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学者姓名：江灏
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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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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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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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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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[目的]针对现有的坐姿监测方法存在的接触式、隐私性低、成本高、部署不方便等问题对坐姿监测方法进行研究.[方法]提出基于Wi-Fi信道状态信息的坐姿监测方法.该方法在不同坐姿下采集商用路由器的Wi-Fi信道状态信息，结合卷积神经网络和长短期记忆神经网络建立坐姿分类模型，融合采样窗口内信道状态信息的幅值和相位数据，并充分提取数据的空间和时间特征，提高坐姿分类精度.在对原始相位数据进行预处理时，提出了近邻子载波差值阈值补偿方法，有效地解决了不同子载波的相位旋绕不同步的问题.[结果]搭建坐姿监测环境，对办公或学习场景下的5种常见坐姿进行分类.实验证明，该坐姿监测方法对坐姿分类有较高的准确率，对所有坐姿分类的平均准确率达到91.23%.[结论]本文提出的基于Wi-Fi信道状态信息的坐姿监测方法，具有非接触式、隐私性高、成本低、部署方便等特点，且对坐姿分类准确率高，在坐姿监测系统的研究上具有一定的实用价值.
Keyword ：
CNN-LSTM CNN-LSTM CSI CSI Wi-Fi感知 Wi-Fi感知 坐姿监测 坐姿监测
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| GB/T 7714 | 刘暾东 , 黄智斌 , 江灏 . 基于Wi-Fi信道状态信息的坐姿监测方法 [J]. | 厦门大学学报(自然科学版) , 2024 , 63 (04) : 649-658 . |
| MLA | 刘暾东 et al. "基于Wi-Fi信道状态信息的坐姿监测方法" . | 厦门大学学报(自然科学版) 63 . 04 (2024) : 649-658 . |
| APA | 刘暾东 , 黄智斌 , 江灏 . 基于Wi-Fi信道状态信息的坐姿监测方法 . | 厦门大学学报(自然科学版) , 2024 , 63 (04) , 649-658 . |
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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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