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学者姓名：陈静
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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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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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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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The low voltage direct current (LVdc) system effectively integrates renewable energy sources and diverse dc loads. It eliminates unnecessary energy conversion steps between dc distribution units and ac grids, thereby enhancing energy efficiency. In LVdc systems, voltage source converters (VSCs) serve as vital interfaces for converting energy between ac and dc systems, however, their capability on dc fault ride-through is usually lacked. Furthermore, the existing dc circuit breakers struggle to reliably isolate faults before VSCs blocked, thereby compromising VSC safety. To address these issues, this article introduces a novel topology self-adjusted fault current limiter (NSAFCL). In normal operating mode, the impedance of NSAFCL is controlled in a parallel state, and a bias power with adaptable output is designed to bypass NSAFCL, minimizing its influence during normal operation. In fault mode, the impedance of NSAFCL is controlled in a series state, and a current limiting resistor is introduced, shaving the fault current and maintaining the fault voltage. Finally, the simulation and experiment are conducted to verify the feasibility of NSAFCL, and results demonstrate that compared to traditional schemes, the proposed NSAFCL offers extended current limitations, prevents VSC blocking, and reduces the peak fault current by 70%.
Keyword ：
Active fault current limiter Active fault current limiter Circuit faults Circuit faults dc fault ride-through dc fault ride-through fault current limitation fault current limitation Fault currents Fault currents Impedance Impedance Inductors Inductors Limiting Limiting Power conversion Power conversion Topology Topology voltage source converter (VSC) voltage source converter (VSC) VSC-low voltage direct current (LVdc) distribution VSC-low voltage direct current (LVdc) distribution
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| GB/T 7714 | Miao, Xiren , Fu, Minyi , Lin, Baoquan et al. A Novel Topology Self-Adjusted Fault Current Limiter for VSC-LVDC Systems [J]. | IEEE TRANSACTIONS ON POWER ELECTRONICS , 2024 , 39 (7) : 8597-8609 . |
| MLA | Miao, Xiren et al. "A Novel Topology Self-Adjusted Fault Current Limiter for VSC-LVDC Systems" . | IEEE TRANSACTIONS ON POWER ELECTRONICS 39 . 7 (2024) : 8597-8609 . |
| APA | Miao, Xiren , Fu, Minyi , Lin, Baoquan , Liu, Xiaoming , Jiang, Hao , Chen, Jing . A Novel Topology Self-Adjusted Fault Current Limiter for VSC-LVDC Systems . | IEEE TRANSACTIONS ON POWER ELECTRONICS , 2024 , 39 (7) , 8597-8609 . |
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Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection. © 2024 Elsevier B.V.
Keyword ：
Convolution Convolution Domain Knowledge Domain Knowledge Fault detection Fault detection Nuclear energy Nuclear energy Nuclear fuels Nuclear fuels Nuclear power plants Nuclear power plants Numerical methods Numerical methods Pressurized water reactors Pressurized water reactors Signal reconstruction Signal reconstruction System stability System stability
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model [J]. | Knowledge-Based Systems , 2024 , 300 . |
| MLA | Lin, Weiqing et al. "Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model" . | Knowledge-Based Systems 300 (2024) . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Ye, Mingxin , Xu, Yong , Liu, Xinyu et al. Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model . | Knowledge-Based Systems , 2024 , 300 . |
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For the maintenance of power lines, a core task is to diagnose the fault of different components from the aerial inspection images. Currently, deep-learning models trained on defect samples have achieved promising performances for automatic fault diagnosis. However, the slow accumulation process of fault data leads to a long-term challenge of data insufficiency in this field. In this article, a normalized multihierarchy embedding matching (NMHEM)-based anomaly detection method is proposed to inspect power line faults, which only utilizes defect-free samples during training. To impart the NMHEM with the ability to detect anomalous patterns in images, three main modules are introduced. First, the embedding generation module (EGM) is employed to extract deep hierarchical representations. Next, hierarchy-wise anomaly scores are calculated through the embedding matching module (EMM) to measure the anomalous degree, which can make the model more discriminative at different hierarchies. Finally, a normalizing module (NM) is developed and served as a credible scoring function indicating the probability of anomaly occurring, thus boosting the performance of the fault diagnosis. The proposed NMHEM adaptively aggregates local spatial and global semantic information which leverages the available nominal knowledge from normal data, achieving effective fault diagnosis of power lines. Experiments are conducted on the dataset that contains five key components. Results show that our method achieves 88.4% area under the curve (AUC) and 80.5% F1-score, which outperforms other supervised and semisupervised methods.
Keyword ：
Anomaly detection Anomaly detection deep learning deep learning Deep learning Deep learning embedding matching embedding matching fault diagnosis fault diagnosis Fault diagnosis Fault diagnosis Feature extraction Feature extraction Inspection Inspection Insulators Insulators power line inspection power line inspection Training Training
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| GB/T 7714 | Liu, Xinyu , Miao, Xiren , Jiang, Hao et al. Fault Diagnosis in Power Line Inspection Using Normalized Multihierarchy Embedding Matching [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2023 , 72 . |
| MLA | Liu, Xinyu et al. "Fault Diagnosis in Power Line Inspection Using Normalized Multihierarchy Embedding Matching" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 72 (2023) . |
| APA | Liu, Xinyu , Miao, Xiren , Jiang, Hao , Chen, Jing , Chen, Zhenghua . Fault Diagnosis in Power Line Inspection Using Normalized Multihierarchy Embedding Matching . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2023 , 72 . |
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随着智能电网的迅速发展，在变电站现场安全作业检测时，时常发生作业人员安全帽、安全带、绝缘手套等佩戴异常情况。为了进一步保障电网安全，针对变电站现场环境复杂、人员较多、实时性要求高等情况，本文提出一种M-YOLOV5s算法，针对检测的实时性要求，将YOLOV5s的特征提取网络换成MobileNet V3 small网络，并引入自适应ACON激活函数。同时，针对现场小目标，在模型特征融合层中加入轻量级注意力机制CBAM，进一步提升模型性能。实验结果表明，采用本文所提出的方法进行电网现场作业安全检测，模型体积缩小至4.89MB，在极大提高模型检测速度的同时，模型精度仅损失3.12%，更有利于模型的前端部署。此外，该方法能够满足电网现场作业人员穿戴安全检测的实时性。
Keyword ：
电网作业 电网作业 目标识别 目标识别 穿戴安全检测 穿戴安全检测 轻量化 轻量化
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| GB/T 7714 | 刘加伟 , 陈新楚 , 江灏 et al. 轻量级深度学习电网作业安全检测算法研究 [J]. | 福建电脑 , 2023 , 39 (04) : 13-18 . |
| MLA | 刘加伟 et al. "轻量级深度学习电网作业安全检测算法研究" . | 福建电脑 39 . 04 (2023) : 13-18 . |
| APA | 刘加伟 , 陈新楚 , 江灏 , 缪希仁 , 陈静 . 轻量级深度学习电网作业安全检测算法研究 . | 福建电脑 , 2023 , 39 (04) , 13-18 . |
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