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学者姓名：刘欣宇
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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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Due to the low automation level and low working efficiency of the existing drone spraying technology, it is difficult to meet the needs of large-scale applications and diversified requirements. Therefore, this paper designs a CGSP detection model for insulator detection and an online optimization Kalman tracking algorithm based on the BP neural network, effectively improving detection efficiency and accuracy. An online coating system is established. Real-time analysis and adjustment of coating coverage and spraying uniformity are achieved through image differential analysis, color histogram, and Haralick texture feature algorithm, ensuring that the sprayed blocks meet the standard values. The experimental results show that the system implemented in this study realizes a semi-automatic closed-loop process of insulator target recognition, flight tracking, and online spraying assisted by artificial intelligence (AI). © 2025 Institute of Physics Publishing. All rights reserved.
Keyword ：
Aircraft detection Aircraft detection Electric insulating materials Electric insulating materials Target drones Target drones
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| GB/T 7714 | Lin, Yongxiang , Chen, Wei , Tang, Yichen et al. Research on insulator online spraying system based on AI assistance of UAV front-end [C] . 2025 . |
| MLA | Lin, Yongxiang et al. "Research on insulator online spraying system based on AI assistance of UAV front-end" . (2025) . |
| APA | Lin, Yongxiang , Chen, Wei , Tang, Yichen , Sun, Qiang , Huang, Yusheng , Chen, Jie et al. Research on insulator online spraying system based on AI assistance of UAV front-end . (2025) . |
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Neutron detectors in nuclear power plants (NPPs) are critical for system stability, yet their malfunctions may lead to false alerts and misdiagnoses. Multidetectors deployed in diverse positions vary with the nuclear reactor states contained spatial-temporal variations of neutron fluxes. Existing methods seldom concurrently consider intricate spatial-temporal correlations and gradual state variations among detectors. This study proposes a detector-oriented fault detection and isolation method named the spatial-temporal state adaptation model (ST-SAM). The method introduces a local-global spatial-temporal network that captures the potential interdependencies within the detector topology. To minimize cross-state discrepancies in reactors, ST-SAM integrates three submodules: a signal reconstructor to enhance the specific-state variation representation; a correlation alignment to mitigate interstate feature discrepancies; and an adversarial discriminator to extract spatial-temporal state-invariant features. Leveraging the parallel detection strategy, ST-SAM effectively detects and isolates faulty detectors, preventing fault propagation on subsequent diagnosis. Experiments on ex-core and in-core neutron detectors in real-world NPPs with simulated faults verify that the ST-SAM outperforms various state-of-the-art methods in terms of signal reconstruction and fault detection.
Keyword ：
Domain adaptation (DA) Domain adaptation (DA) dynamic threshold dynamic threshold fault detection fault detection graph convolutional network (GCN) graph convolutional network (GCN) neutron detector neutron detector nuclear power plant (NPP) nuclear power plant (NPP)
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| GB/T 7714 | Lin, Weiqing , Miao, Xiren , Chen, Jing et al. ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (2) : 1110-1119 . |
| MLA | Lin, Weiqing et al. "ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 21 . 2 (2024) : 1110-1119 . |
| APA | Lin, Weiqing , Miao, Xiren , Chen, Jing , Ye, Mingxin , Zhang, Liping , Xu, Yong et al. ST-SAM: Spatial-Temporal State Adaptation Model for Neutron Detector Fault Detection and Isolation in Nuclear Power Plants . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (2) , 1110-1119 . |
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