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学者姓名：陈静
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Abstract ：
Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry. Owing to economic and security concerns, a common method is to train data generated by simulators. However, achieving a satisfactory performance in practical applications is difficult because simulators imperfectly emulate reality. To bridge this gap, we propose a novel framework called simulation-to-reality domain adaptation (SRDA) for forecasting the operating parameters of nuclear reactors. The SRDA model employs a transformer-based feature extractor to capture dynamic characteristics and temporal dependencies. A parameter predictor with an improved logarithmic loss function is specifically designed to adapt to varying reactor powers. To fuse prior reactor knowledge from simulations with reality, the domain discriminator utilizes an adversarial strategy to ensure the learning of deep domain-invariant features, and the multiple kernel maximum mean discrepancy minimizes their discrepancies. Experiments on neutron fluxes and temperatures from a pressurized water reactor illustrate that the SRDA model surpasses various advanced methods in terms of predictive performance. This study is the first to use domain adaptation for real-world reactor prediction and presents a feasible solution for enhancing the transferability and generalizability of simulated data.
Keyword ：
Domain adaptation Domain adaptation Forecasting Forecasting Knowledge transfer Knowledge transfer Nuclear power plant (NPP) Nuclear power plant (NPP) Pressurized water reactor (PWR) Pressurized water reactor (PWR) Transformer Transformer
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| GB/T 7714 | Lin, Wei-Qing , Miao, Xi-Ren , Chen, Jing et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation [J]. | NUCLEAR SCIENCE AND TECHNIQUES , 2025 , 36 (5) . |
| MLA | Lin, Wei-Qing et al. "Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation" . | NUCLEAR SCIENCE AND TECHNIQUES 36 . 5 (2025) . |
| APA | Lin, Wei-Qing , Miao, Xi-Ren , Chen, Jing , Ye, Ming-Xin , Xu, Yong , Jiang, Hao et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation . | NUCLEAR SCIENCE AND TECHNIQUES , 2025 , 36 (5) . |
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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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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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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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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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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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. 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.
Keyword ：
defect detection defect detection knowledge distillation knowledge distillation representative sampling representative sampling 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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As the essential nuclear measurement equipment in new generation nuclear power plants, the self-powered neutron detector (SPND) plays a crucial role in ensuring the safe operation of reactors. The existing fault detection methods focus on time-domain analysis to build data-driven models, without leveraging the spatial coupling relationship of neutron flux in the reactor core. Therefore, an in-core SPND fault detection and isolation method integrating spatial-temporal information is proposed. First, the spatial-temporal graph data for SPND fault detection are established by combining SPND data with the layout of detector components within the reactor. Then, a real-time SPND fault detection model is designed using the graph convolution network-gate recurrent unit (GCN-GRU) and fault isolation (FI) strategy. Finally, using historical data and simulated fault samples from a pressurized water reactor, case analysis demonstrates that the method effectively fuses the spatial-temporal joint information of the overall SPNDs to reconstruct the current signals of individual SPNDs. The method can accurately detect and isolate faulty SPNDs, which exhibits higher accuracy and universality. ©2024 Chin.Soc.for Elec.Eng.
Keyword ：
Electric power plant equipment Electric power plant equipment Fault detection Fault detection Flow visualization Flow visualization Logic gates Logic gates Neutron detectors Neutron detectors Nuclear power plants Nuclear power plants Photomapping Photomapping Pressurized water reactors Pressurized water reactors Programmable logic controllers Programmable logic controllers Reactor cores Reactor cores Reactor operation Reactor operation
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. Fault Detection and Isolation for In-core Self-powered Neutron Detectors Using Spatial-temporal Information Fusion [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (23) : 9310-9322 . |
| MLA | Lin, Weiqing et al. "Fault Detection and Isolation for In-core Self-powered Neutron Detectors Using Spatial-temporal Information Fusion" . | Proceedings of the Chinese Society of Electrical Engineering 44 . 23 (2024) : 9310-9322 . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Lu, Yanzhen , Xu, Yong , Jiang, Hao . Fault Detection and Isolation for In-core Self-powered Neutron Detectors Using Spatial-temporal Information Fusion . | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (23) , 9310-9322 . |
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自给能中子探测器(self-powered neutron detector,SPND)作为新一代核电厂的重要核测设备,其健康状态关乎反应堆安全运行.鉴于现有故障检测方法侧重于时域分析以构建数据驱动模型,未充分考虑SPND在堆芯内的全局空间耦合关系,为此,该文提出一种时空信息融合的堆芯SPND故障检测与隔离方法.首先,结合SPND运行数据与堆内探测器组件布局,构建面向SPND故障检测的时空图数据;其次,结合图卷积网络-门控循环单元(graph convolution network-gate recurrent unit,GCN-GRU)与故障隔离(fault isolation,FI)策略,设计SPND实时故障检测模型;最后,利用某地区压水堆历史监测数据与模拟故障样本进行算例分析,表明该方法可有效融合整体SPND的时空联合信息以重构个体SPND的电流信号,进而准确检测与隔离故障SPND,且具有较好的精确性和普适性.
Keyword ：
图卷积网络 图卷积网络 故障检测 故障检测 故障隔离 故障隔离 核电厂 核电厂 自给能中子探测器 自给能中子探测器 门控循环单元 门控循环单元
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| GB/T 7714 | 林蔚青 , 缪希仁 , 陈静 et al. 时空信息融合的堆芯自给能中子探测器故障检测与隔离方法 [J]. | 中国电机工程学报 , 2024 , 44 (23) : 9310-9322,中插17 . |
| MLA | 林蔚青 et al. "时空信息融合的堆芯自给能中子探测器故障检测与隔离方法" . | 中国电机工程学报 44 . 23 (2024) : 9310-9322,中插17 . |
| APA | 林蔚青 , 缪希仁 , 陈静 , 卢燕臻 , 许勇 , 江灏 . 时空信息融合的堆芯自给能中子探测器故障检测与隔离方法 . | 中国电机工程学报 , 2024 , 44 (23) , 9310-9322,中插17 . |
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