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Identification algorithm of low-count energy spectra under short-duration measurement based on heterogeneous sample transfer

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nuclear Science and Techniques 2025年第3期36卷 12-26页

作者： Hao-Lin Liu Hai-Bo Ji Jiang-Mei Zhang Jing Lu School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China Department of Automation University of Science and Technology of ChinaHefei 230026China Fundamental Science on Nuclear Wastes and Environment Safety Laboratory Southwest University of Science and TechnologyMianyang 621010China School of Automation and Information Engineering Sichuan University of Science and EngineeringZigong 643000China

In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical *** issues can lead to potential failures in peak-searching-based identification *** address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification *** experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma *** only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain *** proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.

关键词： Radionuclide identification Low-count Gamma energy spectral analysis Heterogeneous Transfer learning

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Research on accurate virtual trajectory length model for TGS transmission measurement

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nuclear Science and Techniques 2025年第3期36卷 178-188页

作者： Rong-Rong Su San-Gang Li Chu-Xiang Zhao Li Yang Ming-Zhe Liu Shan Liao Zhi Zhou Qing-Shan Tan Zhi-Xing Gu Xian-Guo Tuo Yi Cheng College of Nuclear Technology and Automation Engineering Chengdu University of Technology1#DongsanluErxianqiaoChengdu 610059China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province (Chengdu University of Technology)1#DongsanluErxianqiaoChengdu 610059China University of Science and Technology of China Hefei 230026China School of Physics and Electronic Engineering Sichuan University of Science and EngineeringZigong 643000China

To accurately reconstruct the tomographic gamma scanning(TGS)transmission measurement image,this study optimized the transmission reconstruction equation based on the actual situation of TGS transmission *** the transmission reconstruction equation and the Monte Carlo program Geant4,an innovative virtual trajectory length model was *** model integrated the solving process for the trajectory length and detection efficiency within the same *** mitigate the influence of the angular distribution ofγ-rays emitted by the transmitted source at the detector,the transport processes of numerous particles traversing a virtual nuclear waste barrel with a density of zero were ***,a certain amount of information was captured at each step of particle ***,the model addressed the nonuniform detection efficiency of the detector end face by considering whether the energy deposition of particles in the detector equaled their initial *** models were established to validate the accuracy and reliability of the virtual trajectory length *** 1 was a simplified nuclear waste barrel,whereas Model 2 closely resembled the actual structure of a nuclear waste *** results indicated that the proposed virtual trajectory length model significantly enhanced the precision of the trajectory length determination,substantially increasing the quality of the reconstructed *** example,the reconstructed images of Model 2 using the“point-to-point”and average trajectory models revealed a signalto-noise ratio increase of 375.0%and 112.7%,***,the virtual trajectory length model proposed in this study holds paramount significance for the precise reconstruction of transmission ***,it can provide support for the accurate detection of radioactive activity in nuclear waste barrels.

关键词： Tomographic gamma scanning Transmission measurement reconstruction Geant4 Trajectory length model Nonuniform detection efficiency

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Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation

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Neural Computing and Applications 2025年第8期37卷 5891-5911页

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Wang, Xiangxiang Ukot, Akpanika Robert Cai, Jingye School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Medical image segmentation is pivotal in computer-aided diagnosis systems, demanding high precision and contextual understanding. Vision Transformer-based approaches have gained much attention recently due to their excellent performance and ability to capture long-range dependencies in medical images. However, research shows they suffer from inadequate multi-scale feature integration, poor object localization, and inconsistent mask predictions, leading to sub-optimal segmentation performance. This paper addresses these challenges by redefining semantic medical image segmentation through learnable object queries within an enhanced transformer framework with a masked hybrid attention querying mechanism, optimizing multi-scale feature fusion, object localization, and instance-specific segmentation. First, this study presents a novel transformer-based masked hybrid attention mechanism using Laplacian query fusion on learnable query features and incorporating a novel tripartite sequence matching technique as part of the enhanced decoder block to improve the consistency of mask predictions and optimize decoder queries. The designed hybrid multi-head self- and cross-attention mechanisms aim to selectively integrate multi-scale features, ensuring optimal feature combinations for precise segmentation. Secondly, multiple class tokens are incorporated to improve object localization and capture class-specific characteristics within the transformer framework, leveraging the transformer decoders’ ability to learn distinct instance representations. Experimental results and extensive ablation studies demonstrate the effectiveness of the proposed approach on three publicly available datasets, obtaining better segmentation results compared to various state-of-the-art approaches using various evaluation metrics. Specifically, the proposed model achieves a Dice Score of 95.25%, 92.75%, and 85.25% on LUNA, ISIC, and DRIVE datasets, respectively. © The Author(s), under exclusive licence

