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< Page ,Total 39 >
Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation SCIE
期刊论文 | 2025 , 36 (5) | NUCLEAR SCIENCE AND TECHNIQUES
Abstract&Keyword Cite Version(2)

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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Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation EI
期刊论文 | 2025 , 36 (5) | Nuclear Science and Techniques
Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation Scopus
期刊论文 | 2025 , 36 (5) | Nuclear Science and Techniques
Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line SCIE
期刊论文 | 2025 , 42 (4) , 1447-1470 | ENGINEERING COMPUTATIONS
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Abstract :

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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Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line EI
期刊论文 | 2025 , 42 (4) , 1447-1470 | Engineering Computations (Swansea, Wales)
Fusion of cluster analysis and differential particle swarm optimisation for multi-UAV task assignment on power transmission line Scopus
期刊论文 | 2025 , 42 (4) , 1447-1470 | Engineering Computations (Swansea, Wales)
Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices EI
会议论文 | 2025 , 1395 LNEE , 374-385 | 1st Electrical Artificial Intelligence Conference, EAIC 2024
Abstract&Keyword Cite Version(1)

Abstract :

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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Research on a Lightweight YOLOXs-Based Safety Inspection System for Substations on Front-End Devices Scopus
其他 | 2025 , 1395 LNEE , 374-385 | Lecture Notes in Electrical Engineering
Decarbonization and Economic Operation Strategy for New Distribution Systems with Source-Load-Storage Coordinated Planning EI
会议论文 | 2025 , 1397 LNEE , 405-418 | 1st Electrical Artificial Intelligence Conference, EAIC 2024
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Abstract :

Under the guidance of the dual-carbon target,the high-proportion distributed power access power distribution system faces significant challenges in low-carbon and economic operation. Existing source-load-storage coordinated planning in power distribution systems emphasizes economic operation. This article proposes a decarbonization and economic operation strategy method for new power distribution system.Firstly, targeting the operational characteristics of wind-solar-load-storage units in the new power distribution system, individual unit models are constructed. Secondly, a dual-layer decision-making model for energy storage device siting and capacity setting and day-ahead optimized operation is built to reconcile the conflict between decarbonization responsibilities and economic operation in the system. Finally, to address the high-dimensional, multi-constraint optimization problem of economic operation in the new power distribution system, considering carbon trading costs, an improved particle swarm algorithm is used to solve the dual-layer optimization models. The effectiveness of the proposed optimization configuration strategy method in reducing system line losses, enhancing economic and decarbonization operations is validated through IEEE standard node examples. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword :

Particle swarm optimization (PSO) Particle swarm optimization (PSO) Strategic planning Strategic planning

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GB/T 7714 Chen, Xi , Lu, Yanzhen , Tong, Chengxin et al. Decarbonization and Economic Operation Strategy for New Distribution Systems with Source-Load-Storage Coordinated Planning [C] . 2025 : 405-418 .
MLA Chen, Xi et al. "Decarbonization and Economic Operation Strategy for New Distribution Systems with Source-Load-Storage Coordinated Planning" . (2025) : 405-418 .
APA Chen, Xi , Lu, Yanzhen , Tong, Chengxin , Wang, Zhilei , Miao, Xiren , Liu, Yuhan . Decarbonization and Economic Operation Strategy for New Distribution Systems with Source-Load-Storage Coordinated Planning . (2025) : 405-418 .
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Decarbonization and Economic Operation Strategy for New Distribution Systems with Source-Load-Storage Coordinated Planning Scopus
其他 | 2025 , 1397 LNEE , 405-418 | Lecture Notes in Electrical Engineering
Research on Lightweight Substation Instrument Detection Model for Front-End Equipment EI
会议论文 | 2025 , 1395 LNEE , 364-373 | 1st Electrical Artificial Intelligence Conference, EAIC 2024
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Abstract :

