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学者姓名：缪希仁
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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 nuclear power plants (NPPs), ex-core neutron detectors are deployed around reactor cores and are essential for reactor stability, but their deterioration and malfunction can cause misperceptions and misdiagnoses. Existing fault detection seldom accounts for global spatial-temporal coupling relationships implied among overall detectors and uncertainty under transient operations. Thus, we propose a novel detector-oriented fault detection scheme called the global-fused dynamic detection (GFDD) model, established by the global spatial-temporal graph (GSTG), moving-global graph convolution (MGGC), and uncertainty-quantified dynamic detection (UQDD). To enrich informational sources and disperse faulty propagation, we specifically design the GSTG for characterizing the spatial-temporal relationships among overall detectors and the MGGC for efficiently capturing global high-level features, further generating multidetector reconstructed signals and residuals. Through calculating dynamic statistics and quantifying uncertainty under varying operating conditions, the UQDD identifies faulty detectors and corrects erroneous signals. Experiments on steady and transient states from a real-world NPP with simulated faults validate that the GFDD model outperforms various state-of-the-art methods with regard to signal reconstruction and fault detection.
Keyword ：
Circuit faults Circuit faults Detectors Detectors Ex-core neutron detector Ex-core neutron detector fault detection fault detection Fault detection Fault detection Inductors Inductors Load modeling Load modeling Monitoring Monitoring Neutrons Neutrons nuclear power plant (NPP) nuclear power plant (NPP) Power system dynamics Power system dynamics Sensor phenomena and characterization Sensor phenomena and characterization spatial-temporal model spatial-temporal model Uncertainty Uncertainty uncertainty quantization uncertainty quantization
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Lin, Weiqing et al. "Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Duan, Pengbin , Ye, Mingxin , Xu, Yong et al. Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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PurposeMulti-unmanned aerial vehicle (UAV) missions aim to optimize the execution of multiple missions using limited resources, making it possible to balance the objectives of each mission while minimizing the time to completion.Design/methodology/approachAn algorithm combining cluster analysis and differential evolution particle swarm optimization (DE-PSO) is proposed to solve this problem.FindingsThe investigative study is based on the homogenization of multi-UAV missions in multi-objective task distribution to reduce the total elapsed time.Practical implicationsThis method effectively reduces task time and provides a solution for multi-UAV operations in transmission line cooperation.Originality/valueA novel heuristic algorithm is proposed, and the algorithm fully considers the clustering characteristics under multi-region and the positional relationship characteristics of scene target distribution. It also fully considers the physical characteristics of airport location and UAV power to uniformly optimize the time.
Keyword ：
Collaborative work Collaborative work DE-PSO algorithm DE-PSO algorithm Difference and variation Difference and variation Multi-UAV Multi-UAV
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| GB/T 7714 | Jiang, Hao , Lin, Sicheng , Chen, Jing et al. Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line [J]. | ENGINEERING COMPUTATIONS , 2025 , 42 (4) : 1447-1470 . |
| MLA | Jiang, Hao et al. "Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line" . | ENGINEERING COMPUTATIONS 42 . 4 (2025) : 1447-1470 . |
| APA | Jiang, Hao , Lin, Sicheng , Chen, Jing , Miao, Xiren . Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line . | ENGINEERING COMPUTATIONS , 2025 , 42 (4) , 1447-1470 . |
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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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Neutron detectors in nuclear power plants (NPPs) are critical for system stability, yet their malfunctions may lead to false alerts and misdiagnoses. Multidetectors deployed in diverse positions vary with the nuclear reactor states contained spatial-temporal variations of neutron fluxes. Existing methods seldom concurrently consider intricate spatial-temporal correlations and gradual state variations among detectors. This study proposes a detector-oriented fault detection and isolation method named the spatial-temporal state adaptation model (ST-SAM). The method introduces a local-global spatial-temporal network that captures the potential interdependencies within the detector topology. To minimize cross-state discrepancies in reactors, ST-SAM integrates three submodules: a signal reconstructor to enhance the specific-state variation representation; a correlation alignment to mitigate interstate feature discrepancies; and an adversarial discriminator to extract spatial-temporal state-invariant features. Leveraging the parallel detection strategy, ST-SAM effectively detects and isolates faulty detectors, preventing fault propagation on subsequent diagnosis. Experiments on ex-core and in-core neutron detectors in real-world NPPs with simulated faults verify that the ST-SAM outperforms various state-of-the-art methods in terms of signal reconstruction and fault detection.