关键词： Laplace transforms

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Study on Multi-Motor Synchronization Control for nuclear Island Main Equipment Transporter Based on Improved Virtual Line-Shafting Structure 11th

Study on Multi-Motor Synchronization Control for Nuclear Isl...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Sheng, Beining Song, Beibei An, Yanbo Du, Hua Cui, Shumei School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

ISBN: (纸本)9789819788231

The existing drive method of the main equipment transporter of the nuclear island makes the transporter have frequent jamming problems when the transporter going straight and turning, which affects the safety during the transporter of the main equipment. Therefore, it is necessary to design an on-board multi-motor synchronous system with differential synchronization function. Although the traditional virtual line-shafting structure has the ability of same-speed synchronization and differential synchronization, there is a large synchronization error. For this reason, this paper proposes a multi-motor synchronous control method based on the improved virtual line-shafting structure, which can effectively solve the inherent synchronous delay problem of the virtual line-shafting. First, analyze and research the combination method of virtual line-shafting structure and deviation coupling structure, and designs the electronic differential to be able to adapt to the requirements of same-speed and differential synchronization. Then the influence of virtual line-shafting parameters on the system is analyzed using the root locus method, and a simplified parameter model of the virtual line-shafting is established, on the basis of which an improved virtual line-shafting control method with low synchronous delay is proposed. The results show that this method can significantly reduce the synchronization error compared with the traditional virtual line-shafting. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Root loci

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and Applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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Multi-Object Detection and Tracking of Foggy Vehicles Based on iMproved YOLOV5s and Optimized Deepsort

Multi-Object Detection and Tracking of Foggy Vehicles Based ...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiayu Chen Xingwen Dong Yuting Zheng Bo Li School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In view of the problems of ID false detection and missing detection often occur in the multi-target detection and tracking of the vehicle in front of the autonomous vehicle in hazy days, this paper proposes a new de-fog algorithm based on dark channel prior to improve the quality of the input image. CBAM-Net was embedded into the CSPDarknet53 backbone network of YOLOv5s model to improve the feature extraction capability and optimize the detection effect. A new CMTIOU loss algorithm is proposed to replace the IOU in Deepsort, and comprehensively consider the factors of time, feature and location geometry to improve the tracking effect and HOTA index. The experimental results show that the MOTA and HOTA of the proposed algorithm are 77.6% and 78.8% respectively compared with the traditional algorithm.

关键词： YOLO Geometry Accuracy Correlation Force Feature extraction Indexes Convolutional neural networks Autonomous vehicles Meteorology

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Dynamic Programming Based Strategies of Future Crop Plant 24

Dynamic Programming Based Strategies of Future Crop Plant

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Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management

作者： Beining Zhang Xueying Wang Yongkang Deng Kai Chen Qinglang Liu Measurement and Control Technology and Instruments School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China

ISBN: (纸本)9798400711862

In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitutions and complementarities, this paper solves the problem with three sub-problems. First, the optimal cropping plan for 2024-2030 is calculated based on the cropping scenario for 2023. It is assumes that expected sales, cropping costs, yields and marketing prices are stable. Secondly, a multi-objective planning model was developed. It aimed at profitability and stability and took into account uncertainties. Finally, the planting plan was further optimised and it considered crop substitutability and complementarity, and the relationship between expected sales and selling prices and planting costs. Through dynamic planning and Monte Carlo simulation, this paper takes into account uncertainty and crop relationships and it solves the optimal crop planting plan for the years 2024-2030 with a maximum profit of about $7.737 million. Therefore, this paper proposes a decision model based on dynamic programming and it takes into account various factors of the planting plan for the next seven years. An effective method is provided for solving the crop planting strategy problem in sustainable agricultural development.

关键词： Dynamic Programming

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Research on Rural Income Planting Strategies Based on Linear Programming and Monte Carlo Simulation 24

Research on Rural Income Planting Strategies Based on Linear...

