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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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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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To meet the application requirements of flexible power flow control in local power systems across voltage levels, the PFCT circuit topology and its power flow control strategy are studied. A PFCT circuit topology and its equivalent model are proposed. The mathematical modeling for the series and parallel components of the PFCT are established, along with corresponding control strategies. Flexible control of power flow transmission along the line is accomplished by cross-decoupling power flow control and feedforward decoupling control. A simulation model was built by using the MATLAB/Simulink platform to analyze and validate the PFCT's control of power flow transmission across voltage levels in a typical local power system. A prototype was constructed to validate further the correctness and feasibility of the proposed mathematical models and control strategies. © 2025 Institute of Physics Publishing. All rights reserved.
Keyword ：
Electric power system control Electric power system control Electric power transmission Electric power transmission Mathematical morphology Mathematical morphology Topology Topology
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| GB/T 7714 | Jiang, Hao , Yi, Yang , Zhu, Lin et al. Power flow control of local power system with power flow controllable transformer [C] . 2025 . |
| MLA | Jiang, Hao et al. "Power flow control of local power system with power flow controllable transformer" . (2025) . |
| APA | Jiang, Hao , Yi, Yang , Zhu, Lin , Yao, Zhiwei . Power flow control of local power system with power flow controllable transformer . (2025) . |
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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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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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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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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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. 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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