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学者姓名：陈静
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Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry.Owing to economic and secu-rity 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 adver-sarial 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.
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| GB/T 7714 | Wei-Qing Lin , Xi-Ren Miao , Jing Chen et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation [J]. | 核技术（英文版） , 2025 , 36 (5) : 35-49 . |
| MLA | Wei-Qing Lin et al. "Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation" . | 核技术（英文版） 36 . 5 (2025) : 35-49 . |
| APA | Wei-Qing Lin , Xi-Ren Miao , Jing Chen , Ming-Xin Ye , Yong Xu , Hao Jiang et al. Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation . | 核技术（英文版） , 2025 , 36 (5) , 35-49 . |
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The substation plays an essential role in the security and reliability of the power supply of the grid as it serves as the energy conversion hub of the entire power system. To address the challenges posed by complex on-site environments, low detection accuracy, and excessive resource consumption in existing deep learning models, we propose a lightweight substation safety inspection system designed for front-end devices. The system consists of software and hardware modules. The software module utilizes weighted bidirectional feature pyramid network, attention mechanism, and pruning-quantization-distillation operations to improve and lightweight the YOLOXs (you only look once version-xs) model, effectively compressing the model size while maintaining accuracy. The hardware module mainly achieves quantization compilation and hardware acceleration of the lightweight YOLOXs detection model on the FPGA frontend device, enabling low-latency, high-precision real-time detection for on-site operations at substations. In Experiment, the improved YOLOXs model shows an average detection accuracy increase of 2.71% compared to the original model, with a reduction in model size of 86.9% after light weighting. The FPGA front-end device achieves a single-image detection time of 87.33 ms, which satisfies the practical engineering requirements for substation safety inspection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Distillation equipment Distillation equipment Electric power system security Electric power system security Electric power transmission networks Electric power transmission networks Electric substations Electric substations Error correction Error correction Inspection equipment Inspection equipment Requirements engineering Requirements engineering Safety testing Safety testing
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| GB/T 7714 | He, Hao , Jiang, Hao , Liu, Jiawei et al. Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices [C] . 2025 : 374-385 . |
| MLA | He, Hao et al. "Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices" . (2025) : 374-385 . |
| APA | He, Hao , Jiang, Hao , Liu, Jiawei , Chen, Jing , Miao, Xiren , Liu, Xinyu et al. Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices . (2025) : 374-385 . |
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Aiming at the problem that the current deep learning network model for substation meter detection has too many parameters and is difficult to be deployed in mobile devices and embedded devices with limited computing resources, we propose a lightweight substation meter detection algorithm with improved YOLOv5. Based on the YOLOv5 network, the improved algorithm introduces the SE fusion attention mechanism module, and adaptively learns the relationship between feature channels to improve the model’s ability to extract important features from the instrument. Meanwhile, TensorRT technology is used to reconstruct and optimize the improved model, which can reduce the number of model parameters, improve the detection speed and ensure the accuracy of the model detection. Experimental results demonstrate that compared with YOLOv5 on the embedded device Jetson Nano, the improved algorithm proposed in this paper presents significant advantages, which increase by 1.5% and 2.3% respectively on mAP@.5 and mAP@.5:.95, and the detection frame per second increases by 130%, reaching 23FPS. It can realize real-time instrument detection in substation scene, and has practical application significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Electric substations Electric substations Instrument testing Instrument testing
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| GB/T 7714 | Liu, Xian , Jiang, Hao , Zhang, Minggui et al. Research on Lightweight Substation Instrument Detection Model for Front-End Equipment [C] . 2025 : 364-373 . |
| MLA | Liu, Xian et al. "Research on Lightweight Substation Instrument Detection Model for Front-End Equipment" . (2025) : 364-373 . |
| APA | Liu, Xian , Jiang, Hao , Zhang, Minggui , Miao, Xiren , Liu, Xinyu , Chen, Jing . Research on Lightweight Substation Instrument Detection Model for Front-End Equipment . (2025) : 364-373 . |
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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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The dampers absorb transmission line vibration energy, reducing the vibration amplitudes of conductors. However, dampers may develop internal structural anomalies (e.g., damage to damper heads) or external positional anomalies (e.g., slippage along the conductor), both of which compromise vibration suppression efficacy. Existing anomaly detection methods focus on single anomaly type and struggle with local feature extraction. To address these limitations, this paper introduces SKAD, a unified framework guided by structural knowledge, to concurrently detect internal and external damper anomalies. SKAD encodes structural properties of dampers through four key structural points, enabling sub-pixel-level localization via a hybrid network (HRNet + GAU + SimCC). By analyzing spatial relationships and vector features of these structural points, SKAD can simultaneously detect anomalies like damage (via confidence thresholds and vector dot products) and slippage (via depth-parallelism-distance constraints) at the structural level. Experiments on a real-world dataset demonstrate SKAD outperforms object-based methods in accuracy and robustness, providing novel transmission line inspection perspectives, ensuring early anomaly detection to prevent conductor fatigue and power outages. © 2025 IEEE.
