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学者姓名：江灏
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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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As an important tool for current electric power patrol, UAVs show intelligence instead of traditional human patrol. In this paper, for the problem of patrol planning for transmission line towers, considering the risk factors of UAV patrol in post-disaster environments, a multi-featured risk estimation is carried out, and a multi-objective optimization model under time and risk conditions is established. Secondly, for this problem model, an improved genetic algorithm based on elite guidance (EGIGA) is used for optimization, which adopts strategies such as partial elite crossover and adaptive mutation to accelerate the convergence performance of the algorithm. Finally, the feasibility and effectiveness of the method in this paper are verified through example simulation and algorithm comparison. © 2024 SPIE.
Keyword ：
elite-guided genetic algorithms elite-guided genetic algorithms multi-objective optimization multi-objective optimization risk estimation risk estimation UAV patrol planning UAV patrol planning
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| GB/T 7714 | Chen, J. , Tang, Y. , Shen, B. et al. An unmanned aerial vehicle path planning method under consideration of transmission line state assessment [未知]. |
| MLA | Chen, J. et al. "An unmanned aerial vehicle path planning method under consideration of transmission line state assessment" [未知]. |
| APA | Chen, J. , Tang, Y. , Shen, B. , Lin, S. , Jiang, H. . An unmanned aerial vehicle path planning method under consideration of transmission line state assessment [未知]. |
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With the development of robot technology, intelligent robot has been widely used in substation inspection. However, the current target detection algorithm faces the problem of too many parameters to realize real-time detection on embedded platform. To address this issue, an improved YOLOv5 algorithm based on AI front-end is proposed for substation instrument detection. The algorithm is based on YOLOv5 network and introduces the SE fusion attention mechanism module, which adaptively learns the relationship between feature channels to improve the model's ability to extract important features of objects. At the same time, TensorRT technology is used for reconstruction and optimization to reduce the computation and improve the detection speed. Experimental results show that, compared with YOLOv5, the algorithm at mAP@.5 and mAP@.5:.95 increases 1.5% and 2.3% respectively, and the detection frame number per second increases 150% to reach 25FPS, which provides the possibility for real-time instrument detection in substation scenarios. © 2024 SPIE.
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| GB/T 7714 | Chen, J. , Hong, X. , Zhang, M. et al. Lightweight target detection model for substation instrumentation and its hardware acceleration for front-end applications [未知]. |
| MLA | Chen, J. et al. "Lightweight target detection model for substation instrumentation and its hardware acceleration for front-end applications" [未知]. |
| APA | Chen, J. , Hong, X. , Zhang, M. , Jiang, H. . Lightweight target detection model for substation instrumentation and its hardware acceleration for front-end applications [未知]. |
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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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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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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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