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2024 International Conference on Economic Data Analytics and Artificial Intelligence, EDAI 2024

作者： Zhang, Huangnian Ji, Shuai Zhang, Chun Liu, Chang Chen, Li School of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China College of Marine Life Sciences Ocean University of China Shandong Qingdao China School of Rehabilitation Medicine Qilu Medical University Shandong Zibo China

ISBN: (纸本)9798400713255

Utilizing cultivated land resources and choosing appropriate crop planting strategies are of crucial importance for rural economic development. This paper summarizes the production structure of 74,000 villages in Shandong Province. By taking the average values of the selling prices, production costs, and cultivated areas of each crop, it centrally reflects the production characteristics of this province. Based on previous years' planting data, a maximization objective function is established using linear programming + 0-1 programming to analyse three problems under different planting scenarios [1]. In problem one, the planting strategy that maximizes total revenue is sought. The linear programming + 0-1 programming method and three-dimensional arrays are used to discuss the limiting conditions. The 0-1 variable is used to control and prevent yield reduction and solve the difficulties in planting leguminous crops. The optimal planting strategies are obtained respectively under the two circumstances of overproduction leading to unsalable products and a 50% price reduction for sale. In problem two, a dynamic programming model is established through uncertainty analysis. Monte Carlo simulation is used to quantify the planting risk coefficient and obtain the optimal strategy under the influence of uncertain factors. In problem three, considering the substitutability, complementarity, and correlation of crops, by using Euclidean distance to calculate the similarity between various crops, the concepts of reward items and penalty items are introduced, and planning is carried out on the basis of the second problem. In general, this paper introduces new methods and considers multiple factors to make the strategy more practical and provide decision support for rural economic development. © 2024 Copyright held by the owner/author(s).

关键词： Dynamic programming

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Study on dynamic wear evolution of modified gear rack considering the real-time variation of contact characteristics

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Wear 2025年

作者： Zeng, Liyuan Deng, Xingqiao Li, Fan Dong, Chenglong Wang, Shisong Yang, Hongjuan Tang, Chuan Li, Yuhang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu610059 China

Tooth profile modification (TPM) is an essential process in the design of open gear rack, which plays an important role in improving the meshing performance and reducing the tooth surface wear. However, TPM and tooth surface wear cause significant alterations to the tooth profile, which in turn affects the contact characteristics on the tooth surface. This poses a significant challenge for predicting the dynamic wear evolution of modified gear rack. In this paper, a dynamic wear evolution model of modified gear rack considering the real-time variation of contact characteristics is proposed. Firstly, a wear model of gear rack under mixed elastohydrodynamic lubrication (EHL) is established, considering the lubrication state of the tooth surface. By incorporating the effects of TPM and tooth surface wear, a modified time-varying meshing stiffness (TVMS) model and a contact load distribution model are proposed. Based on the proposed model, the dynamic wear evolution of modified gear rack under mixed EHL is accurately predicted, by employing a cyclic iterative method to determining the real-time contact characteristics under the wear degradation of the tooth profile. After validating the effectiveness of the model, the dynamic wear evolution of the gear rack driving mechanism of a rack rail train is investigated, and a quantitative analysis of the effect of TPM parameters on wear depth is conducted. This study holds significant implications for unveiling the wear failure mechanism of modified gear rack and optimizing the design of TPM parameters. © 2025 Elsevier B.V.

关键词： Gear teeth

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Research on Defect Measurement Method for nuclear Components Based on Smooth Optimization Algorithm

Research on Defect Measurement Method for Nuclear Components...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Wang, Yong Xiao, Zongzhen Xing, Jingyi Liu, Yang Wang, Hao Li, Jianjun School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China School of Mechanical Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China China North Nuclear Fuel Co. Ltd. Inner Mongolia Baotou014035 China School of Digital and Intelligent Industry Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China

ISBN: (纸本)9783031734069

As an important component of nuclear reactors, guide tubes have critical applications in the nuclear industry. In order to assist engineers in quickly and accurately performing guide tube defect detection tasks and to construct an automatic defect detection system for nuclear components, this study proposes an automatic detection method that integrates traditional image processing algorithms with U-Net and YOLOv8 deep learning algorithms. This method is aimed at the data collected by metallographic microscopes, using the U-Net network to extract edge coordinate information of the data, and designing a boundary fitting method based on smooth optimization to obtain the boundary equation of the fusion zone. At the same time, the YOLOv8 network is used to detect the coordinate range of weld joints, and the final coordinates of the weld joints are obtained by combining the coordinate of the fusion zone edge. Experimental results show that, while maintaining fitting accuracy, the boundary equation obtained by the smooth optimization algorithm has better continuity. Compared with the traditional method of metallographic inspection combined with manual measurement, this method significantly improves the detection efficiency and accuracy of Effective Melt Depth and Melt Zone Depth of guide tubes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： nuclear industry

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