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Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network 认领 引用 被引量：12
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作者 Hao-Ran Liu Yu-Xin Cheng +2 位作者 Zhuo Zuo Tian-Tian Sun Kai-Min Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2021年第8期48-56,共9页
Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamm... Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal. 展开更多
关键词 Pulse-coupled neural network Charge comparison Back-propagation neural network Fractal spectrum n-γdiscrimination
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Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing 认领 引用 被引量：1
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作者 Sathish Rao Udupi Gururaj Bolar +1 位作者 Manjunath Shettar Ashwini Bhat 《Computers, Materials & Continua》 SCIE EI 2026年第6期903-918,共16页
Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear duri... Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear during drilling.Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy.This research investigates the impact of spindle speed,feed rate,and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel(HSS)twist drills.A full-factorial design with 81 experiments is used to create a comprehensive dataset.ANOVA indicates that spindle speed is the dominant factor affecting wear changes,accounting for 74.43%,followed by feed rate(15.80%)and drill diameter(6.16%).A linear regression model demonstrates reasonable statistical sufficiency(R2=0.964),but it falls short in reflecting nonlinear interactions.Hence,an artificial neural network(ANN)model is developed to improve prediction.The multilayer feed-forward ANN with a 3-10-6-1 architecture,trained using the Levenberg-Marquardt optimization algorithm,achieves excellent predictive accuracy,with high correlation and low root-mean-square error.Model validation was achieved through independent confirmation experiments,yielding a mean absolute percentage error of only 2.27%,with all predictions falling within the permissible wear range.The findings indicate that ANN-based modeling provides a reliable framework for capturing the complex nonlinear relationships governing tool wear in GFRPC drilling and serves as a viable soft sensor for tool condition monitoring,process optimization,and sustainable,data-driven manufacturing. 展开更多
关键词 Glass fiber-reinforced polymer composites machining and drilling drill flank wear prediction artificial neural networks(ANN) soft computational modeling
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network(PINN)Surrogate Model 认领 引用
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作者 An Xu Zhixiong Liu +4 位作者 Hua Rong Liang Han Wei Shi Jun Huang Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期279-313,共35页
In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key struc... In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses(e.g.,top displacement),but usually fail to achieve accurate internal force predictions with conventional training data volumes.As a result,most existing studies involving surrogate models did not concern internal force constraints.To address this issue,this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network(PINN)surrogate model.By embedding static equilibrium equation into the loss function,the model achieves higher predictive accuracy,particularly for internal forces,while pre-training accelerates convergence and enhances stability.Combined with an improved multi-swarm particle swarm optimization(MPSO)algorithm,the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints.The application to a six-story frame structure validates its effectiveness:compared with a DNN-based model,the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937.These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures. 展开更多
关键词 Structural optimization intelligent algorithms physics-informed neural networks(PINN) neural networks
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Neural network control for mitigating actuator delay in ATR engines using predictive compensation and stability reward 认领 引用
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作者 Weidong CAI Wei ZHAO +3 位作者 Xiaorong XIANG Sanqun REN Xuesen YANG Qingjun ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期301-327,共27页
The flight envelope of Air Turbo Rocket(ATR)engines is broader compared to conventional aero-engines,and designing a full-envelope controller using traditional methods poses significant challenges due to a burdensome ... The flight envelope of Air Turbo Rocket(ATR)engines is broader compared to conventional aero-engines,and designing a full-envelope controller using traditional methods poses significant challenges due to a burdensome design process.To address this issue,this paper proposes a self-learning neural network controller design method based on Reinforcement Learning(RL).Additionally,a method for predictive compensation and stability rewards is proposed to reduce the system oscillation caused by actuator delay.This approach simplifies the actuator to a firstorder inertial element exhibiting pure delay.A simulation environment for the ATR engineactuator system is first established.Based on this environment,a self-learning neural network controller using a predictive compensator and the Proximal Policy Optimization(PPO)algorithm is then developed.Furthermore,the temporal difference signals from the controller output are integrated into the reward function to enhance system stability.The proposed method is validated through numerical simulations and semi-physical experiments.The numerical simulation results demonstrate that the proposed method increases the system's tolerance to delays from 20 ms to 400 ms.Under an actuator delay of 400 ms,the average steady-state error remains less than0.1%,the overshoot is limited to 1%,and the settling time does not exceed 3 s.Moreover,compared to the traditional method,the proposed method exhibits higher adaptability to model errors and variations in flight conditions.In the conducted semi-physical simulation experiments,the proposed method achieves stable control of a real electric pump. 展开更多
