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学者姓名：陈静
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Abstract ：
Pose estimation based on visual images is evolving, but it is also limited by environmental factors such as occlusion and darkness. Due to its non-intrusive and ubiquitous characters, WiFi Channel State Information (CSI)-based human activity recognition attracts immense attention. In this paper, a CSI-based passive sensing system is proposed to predict joint points of human skeleton for activity recognition. The system leverages a pair of ESP32 based CSI sensors with bidirectional link that can prevent unidirectional link from missing important activity information to collect the amplitude of CSI signals. The Kinect 2.0 is employed to obtain skeleton data as ground truth label synchronously. A hybrid deep neural network composed of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is utilized to extract features of CSI signal and map to corresponding human skeleton. K-means clustering algorithm is incorporated to cull the outliers. Experimental results demonstrate that the proposed system achieves satisfactory results with 3.489% average error.
Keyword ：
Channel state information (CSI) Channel state information (CSI) Human activities recognition Human activities recognition Human skeleton images Human skeleton images Wi-Fi sensing Wi-Fi sensing
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| GB/T 7714 | Chen, Jing , Wei, Zhouwang , Tong, Yixuan et al. Skeleton-based human activity recognition with wifi CSI using a hybrid approach combining convolutional neural network and long short term memory [J]. | MULTIMEDIA SYSTEMS , 2024 , 30 (6) . |
| MLA | Chen, Jing et al. "Skeleton-based human activity recognition with wifi CSI using a hybrid approach combining convolutional neural network and long short term memory" . | MULTIMEDIA SYSTEMS 30 . 6 (2024) . |
| APA | Chen, Jing , Wei, Zhouwang , Tong, Yixuan , Jiang, Hao , Miao, Xiren , Yin, Cunyi . Skeleton-based human activity recognition with wifi CSI using a hybrid approach combining convolutional neural network and long short term memory . | MULTIMEDIA SYSTEMS , 2024 , 30 (6) . |
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在核电厂安全监控体系中,堆外中子探测器扮演着关键角色.然而,现有针对探测器的故障检测方法侧重于提取时序特征,以及采用固定阈值方式加以辨识故障,未充分利用探测器之间蕴含的空间耦合关系且缺乏灵活性.为此,该文提出1种针对堆外中子探测器的时空动态检测模型(STDDM).该模型由时序卷积网络(TCN)、图卷积网络(GCN)和动态阈值3个模块构成.其中,将TCN和GCN模块相组合,用于提取探测器间隐含的时空关系以重构探测器信号.在此基础上,计算重构与真实信号间的残差,根据个体探测器的残差均值以及整堆探测器残差标准差,设计动态阈值检测策略,使模型能够自适应于反应堆运行工况的变化.通过某地区核电厂真实数据加以验证,所提STDDM不仅能实时且精准地重构探测器信号,而且在不同故障情况下依然具有较强的故障容错能力,证明其在核电厂堆外中子探测器故障检测中的有效性和实用性.
Keyword ：
动态阈值 动态阈值 图卷积网络 图卷积网络 堆外中子探测器 堆外中子探测器 故障检测 故障检测 时序卷积网络 时序卷积网络 核电厂 核电厂
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| GB/T 7714 | 江灏 , 叶铭新 , 林蔚青 et al. 基于时空动态检测的核电厂堆外中子探测器故障检测方法 [J]. | 仪器仪表学报 , 2024 , 45 (9) : 131-144 . |
| MLA | 江灏 et al. "基于时空动态检测的核电厂堆外中子探测器故障检测方法" . | 仪器仪表学报 45 . 9 (2024) : 131-144 . |
| APA | 江灏 , 叶铭新 , 林蔚青 , 陈静 , 缪希仁 . 基于时空动态检测的核电厂堆外中子探测器故障检测方法 . | 仪器仪表学报 , 2024 , 45 (9) , 131-144 . |
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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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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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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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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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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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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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