection Anomaly detection Defects Defects Distillation Distillation Knowledge management Knowledge management Textiles Textiles
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| GB/T 7714 | Jiang, Hao , Huang, Shicong , Jin, Zhiheng et al. Multi-style textile defect detection using distillation adaptation and representative sampling [J]. | Journal of Electronic Imaging , 2024 , 33 (3) . |
| MLA | Jiang, Hao et al. "Multi-style textile defect detection using distillation adaptation and representative sampling" . | Journal of Electronic Imaging 33 . 3 (2024) . |
| APA | Jiang, Hao , Huang, Shicong , Jin, Zhiheng , Zhang, Minggui , Chen, Jing , Miao, Xiren . Multi-style textile defect detection using distillation adaptation and representative sampling . | Journal of Electronic Imaging , 2024 , 33 (3) . |
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In this article, we proposed a distortion-tolerant method for fiber Bragg grating (FBG) sensor networks based on the estimation of distribution algorithm (EDA) and convolutional neural network (CNN). Addressing the parameter reconstruction of the reflection spectrum, an objective function is formulated to pinpoint the Bragg wavelength detection problem, with the optimal solution acquired via EDA. By incorporating spectral distortion into the objective function, the EDA-based method effectively manages distorted spectrums, ensuring the fidelity of wavelength data. Further, CNN aids in extracting features from the entire FBG sensor network's wavelength information, facilitating the creation of the localization model. By sending the reliable wavelength data obtained by EDA to the trained model, swift identification of the load position is achieved. Testing revealed that under conditions of spectral distortion, EDA can adeptly detect the Bragg wavelength. Additionally, the CNN-trained localization model outperforms other machine-learning techniques. Notably, experimental results demonstrate that the proposed EDA surpasses the second-ranked method, i.e., the maximum method, achieving a root mean square error (RMSE) of merely 1.4503 mm which is substantially lower than the 6.2463 mm achieved by the maximum method. The average localization error remains under 2 mm when 5 out of 9 FBGs' reflection spectra are distorted. Furthermore, Bragg wavelength detection error stays below 1 pm amid spectral distortion. Consequently, our method offers promising application prospects for long-term FBG sensor network monitoring, ensuring high accuracy and robustness in detecting structural damage.
Keyword ：
Bragg wavelength detection Bragg wavelength detection convolutional neural network (CNN) convolutional neural network (CNN) estimation of distribution algorithm (EDA) estimation of distribution algorithm (EDA) fiber Bragg grating (FBG) sensor network fiber Bragg grating (FBG) sensor network Fiber gratings Fiber gratings Load modeling Load modeling Location awareness Location awareness Optical distortion Optical distortion Reflection Reflection Reliability Reliability spectral distortion spectral distortion Strain Strain
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| GB/T 7714 | Luo, Yuemei , Huang, Chenxi , Lin, Chaohui et al. Distortion Tolerant Method for Fiber Bragg Grating Sensor Network Using Estimation of Distribution Algorithm and Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Luo, Yuemei et al. "Distortion Tolerant Method for Fiber Bragg Grating Sensor Network Using Estimation of Distribution Algorithm and Convolutional Neural Network" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Luo, Yuemei , Huang, Chenxi , Lin, Chaohui , Li, Yuan , Chen, Jing , Miao, Xiren et al. Distortion Tolerant Method for Fiber Bragg Grating Sensor Network Using Estimation of Distribution Algorithm and Convolutional Neural Network . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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