关键词 Actuator delay ATR engines Neural network control Reinforcement learning Semi-physical simulation
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Physics-informed neural network for material identification via distortion-robust polychromatic x-ray attenuation correction in photon-counting detectors 认领 引用
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作者 Xin Yan Jie Zhang +5 位作者 Kai He Yiheng Liu Yuetong Zhao Gang Wang Xinlong Chang Youwei Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期396-405,共10页
Spectral distortions in photon-counting detectors(PCDs)fundamentally limit the quantitative accuracy of material identification.While machine learning is used for compensation,current data-driven methods often lack ph... Spectral distortions in photon-counting detectors(PCDs)fundamentally limit the quantitative accuracy of material identification.While machine learning is used for compensation,current data-driven methods often lack physical constraints,limiting their interpretability and reliability across varying conditions.To address this issue,we propose a physics-informed neural network(PINN)framework that explicitly embeds the Beer-Lambert law into the learning architecture.By integrating an explicit differential layer to extract high-order curvature features from distorted spectra,the model enables direct inference of the effective atomic number and areal density.This approach effectively leverages the Z-dependent non-linear profile of the photoelectric effect,even when explicit absorption edges are outside the primary detection window.Simulation results establish a high-precision benchmark for Zeffestimation in the target low-Z range(613),with an RMSE of 0.2111.Experimental validation on a CdZnTe-PCD further demonstrates that this accuracy improvement is preserved under realistic pulse pile-up and noise conditions,achieving an RMSE of 0.2457 and an R2of 0.9670.Compared with conventional physical correction methods(typically±0.5 error margin),the proposed framework provides improved precision,with 92.86%of Zeffestimation errors falling within±0.4,corresponding to an approximately 20%tighter error bound.These results confirm that the proposed framework effectively mitigates spectral distortion,providing a robust,calibration-free solution for precise material identification of low-Z materials in industrial non-destructive testing. 展开更多
关键词 physics-informed neural networks (PINNs) photon-counting detectors (PCDs) material identification x-ray imaging
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Physics-informed neural network model for predicting projectile mass erosion integrating cutting and thermal melting mechanisms 认领 引用
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作者 Hengwei Xu Yonggang Lu +3 位作者 Lingmei Guo Junrun Li Xiaowei Feng Zhengcao Lu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期310-324,共15页
High-velocity penetration of projectiles into concrete induces intense thermo mechanical interactions that lead to substantial projectile mass erosion,thereby compromising structural integrity and ballistic stability.... High-velocity penetration of projectiles into concrete induces intense thermo mechanical interactions that lead to substantial projectile mass erosion,thereby compromising structural integrity and ballistic stability.Accurate mass erosion prediction is therefore critical for warhead lethality assessment.To address this challenge,we develop a physics-informed neural network(PINN)model for predicting projectile mass erosion at velocities ranging from 345 m/s to 1852 m/s.The model integrates physicsbased constraints from cutting and thermal melting mechanisms through a composite loss function.Data augmentation and dimensionality reduction techniques are applied to improve model generalization across diverse impact conditions.Training and validation are conducted on an augmented dataset comprising 283 samples,derived from 10 published studies.The model's generalization capability is rigorously evaluated on an independent set of 13 new penetration experiments with ogivenosed projectiles made of three distinct materials.Compared to a purely data-driven neural network(NN)model,the PINN model reduces the average relative error on the validation set from 16.3%to 14.5%and improves the proportion of predictions within a 20%relative error margin from 67.3%to 71.2%.On an independent test set,the PINN model demonstrates superior accuracy over both the data-driven neural network model and conventional theoretical models,achieving an average relative error of11.7%,with 38.5%and 84.6%of the predictions falling within 10%and 20%relative error margins,respectively.By enabling accurate mass erosion prediction under high-velocity conditions,the proposed methodology supports weight-optimized penetrator design and directly contributes to terminal effectiveness improvement. 展开更多
关键词 High-velocity penetration Projectile mass erosion Machine learning Physics-informed neural network
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A visually meaningful medical image encryption scheme based on image steganography and memristive Hopfield neural networks 认领 引用
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作者 Wei Yao Xiangyun Huang +2 位作者 Jianhua Xiao Fei Yu Yichuang Sun 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期368-383,共16页