Keyword ：
Fracture mechanics Fracture mechanics Health risks Health risks
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| GB/T 7714 | Shi, Jiahao , Chen, Jing , Jiang, Hao et al. SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines [J]. | IEEE Transactions on Power Delivery , 2025 , 40 (3) : 1743-1753 . |
| MLA | Shi, Jiahao et al. "SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines" . | IEEE Transactions on Power Delivery 40 . 3 (2025) : 1743-1753 . |
| APA | Shi, Jiahao , Chen, Jing , Jiang, Hao , Miao, Xiren , Yang, Lin . SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines . | IEEE Transactions on Power Delivery , 2025 , 40 (3) , 1743-1753 . |
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The axial power deviation of a reactor can reflect the axial power distribution of the core and the operation of the reactor. Aiming at the difficulties in predicting the axial power deviation under variable operating conditions, this paper proposes a prediction method of reactor axial power deviation based on the combined feature selection and temporal convolutional network (TCN). Taking the basic principle of axial power deviation control as the starting point, this paper analyzes the factors affecting the change of axial power deviation, comprehensively analyzes the redundancy and correlation among multi-dimensional features, uses the combined feature selection strategy to form the optimal feature subset for axial power deviation prediction, constructs the key correlation feature data for axial power deviation prediction, and inputs it into TCN to capture dynamic causality, so as to achieve the prediction of reactor axial power deviation. Experimental studies show that the proposed method can deeply explore the temporal causal change characteristics of the parameters related to the axial power deviation of the reactor, accurately predict the development trend of the axial power deviation, solve the problem that the traditional prediction model does not predict and track in time under complex operating conditions, and provide an auxiliary reference basis for the reactor status monitoring and safe operation of nuclear power plants. © 2025 Atomic Energy Press. All rights reserved.
