Query：
学者姓名：陈静
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the increasing demand for indoor location-based services (LBS), indoor positioning technology, especially the received signal strength (RSS)-based positioning technology, has attracted extensive attention. In the process of localization, the difference in RSS caused by heterogeneity between different devices cannot be ignored. It leads to the degradation of positioning accuracy. A comprehensive overview of device heterogeneity management methods in indoor positioning is presented in this chapter to deliver a superior solution. An analysis of the causes of device heterogeneity is conducted at the hardware and communication layers. The existing methods to deal with the device heterogeneity are summarized. The approaches for dealing with device heterogeneity are divided into three categories based on the development of technology. The methods are adjustment approach based on linear transformation, calibration-free function mapping method, and non-absolute fingerprint method, respectively. The principles of the implementation for these methods are presented in this chapter. Different evaluation metrics are utilized to participate in the comparison of these methods. The advantages and issues are summarized. Also, the future research trends are proposed at the end of this chapter. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Keyword ：
Calibration-free function mapping Calibration-free function mapping Device heterogeneity Device heterogeneity Linear transformations Linear transformations Non-absolute fingerprint Non-absolute fingerprint Received signal strength (RSS) Received signal strength (RSS)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yin, C. , Jiang, H. , Chen, J. . Overview of Approaches for Device Heterogeneity Management During Indoor Localization [未知]. |
| MLA | Yin, C. et al. "Overview of Approaches for Device Heterogeneity Management During Indoor Localization" [未知]. |
| APA | Yin, C. , Jiang, H. , Chen, J. . Overview of Approaches for Device Heterogeneity Management During Indoor Localization [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The top oil temperature in ultra-high voltage (UHV) reactors has attracted enormous interest due to its wide applications in fault diagnosis and insulation evaluation. In this work, the precise prediction method based on the Seq2Seq module with the convolutional block attention mechanism is proposed for the UHV reactor. To reduce the influence of vibratility and improve computational efficiency, a combination of the encoding layer and decoding layer named Seq2Seq is performed to reconstruct the complex raw data. The convolutional block attention mechanism (CBAM), composed of spatial attention and channel attention, is utilized to maximize the use of information in data. The Seq2Seq-CBAM is established to forecast the variation tendency of the oil temperatures in the UHV reactor. The experimental results show that the proposed method achieves high prediction accuracy for the top oil temperature in both single-step and multi-step.
Keyword ：
attention attention convolution block attention mechanism (CBAM) convolution block attention mechanism (CBAM) online detection scenario online detection scenario top oil temperature top oil temperature UHV reactor UHV reactor
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jiang, Hao , Zhang, Hongwei , Chen, Jing et al. Forecasting top oil temperature for UHV reactor using Seq2Seq model with convolutional block attention mechanism [J]. | INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS , 2023 , 73 (4) : 283-302 . |
| MLA | Jiang, Hao et al. "Forecasting top oil temperature for UHV reactor using Seq2Seq model with convolutional block attention mechanism" . | INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 73 . 4 (2023) : 283-302 . |
| APA | Jiang, Hao , Zhang, Hongwei , Chen, Jing , Xiao, Sa , Miao, Xiren , Lin, Weiqing . Forecasting top oil temperature for UHV reactor using Seq2Seq model with convolutional block attention mechanism . | INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS , 2023 , 73 (4) , 283-302 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Human sensing based on the low-resolution infrared sensor is widely used in hand gestures recognition, activity recognition, intrusion detection, etc. However, the information about humans acquired by the previous human sensing system using the infrared sensor is limited. In this paper, a human pose estimation system is proposed to realize the three-dimensional skeleton information acquisition by low-resolution infrared sensors. It is a difficult task to acquire human pose estimation with more rich human information from low-resolution infrared sensors. The system leverages the 8 x 8 pixels low-resolution infrared array sensor to collect the activity data and the Kinect v2 camera to capture the three-dimensional skeleton of the human body as annotations of the infrared data. The convolutional neural network-bidirectional gated recurrent unit model with attention mechanism (CNN-BiGRU-AM) model is employed for model training to effectively extract the characteristics of the infrared data from spatial and temporal dimensions. The attention mechanism (AM) can improve the ability of the model to capture important local information. The bone joint point data predicted by the model are utilized to draw the three-dimensional skeleton diagram. The k-means clustering algorithm is applied to eliminate the outliers that affect the overall visualization effect in the prediction. The accuracy and completeness of human pose estimation are measured by the euclidean distance between the real coordinates of the bone joint points obtained by Kinect v2 camera and the coordinates predicted by the model. The proportion of the number of predictions with euclidean distance less than a threshold 20 mm is 90.151%, representing the accuracy of human pose estimation. The experimental results show that three-dimensional skeleton information can be acquired accurately by the low-resolution infrared array sensor and the subtle difference within each activity can be observed through the 3D human pose to improve the effect of activity recognition.