With the advancement of telemedicine technology,the security of digital medical images has become increasingly important.To address this issue,this paper proposes a visually meaningful color medical image encryption a... With the advancement of telemedicine technology,the security of digital medical images has become increasingly important.To address this issue,this paper proposes a visually meaningful color medical image encryption algorithm.First,a high-dimensional chaotic sequence is generated using a memristive Hopfield neural network.Subsequently,multichannel pixel permutation is performed based on a chaos-driven pseudo-random strategy,followed by the implementation of a double-layer diffusion mechanism integrating cellular automata and dynamic deoxyribonucleic acid(DNA)coding.Finally,a chaos-driven cross-channel least significant bit(LSB)embedding approach is adopted.Simulation experiments and security analyses demonstrate that the proposed algorithm achieves excellent encryption performance,a large key space,and strong robustness against noise and data-loss attacks,thereby effectively ensuring the secure transmission of digital medical images. 展开更多
关键词 Hopfield neural network memristor chaotic sequence image encryption algorithm
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Joint physics and model-guided pre-stack seismic inversion using double dual neural network 认领 引用
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作者 Jian Zhang Hui Sun +2 位作者 Xing-Guo Huang Li Han Yan-Song Li 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期157-177,共21页
Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enha... Pre-stack seismic inversion is used to calculate elastic parameters,including P-wave and S-wave velocities,as well as densities.These parameters play an integral role in the characterization of reservoirs,thereby enhancing the exploration and production process.Deep learning-based seismic inversion does not need a known physical system and can give satisfactory results with sufficient training data.The acquisition of such datasets for seismic inversion poses a significant challenge due to the exorbitant costs associated with drilling activities.Integrating domain knowledge,physical systems,and well log data into a deep learning-based seismic inversion framework is crucial for improving its efficiency and effectiveness.Nevertheless,existing data-driven approaches do not adequately exploit such information,thereby constraining their overall performance and applicability.Therefore,we develop a double dual neural network structure built upon the closed-loop neural network framework,which incorporates both physics and model information to mitigate the dependency on extensive labeled datasets.The information from the different domains is linked through a loss function,where one dual network is responsible for constraining the inversion results using physics information to ensure the physics consistency of the predictions,and the other dual network is responsible for constraining the inversion results using a priori model information to enhance the reliability of the predictions.The method makes full use of well-log data for network training when wells are available,as well as providing unsupervised learning and inversion under well-free conditions.The integration of qualitative and quantitative analyses proves instrumental in demonstrating the effectiveness of the proposed methodology through the use of synthetic and field pre-stack examples. 展开更多
关键词 Pre-stack seismic inversion Double dual neural network Physics guided Model guided
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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PASSIVITY-BASED SYNCHRONIZATION OF MARKOVIAN JUMP INERTIAL NEURAL NETWORKS VIA ADAPTIVE EVENT-DRIVEN PROTOCOL AND ITS APPLICATION TO IMAGE ENCRYPTION 认领 引用
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作者 A.CHANDRASEKAR Quanxin ZHU +2 位作者 T.RADHIKA M.KAMALI A.R.SUBHASHRI 《Acta Mathematica Scientia》 SCIE CSCD 2026年第3期1555-1580,共26页
This paper concentrates on the study of passivity-based synchronization of inertial neural networks including Markov jump parameters.The second-order differential equations are converted into first-order differential ... This paper concentrates on the study of passivity-based synchronization of inertial neural networks including Markov jump parameters.The second-order differential equations are converted into first-order differential equations using the variable transformation method.To make effective use of network bandwidth resources and to optimize the Markov jump inertial neural networks(MJINNs)performance,an adaptive event-driven protocol controller is studied.To achieve synchronization,an appropriate Lyapunov-Krasovskii functional(LKF)is constructed,which includes double integral terms that capture the information of time-varying delay terms.Some sufficient conditions are obtained in terms of linear matrix inequalities(LMIs)using Reciprocal convex combination lemma(RCCL).Then,a numerical simulation and an application of image encryption are carried out to illustrate the effectiveness of the proposed method. 展开更多
关键词 inertial neural networks adaptive event-driven protocol Markovian jump process synchronization
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Model predictive control based on LSTM neural network for maglev vehicle’suspension system 认领 引用
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作者 Mengjuan Liu Han Wu +3 位作者 Xin Liang Jiali Liu Xiaohui Zeng Kaixuan Hu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期504-517,共14页
To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dyn... To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory(LSTM)neural network,and this model is trained by machine learning.Subsequently,a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system’s prediction model and the suspension target.To compensate for the error of the prediction model resulting from changes in the control algorithm,a composite MPC algorithm is devised by combining both the proportionalintegral-derivative(PID)algorithm and the MPC algorithm.This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error.Finally,the effectiveness of the composite MPC algorithm is verified by simulation and experiment.The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle.Moreover,the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle,thereby fostering improved stability in the suspension system. 展开更多