Keyword ：
Domain adaptation (DA) Domain adaptation (DA) dynamic threshold dynamic threshold fault detection fault detection graph convolutional network (GCN) graph convolutional network (GCN) neutron detector neutron detector nuclear power plant (NPP) nuclear power plant (NPP)
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (2) : 1110-1119 . |
| MLA | Lin, Weiqing et al. "ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 21 . 2 (2024) : 1110-1119 . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Ye, Mingxin , Zhang, Liping , Xu, Yong et al. ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (2) , 1110-1119 . |
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低压交流系统过电流原理短路保护方法受限于短路检测特征单一,已难以满足复杂工况下系统运行保护,分布式光伏、储能等柔性装置接入给低压保护技术带来新挑战.因此,提出节点自治的含分布式光伏低压交流系统短路快速保护方法.首先,以短路电流、电压瞬时幅值构建支路状态特征,研究短路状态早期辨识方法.其次,引入同节点各支路短路电流极性信息,设计基于短路电流方向相关性辨识的短路支路保护决策机制.最后,进行低压真型系统及其仿真模型的保护实验.实验结果表明,传统支路和光伏并网支路的短路均可在0.5 ms内检出;不同节点间的保护响应范围合理搭配,且对于同一节点的近端短路,各支路可在短路1 ms内独立决断是否触发保护,保障了保护方法的选择性;保护方法在各类源荷运行工况扰动下不误动,研究成果具有较高的理论和工程价值.
Keyword ：
低压交流系统 低压交流系统 保护决策机制 保护决策机制 分布式光伏 分布式光伏 短路故障 短路故障 短路状态早期检测 短路状态早期检测
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| GB/T 7714 | 庄胜斌 , 缪希仁 , 郭谋发 . 含分布式光伏低压交流系统短路快速保护方法 [J]. | 电网技术 , 2024 , 48 (12) : 5138-5148 . |
| MLA | 庄胜斌 et al. "含分布式光伏低压交流系统短路快速保护方法" . | 电网技术 48 . 12 (2024) : 5138-5148 . |
| APA | 庄胜斌 , 缪希仁 , 郭谋发 . 含分布式光伏低压交流系统短路快速保护方法 . | 电网技术 , 2024 , 48 (12) , 5138-5148 . |
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针对高比例分布式光伏接入低压台区所导致的用户相序难以准确辨识的问题,依托于高级量测体系获取的用电数据,提出一种基于最大互信息系数(MIC)的低压光伏台区用户相序辨识方法.根据低压光伏台区的拓扑结构对用电信息的空间特性进行分析,基于用户电压所确定的数学表达式,挖掘用户相序的深层物联信息;结合工程实际,采用时间序列筛选机制,选取用户电压序列和台区配电变压器低压侧三相电流序列,以此构建低压光伏台区用户相序辨识特征;针对传统相关性表征方法的不足,引入MIC对相序辨识特征的三相关联度分别进行度量,根据MIC的数值大小对用户相序进行判别;基于低压光伏台区的实际数据进行算例分析,验证所提方法的有效性与可靠性.
Keyword ：
低压光伏台区 低压光伏台区 最大互信息系数 最大互信息系数 相序辨识 相序辨识 高级量测体系 高级量测体系
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| GB/T 7714 | 郑楷迪 , 缪希仁 , 林雨润 et al. 基于最大互信息系数的低压光伏台区用户相序辨识方法 [J]. | 电力自动化设备 , 2024 , 44 (12) : 108-114 . |
| MLA | 郑楷迪 et al. "基于最大互信息系数的低压光伏台区用户相序辨识方法" . | 电力自动化设备 44 . 12 (2024) : 108-114 . |
| APA | 郑楷迪 , 缪希仁 , 林雨润 , 黄灿水 . 基于最大互信息系数的低压光伏台区用户相序辨识方法 . | 电力自动化设备 , 2024 , 44 (12) , 108-114 . |
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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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