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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Research on Lightweight Substation Instrument Detection Model for Front-End Equipment Scopus
其他 | 2025 , 1395 LNEE , 364-373 | Lecture Notes in Electrical Engineering
Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model SCIE
期刊论文 | 2025 , 74 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Abstract&Keyword Cite Version(3)

Abstract :

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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Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model Scopus
期刊论文 | 2025 , 74 | IEEE Transactions on Instrumentation and Measurement
Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model Scopus
期刊论文 | 2025 | IEEE Transactions on Instrumentation and Measurement
Fault Detection for Ex-Core Neutron Detectors in Nuclear Power Plants Using Global-Fused Dynamic Detection Model EI
期刊论文 | 2025 , 74 | IEEE Transactions on Instrumentation and Measurement
SR-STM: Simulation–Reality Spatial–Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants EI
期刊论文 | 2025 , 12 (11) , 17854-17868 | IEEE Internet of Things Journal
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Abstract :

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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SR-STM: Simulation-Reality Spatial-Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants SCIE
期刊论文 | 2025 , 12 (11) , 17854-17868 | IEEE INTERNET OF THINGS JOURNAL
SR-STM: Simulation-Reality Spatial-Temporal Model for Early Warning of Power Tilt in Nuclear Power Plants Scopus
期刊论文 | 2025 , 12 (11) , 17854-17868 | IEEE Internet of Things Journal
SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines EI
期刊论文 | 2025 , 40 (3) , 1743-1753 | IEEE Transactions on Power Delivery
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Abstract :

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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SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines Scopus
期刊论文 | 2025 , 40 (3) , 1743-1753 | IEEE Transactions on Power Delivery
SKAD: A Unified Framework Guided by Structural Knowledge for Anomaly Detection of Dampers in Transmission Lines SCIE
期刊论文 | 2025 , 40 (3) , 1743-1753 | IEEE TRANSACTIONS ON POWER DELIVERY
基于Transformer与单分类支持向量机的窃电时间识别方法
期刊论文 | 2025 , 49 (05) , 2109-2118 | 电网技术
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窃电量的追回是窃电检测的最终目的,准确的窃电时间识别是进行窃电量精确估算的重要依据。然而,现有窃电检测方法侧重于识别窃电行为,对窃电时间缺乏深入分析,亟需研究基于窃电用户自身计量数据的窃电时间识别模型,为窃电量的估算提供依据。针对窃电时间识别问题,提出一种基于Transformer与单分类支持向量机(one-classsupport vector machine,OCSVM)的半监督窃电数据分类方法。首先,对用户负荷数据按日进行切割,将窃电时间识别问题转化为窃电日负荷数据判别问题;然后,使用Transformer作为重构模型学习用户的正常用电模式与规律,以重构出基于用户日负荷数据的重构值;最后,将构造重构误差曲线作为OCSVM的输入,构造正常用电行为的决策边界,进而判别出窃电数据,以实现窃电时间识别。根据南方某省智能电表用户数据进行算例分析,验证了该方法的可行性和有效性,实验结果表明该方法具有较好的灵敏性和鲁棒性。

Keyword :

Transformer模型 Transformer模型 半监督学习 半监督学习 单分类支持向量机 单分类支持向量机 窃电 窃电 窃电时间识别 窃电时间识别

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GB/T 7714 陈静 , 王铭海 , 刘煜寒 et al. 基于Transformer与单分类支持向量机的窃电时间识别方法 [J]. | 电网技术 , 2025 , 49 (05) : 2109-2118 .
MLA 陈静 et al. "基于Transformer与单分类支持向量机的窃电时间识别方法" . | 电网技术 49 . 05 (2025) : 2109-2118 .
APA 陈静 , 王铭海 , 刘煜寒 , 江灏 , 缪希仁 , 林蔚青 et al. 基于Transformer与单分类支持向量机的窃电时间识别方法 . | 电网技术 , 2025 , 49 (05) , 2109-2118 .
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Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation
期刊论文 | 2025 , 36 (5) , 35-49 | 核技术(英文版)
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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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