关键词 Maglev vehicle Suspension system Model predictive control Long short-term memory neural network
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A Novel Phase-Aware Neural Network Framework for Fault Detection in Multiphase Processes via Feature Augmentation and Phase Discrimination 认领 引用
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作者 Yimeng He Zidong Wang +3 位作者 Weibo Liu Jingzhong Fang Linwei Chen Zhihuan Song 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期864-876,共13页
The dynamic nature of multiphase processes presents significant challenges to industrial fault detection.Most existing fault detection methods for multiphase processes,which have been developed to focus on creating a ... The dynamic nature of multiphase processes presents significant challenges to industrial fault detection.Most existing fault detection methods for multiphase processes,which have been developed to focus on creating a local fault detector for each phase,are hindered by two key challenges.Firstly,accurately matching test samples to their respective phases proves difficult,which leads to what is known as the phase matching problem.Secondly,constructing a reliable fault detector becomes challenging when limited data is available for specific phases.To overcome these challenges,a novel phase-aware neural network(PANN)is proposed in this paper for multiphase fault detection.The PANN is composed of a feature augmentation module,an encoder,a phase discriminator,and a decoder.Multiscale convolutional neural networks are employed to construct the feature augmentation module,which is used to extract multiscale features from the input data.The pseudo labels,which capture knowledge of the multiphase process,are used during the training of the phase discriminator to address the phase matching issue.A joint loss function is designed to train the entire PANN by integrating the loss terms for phase discrimination and future sample prediction.Validation of the proposed PANN is carried out using a numerical example.To further assess its practical application,the PANN is tested on a penicillin fermentation process dataset.Experimental results demonstrate that the proposed PANN achieves higher fault detection rates compared to several popular models currently used for fault detection in multiphase processes. 展开更多
关键词 Fault detection feature augmentation multiphase process phase-aware neural network (PANN) phase discriminator process monitoring
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FedGNN:Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids 认领 引用
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作者 Alanoud Al Mazroa Fahad Masood +3 位作者 Bakri Hussain Awaji Mohammad Alhefdi Abeer Aljohani Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1462-1483,共22页
The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv... The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids. 展开更多
关键词 Cyber security energy optimization graph neural networks IoT smart grids
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Memristive neural network circuit with fault tolerance for character recognition 认领 引用
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作者 Mei Guo Jikang Liu Jingzhi Xu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期325-342,共18页
Memristor-based neural networks are one of the most promising approaches for the hardware implementation of artificial neural networks.In this paper,a memristor-based neural network circuit based on a one-memristor–o... Memristor-based neural networks are one of the most promising approaches for the hardware implementation of artificial neural networks.In this paper,a memristor-based neural network circuit based on a one-memristor–one-resistor(1M1R)synaptic array structure is designed for character recognition.Compared with other memristive synaptic arrays,the 1M1R structure can reduce the number of memristors used.However,memristors may malfunction due to fabrication defects and the influence of external factors,resulting in a decrease in the accuracy of the circuit's character recognition,and a suitable solution needs to be found to improve the stability and durability of the circuit.Therefore,in this paper,a fault-tolerant module with feedback adjustment capability is designed in the memristive neural network circuit that can readjust the weights of the memristors through in-situ training to solve multiple faults in the memristive neural network.The effect of fault tolerance is verified by character recognition.The experimental results show that the designed memristive neural network circuit can accurately realize character recognition,and the designed fault-tolerant circuit can well tolerate multiple faults,ensuring stable operation of the circuit under fault conditions. 展开更多
关键词 memristor neural network circuit character recognition fault-tolerant feedback
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Residual resampling-based physics-informed neural network for neutron diffusion equations 认领 引用
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作者 Heng Zhang Yun-Ling He +3 位作者 Dong Liu Qin Hang He-Min Yao Di Xiang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期16-41,共26页
The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN app... The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations. 展开更多
关键词 Neutron diffusion equation Physics-informed neural network CNN with shortcut Residual adaptive resampling
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A neural network response surface updating method for complex dynamic models based on modal matching reconstruction strategy 认领 引用
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作者 Xiang LI Zhinong JIANG +2 位作者 Huajin SHAO Hao WANG Yanfei ZUO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期626-642,共17页