Keyword ：
Attention mechanism Attention mechanism Bidirectional gated recurrent unit (BiGRU) Bidirectional gated recurrent unit (BiGRU) Convolutional neural network (CNN) Convolutional neural network (CNN) Low-resolution infrared array sensor Low-resolution infrared array sensor Skeleton-based 3D human pose estimation Skeleton-based 3D human pose estimation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Jing , Chen, Deying , Jiang, Hao et al. Skeleton-based 3D human pose estimation with low-resolution infrared array sensor using attention based CNN-BiGRU [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (5) : 2049-2062 . |
| MLA | Chen, Jing et al. "Skeleton-based 3D human pose estimation with low-resolution infrared array sensor using attention based CNN-BiGRU" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 5 (2023) : 2049-2062 . |
| APA | Chen, Jing , Chen, Deying , Jiang, Hao , Miao, Xiren , Yin, Cunyi . Skeleton-based 3D human pose estimation with low-resolution infrared array sensor using attention based CNN-BiGRU . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (5) , 2049-2062 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Differential protection has been introduced into the distribution network to address the ineffectiveness of traditional protection due to the uncertainties of power flow caused by the access of multiple inverter-interfaced distributed generation (IIDG). Designed for lower data synchronization costs, this paper proposes a novel differential protection scheme based on improved dynamic time warping (DTW) distance. The proposed algorithm can effectively alleviate the singularity caused by DTW's excessive X-axis distortion through relaxation search and derivative estimation and minimize the impact of data synchronization errors and data loss. Based on the proposed algorithm, the protection scheme combined with the feeder terminal unit (FTU) for fast fault isolation is designed, which can make full use of the existing equipment and reduce costs. Moreover, multiple influencing factors considered in this scheme, including transition resistance, penetration rate, and output intermittency of IIDG, variable network topology, underground cables, and hybrid lines. The test results demonstrate that the proposed protection scheme can effectively isolate short-circuit faults in various scenarios.
Keyword ：
Costs Costs differential current protection differential current protection Distribution network Distribution network Distribution networks Distribution networks Fault currents Fault currents feeder terminal unit feeder terminal unit Impedance Impedance improved DTW improved DTW inverter-interfaced distributed generation inverter-interfaced distributed generation Market research Market research positive sequence current positive sequence current Resistance Resistance Synchronization Synchronization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Miao, Xiren , Zhao, Dan , Lin, Baoquan et al. A Differential Protection Scheme Based on Improved DTW Algorithm for Distribution Networks With Highly-Penetrated Distributed Generation [J]. | IEEE ACCESS , 2023 , 11 : 40399-40411 . |
| MLA | Miao, Xiren et al. "A Differential Protection Scheme Based on Improved DTW Algorithm for Distribution Networks With Highly-Penetrated Distributed Generation" . | IEEE ACCESS 11 (2023) : 40399-40411 . |
| APA | Miao, Xiren , Zhao, Dan , Lin, Baoquan , Jiang, Hao , Chen, Jing . A Differential Protection Scheme Based on Improved DTW Algorithm for Distribution Networks With Highly-Penetrated Distributed Generation . | IEEE ACCESS , 2023 , 11 , 40399-40411 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The concentration of dissolved gases in transformer oil can be utilized to diagnose faults in transformers. However, substandard online monitoring data may lead to inaccurate fault diagnosis outcomes, resulting in severe repercussions. Hence, this study presents a novel approach for anomaly detection in dissolved gas online monitoring data in transformer oil using stacking ensemble learning. Firstly, a sliding time window is employed to preprocess the monitoring data and generate a dataset consisting of time series monitoring data. Subsequently, evaluation metrics and diversity measures are applied to select distinct base learners and a meta-learner for the stacking model. This approach amalgamates the strengths and disparities of various learners. Lastly, comparative analysis of case studies demonstrates the effectiveness of the proposed method in distinguishing different types of anomalies in dissolved gas online monitoring data, exhibiting superior performance in terms of accuracy, F1 score, and area under curve(AUC). © 2023 IEEE.
Keyword ：
Anomaly detection Anomaly detection Dissolution Dissolution E-learning E-learning Gases Gases Learning systems Learning systems Oil filled transformers Oil filled transformers Partial discharges Partial discharges
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhao, Rui , Chen, Jing , Yin, Cunyi et al. Anomaly Detection Method for Online Monitoring Data of Dissolved Gas in Transformer Using Stacking Ensemble Learning [C] . 2023 . |
| MLA | Zhao, Rui et al. "Anomaly Detection Method for Online Monitoring Data of Dissolved Gas in Transformer Using Stacking Ensemble Learning" . (2023) . |
| APA | Zhao, Rui , Chen, Jing , Yin, Cunyi , Jiang, Hao , Miao, Xiren , Lin, Weiqing . Anomaly Detection Method for Online Monitoring Data of Dissolved Gas in Transformer Using Stacking Ensemble Learning . (2023) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
