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Research on accurate virtual trajectory length model for TGS transmission measurement

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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A nuclide identification method ofγspectrum and model building based on the transformer

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nuclear Science and Techniques 2025年第1期36卷 80-93页

作者： Fei Li Chu-Yang Luo Ying-Zi Wen Sheng Lv Feng Cheng Guo-Qiang Zeng Jian-Feng Jiang Bing-Hai Li College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610000China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu 610000China Department of Physics Universitat Politècnica de CatalunyaBarcelona 08028Spain Airborne Survey and Remote Sensing Center of Nuclear Industry Shijiazhuang 050000China Hebei Key Laboratory of Airborne Detection and Remote Sensing Technology Shijiazhuang 050000China

In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical *** is difficult to make progress in terms of training data requirements and prediction *** contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent ***,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first ***,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and *** on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model *** experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,*** indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitativ

关键词： Nuclide identification Neural network Transformer

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Power Allocation Optimization of Hybrid Energy Storage System Based on AOA-VMD 11th

Power Allocation Optimization of Hybrid Energy Storage Syste...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Liu, Wei Zhao, Wenhao Feng, Chengwu Jing, Hailian Fang, Xiao Liu, Yingcan Han, Yuecheng College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

ISBN: (纸本)9789819787791

With the construction and grid integration of large-scale photovoltaic power generation systems, utilizing energy storage technology to reduce grid-connected power fluctuations and enhance grid stability has become a research hotspot. This paper, based on a hybrid energy storage system composed of flywheels and lithium-ion batteries, analyzes the measured photovoltaic output power, establishes a hybrid energy storage system model to smooth the fluctuation rate of photovoltaic power generation. Addressing the power allocation issue of the hybrid energy storage system, an optimization algorithm (Arithmetic Optimization Algorithm, AOA) combined with Variational Mode Decomposition (VMD) is employed to solve the model. The simulation results demonstrate that the AOA-VMD algorithm reduces the rated power of both lithium batteries and flywheels. This validates its superiority in smoothing photovoltaic output fluctuations and addressing the mode mixing problem of non-stationary signals, as well as its effectiveness in optimizing power allocation in hybrid energy storage systems. It enhances the efficiency and operational reliability of hybrid energy storage systems, thus advancing the large-scale application of renewable energy sources. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Hybrid power

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Enhancing traffic light recognition in autonomous vehicles: A novel convolutional neural network approach for fog removal using efficient channel attention 4

Enhancing traffic light recognition in autonomous vehicles: ...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Ji, Shuai Zhang, Huangnian Dong, Xingwen The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Adverse environmental conditions such as haze, rain, and dust can significantly impair visibility for vehicles in motion, leading to blurred vision. Traditional de-fogging techniques often encounter limitations, including reliance on pre-existing assumptions, color distortion, and inadequate real-time performance. Consequently, these methods may fail to effectively distinguish traffic light images post-de-fogging, thereby hindering target recognition in autonomous driving systems. This study introduces a convolutional neural network that integrates deep and shallow feature fusion utilizing the Efficient Channel Attention mechanism. By employing an atmospheric scattering model, shallow features from fogged images are extracted through convolutional layer operations, while deep features are obtained using parallel multi-scale convolution kernels. The integration of shallow and deep features is achieved through a series of fusion steps, enhanced by the application of an effective channel attention mechanism. Ultimately, a transmittance map corresponding to the fog map is derived through nonlinear regression, facilitating the recovery of fog-free images and improving the recognition capabilities of the YOLOv8 model. Experimental results demonstrate that this approach effectively identifies traffic light targets during simulations of vehicles navigating traffic intersections. Furthermore, the quality of fog removal achieved by this method surpasses that of other algorithms, as evidenced by both subjective evaluations and objective analyses. © 2025 SPIE.