An integrated dynamic model updating method is proposed to address the challenges of mode jumping and mode degeneracy for complex dynamic models.A reduced order proxy model of Neural Network Response Surface(NNRS)was ... An integrated dynamic model updating method is proposed to address the challenges of mode jumping and mode degeneracy for complex dynamic models.A reduced order proxy model of Neural Network Response Surface(NNRS)was constructed by Modal Matching Reconstruction Strategy(MMRS)and an Improved Vectorial Surrogate Model(IVSM).Among them,the analytical modes are correctly matched with the experimental modes by MMRS,and the order of the mode matching is determined by calculating the Modal Assurance Criterion(MAC),addressing the dynamic changes of the mode matching order during the construction of the NNRS.The fitted NNRS model results are vectorized by IVSM,enabling the rapid extraction of required input and output parameters under multi-parameter conditions,thereby improving efficiency.The model parameters are updated using a multi-objective genetic algorithm,which achieves the simultaneous updating of natural frequency and mode shape.To validate the accuracy and efficiency,an intermediate casing of a gas turbine was updated using the proposed method.With high efficiency,the mean absolute error of natural frequency for the matched order decreased from 24.46%to 3.89%,while the corresponding average MAC value increased from 0.654 to 0.752. 展开更多
关键词 Complex structures Dynamic model updating Modal analysis Modal Assurance Criterion Neural network Response surface
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Perceiving the Battery Multi-Electrochemical States in Real-time Based on Model-Informed Neural Network 认领 引用
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作者 Ying Zhang Qinghao Zhang +3 位作者 Bin Duan Pingwei Gu Changlong Li Chenghui Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1041-1053,共13页
Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in a... Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in an advanced battery management system.Yet conventional pseudo-two-dimensional(P2D)physics methods suffer from high computational complexity and limit their online application.Thus,we develop a modelinformed neural network(MINN)framework that synergistically combines deep learning with a physics-based model to accurately monitor the battery electrochemical state(such as lithium-ion concentration,plating potential).Firstly,the MINN model is constructed with the innovative loss term containing experimentally measurable parameters and governing physical laws.Secondly,a composite framework based on a convolutional neural network(CNN)architecture is integrated to automatically extract features and enforce spatial boundary conditions,which significantly reduces the number of boundary loss terms that need to be solved and alleviates the complexity of the training process.After training,the MINN model can achieve an accurate estimation of internal states and even their spatiotemporal distributions that cannot be directly measured based on limited observable data and physical laws.At last,by incorporating dynamic current input,the well-trained basic model exhibits strong robustness and can be directly transferred to other cycling protocols with high accuracy,requiring no further retraining.MINN is a novel and promising framework to realize online and accurate micro electrochemical states monitoring,achieving at least 776 times speedup compared with the P2D model.As an innovative artificial intelligence assisted modeling for electrochemical systems,this framework enables root-cause analysis of battery behavior and failure modes,while empowering the management system with more reliable and trustworthy decision-making capabilities. 展开更多
关键词 Electrochemical state estimation lithium-ion battery model-informed neural network(MINN) real-time application
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Methods for correcting mud-logging lithology profi le using well logging data based on neural networks 认领 引用
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作者 Jin Li-zuan Sun Yu-hong +2 位作者 Li Hui Dai Zi-shan Wang Cheng-fang 《Applied Geophysics》 SCIE CSCD 2026年第2期710-719,870,共10页
This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of hu... This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of human factors in the process of traditional lithology correction of mud-logging profi le.Firstly,the lithology of mud-logging profi le is processed by digital technology and converted into digital curve which is consistent with the logging sampling interval,and the logging lithology curve is calculated by using the optimal logging method.Then,combining automatic depth-correction technology with manual correction methods,the lithology of mud-logging profi le is corrected for depth.On the basis of lithology depth-correction of mudlogging profile,the multi-layer perceptron(MLP)neural network is used to learn logging data and realize accurate identification of multiple lithologies,so as to construct a high-precision logging profile lithology curve and provide accurate basis for lithology correction of mud-logging profile.The effectiveness and accuracy of the proposed method are verifi ed by practical application cases.The corrected lithology of mudlogging profi le is highly consistent with the lithology of logging profi le,which provides a solid foundation for subsequent geological interpretation,reservoir evaluation and oil and gas resource assessment.This study not only improves the effi ciency of mud-logging data processing,but also ensures that the needs of exploration and exploitation work are met in a timely manner,which has important theoretical signifi cance and application value. 展开更多
关键词 logging data neural network data fusion mud-logging profi le lithology correction
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