Keyword ：
Nuclear energy Nuclear energy Nuclear power plants Nuclear power plants Prediction models Prediction models Reactor operation Reactor operation
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| GB/T 7714 | Chen, Jing , Chen, Yan , Jiang, Hao et al. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network [J]. | Nuclear Power Engineering , 2025 , 46 (2) : 239-247 . |
| MLA | Chen, Jing et al. "Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network" . | Nuclear Power Engineering 46 . 2 (2025) : 239-247 . |
| APA | Chen, Jing , Chen, Yan , Jiang, Hao , Duan, Pengbin , Lin, Weiqing , Qiu, Xinghua et al. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network . | Nuclear Power Engineering , 2025 , 46 (2) , 239-247 . |
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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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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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Balanced in-core power levels in nuclear power plants (NPPs) are critical for safety, whereas power tilt disrupts this balance, reducing safety margins and posing risks. Early warning for power tilt offers an effective way of optimizing monitoring. Due to abnormal-sample scarcity and security concerns, common data-driven models train on the simulated data generated by simulators. However, achieving a satisfactory effect in practices is difficult because simulators imperfectly emulate reality. Thus, we propose a power tilt-oriented early warning method called simulation–reality spatial–temporal model (SR-STM). Motivated by the physical model in NPPs, a knowledge-guided hierarchical graph is designed to characterize spatial correlations among local power levels for SR-STM’s input. The SR-STM uses a lightweight spatial–temporal network (LST-Net) as a feature extractor, balancing precision, and efficiency. To bridge sim-real interdomain discrepancies, SR-STM utilizes node-alignment adversarial learning (NAAL) for fine weight tuning in subdomain, and eigenvalue-based scale alignment (ESA) for sim-real feature proximity. Forecasting local power levels using the SR-STM, dynamic metrics and alarm limits are calculated and compared to perform the early warning task. The online experimental prototype verifies that SR-STM surpasses various state-of-the-art methods in terms of early warning and sim-real cross-domain tasks. © 2014 IEEE.
Keyword ：
Core disruptive accidents Core disruptive accidents Digital elevation model Digital elevation model Eigenvalues and eigenfunctions Eigenvalues and eigenfunctions Nuclear energy Nuclear energy Nuclear power plants Nuclear power plants
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. SR-STM: Simulation–Reality Spatial–Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants [J]. | IEEE Internet of Things Journal , 2025 , 12 (11) : 17854-17868 . |
| MLA | Lin, Weiqing et al. "SR-STM: Simulation–Reality Spatial–Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants" . | IEEE Internet of Things Journal 12 . 11 (2025) : 17854-17868 . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Duan, Pengbin , Ye, Mingxin , Xu, Yong et al. SR-STM: Simulation–Reality Spatial–Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants . | IEEE Internet of Things Journal , 2025 , 12 (11) , 17854-17868 . |
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As nuclear power plants (NPPs) undertake more peak regulation tasks to handle high new energy penetration and overcapacity, precise forecasting of in-core power distributions is essential for optimal control and safe operation. However, current works lack an effective strategy for predicting high-resolution power distributions and neglect in-core spatial correlations. This study proposes a spatial-temporal hierarchical-directed network (ST-HDN) for forecasting power distributions, whose prediction strategy is guided by the physical model. To characterize spatial correlations and causal relationships among physical quantities, the hierarchical-directed graph is designed and combined with neutron and power signals for input to the ST-HDN. Concretely, the ST-HDN integrates three sub-modules: a temporal-differencing layer to enhance representation of subtle variations; a multi-dilated convolutional network to extract dynamic temporal features; and a graph convolutional network to propagate spatial adjacent information, further predicting power nodes at various positions. The predicted power nodes are post-processed to derive future power distributions. Experiments on two peak regulation scenarios from a real-world NPP illustrate that the ST-HDN outperforms various state-of-the-art methods in 10-, 20-, and 30-min ahead forecasting.
Keyword ：
Forecast power distributions Forecast power distributions Graph convolutional network (GCN) Graph convolutional network (GCN) Nuclear power plants (NPPs) Nuclear power plants (NPPs) Physical model Physical model Spatial-temporal model Spatial-temporal model
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. Forecasting in-core power distributions in nuclear power plants via a spatial-temporal hierarchical-directed network [J]. | PROGRESS IN NUCLEAR ENERGY , 2025 , 186 . |
| MLA | Lin, Weiqing et al. "Forecasting in-core power distributions in nuclear power plants via a spatial-temporal hierarchical-directed network" . | PROGRESS IN NUCLEAR ENERGY 186 (2025) . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Duan, Pengbin , Ye, Mingxin , Xu, Yong et al. Forecasting in-core power distributions in nuclear power plants via a spatial-temporal hierarchical-directed network . | PROGRESS IN NUCLEAR ENERGY , 2025 , 186 . |
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