关键词： Convolutional neural networks

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 206-217页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common *** from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific ***,we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement *** exploit collective milling structures of natural herds to cage the targets,and adopt a switching control policy inspired by sperms’chiral dynamics to optimize the trajectories of individuals,through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical,adaptive and scalable *** exists a critical swarm size,beyond which the excessive competition between agents would generate large mechanical forces,leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple *** work provides physical insights into distributed active swarms and could offer a multilevel,decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices,machine immunity,and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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SR-STM: Simulation-Reality Spatial-Temporal Model for Early Warning of Power Tilt in nuclear Power Plants

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IEEE Internet of Things Journal 2025年第11期12卷 17854-17868页

作者： Lin, Weiqing Miao, Xiren Chen, Jing Duan, Pengbin Ye, Mingxin Xu, Yong Liu, Xinyu Jiang, Hao Fuzhou University College of Electrical Engineering and Automation Fuzhou350108 China China Nuclear Power Technology Research Institute Company Limited Shenzhen518000 China China National Nuclear Power Operation Maintenance Technology Company Limited Hangzhou311200 China

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.0Early 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).0Motivated 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 inter-domain discrepancies, SR-STM utilizes node-alignment adversarial learning for fine weight tuning in subdomain, and eigenvalue-based scale alignment 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. © 2025 IEEE.

关键词： Eigenvalues and eigenfunctions

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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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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Explainable reinforcement learning for enhancing personal thermal comfort and optimizing demand response in household multi-zone HVAC system

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Science China Technological Sciences 2025年第6期68卷 1-17页

作者： Su, Yongxin Xu, Mengyao Liu, Xiao Tan, Mao Wang, Rui Yang, Chunhua School of Automation and Electronic Information Xiangtan University Xiangtan411105 China Hunan National Center for Applied Mathematics Xiangtan University Xiangtan411105 China College of Systems Engineering National University of Defense Technology Changsha410073 China School of Automation Central South University Changsha410083 China

Heating, ventilation and air conditioning (HVAC) systems maintain personal thermal comfort (PTC) and serve as key demand response (DR) resources. Given the challenges of uncertain environments, varied user preferences, and legal requirements for transparent control strategies, this paper proposes an explainable reinforcement learning (XRL) solution for household multi-zone HVAC systems. This approach aims to optimize energy costs, ensure users’ PTC, and maintain the explainability of the DR strategy under uncertainty. Firstly, an XRL-based optimization framework is proposed. The framework utilizes XRL’s online learning capabilities to handle uncertainties and meet the PTC requirements of different zones while maintaining the explainability of the optimization strategy. Then, we propose an explainable proximal policy optimization (XPPO) algorithm as an instantiation of XRL for optimizing household multi-zone HVAC systems, using interpretable continuous control trees as actor networks of the XPPO. Moreover, we design state space, action space, reward, learning network, and learning algorithm of the XPPO in detail for the needs of HVAC operational optimization. Simulation results show that our method is explainable and thus can fulfill the requirements of the laws. At the same time, the proposed method consumes 22.4% less energy cost compared to scenarios without DR. Furthermore, the optimization of DR for a typical 4-zone household can be achieved within 15 min on an i7-12700 CPU. © Science China Press 2025.

关键词： Reinforcement learning

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STSR: An Efficient Model for Super-Resolution Reconstruction

STSR: An Efficient Model for Super-Resolution Reconstruction

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

作者： Xinhui Liu College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

STSR is an efficient and lightweight super-resolution model built upon the Swin Transformer architecture. By utilizing the hierarchical window-based self-attention mechanism of the Swin Transformer, STSR effectively captures global contextual information while ensuring high computational efficiency. To further enhance its performance and reduce model complexity, we incorporate lightweight design elements and residual structures. These improvements not only facilitate better gradient flow but also significantly boost the recovery of high-frequency details. Experimental results on the DIV2K dataset show that STSR delivers exceptional performance, significantly reducing both parameter count and computational costs.

关键词： Computational modeling Superresolution Computer architecture Transformers Computational efficiency Complexity theory Image reconstruction Consumer electronics

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