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Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks
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作者 Rui Xiong Yinghao He +2 位作者 Yue Sun Yanbo Jia Weixiang Shen 《Journal of Energy Chemistry》 2025年第5期618-627,共10页
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models... For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management. 展开更多
关键词 Lithium-ion batteries Electrode level Ageing diagnosis Physics-informed neural network Convolutional neural networks
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Adaptive multi-stable stochastic resonance assisted by neural network and physical supervision
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作者 Xucan Li Deming Nie +1 位作者 Ming Xu Kai Zhang 《Chinese Physics B》 2025年第5期210-219,共10页
Stochastic resonance can utilize the energy of noise to enhance weak frequency characteristic.This paper proposes an adaptive multi-stable stochastic resonance method assisted by the neural network(NN)and physics supe... Stochastic resonance can utilize the energy of noise to enhance weak frequency characteristic.This paper proposes an adaptive multi-stable stochastic resonance method assisted by the neural network(NN)and physics supervision(directly numerical simulation of the physical system).Different from traditional adaptive algorithm,the evaluation of the objective function(i.e.,fitness function)in iteration process of adaptive algorithm is through a trained neural network instead of the numerical simulation.It will bring a dramatically reduction in computation time.Considering predictive bias from the neural network,a secondary correction procedure is introduced to the reevaluate the top performers and then resort them in iteration process through physics supervision.Though it may increase the computing cost,the accuracy will be enhanced.Two examples are given to illustrate the proposed method.For a classical multi-stable stochastic resonance system,the results show that the proposed method not only amplifies weak signals effectively but also significantly reduces computing time.For the detection of weak signal from outer ring in bearings,by introducing a variable scale coefficient,the proposed method can also give a satisfactory result,and the characteristic frequency of the fault signal can be extracted correctly. 展开更多
关键词 stochastic resonance multi-stable physical supervision neural network fault diagnosis
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Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks
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作者 Qian-Kun Sun Yue Zhang +8 位作者 Zi-Rui Hao Hong-Wei Wang Gong-Tao Fan Hang-Hua Xu Long-Xiang Liu Sheng Jin Yu-Xuan Yang Kai-Jie Chen Zhen-Wei Wang 《Nuclear Science and Techniques》 2025年第3期146-156,共11页
This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden node... This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data. 展开更多
关键词 Photoneutron reaction Bayesian neural network Machine learning Gamma source SLEGS
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Selection of abnormal trends in nuclearβ-decay half-lives by neural network and exploration of the physical mechanisms
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作者 Peng Li Zhong-Ming Niu Yi-Fei Niu 《Nuclear Science and Techniques》 2025年第3期134-145,共12页
Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first t... Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first time,to identify abnormal trends in nuclearβ-decay half-lives based on deviations between experimental values and the predictions of neural networks.Nuclei exhibiting anomalous increases,abrupt peaks,sharp decreases,abnormal odd-even oscillations,and excessively large experimental errors in theirβ-decay half-lives,which deviate from systematic patterns,were identified through deviations.These anomalous phenomena may be associated with shell effects,shape coexistence,or discrepancies in the experimental data.The discovery and analysis of these abnormal nuclei provide a valuable reference for further investigations using sophisticated microscopic theories,potentially offering insights into new physics through studies of nuclearβ-decay half-lives. 展开更多
关键词 β-decay half-lives neural network Abnormal nuclei
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Enhancing neural network robustness: Laser fault injection resistance in 55-nm SRAM for space applications
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作者 Qing Liu Haomiao Cheng +3 位作者 Xiang Yao Zhengxuan Zhang Zhiyuan Hu Dawei Bi 《Chinese Physics B》 2025年第4期478-484,共7页
The integration of artificial intelligence(AI)with satellite technology is ushering in a new era of space exploration,with small satellites playing a pivotal role in advancing this field.However,the deployment of mach... The integration of artificial intelligence(AI)with satellite technology is ushering in a new era of space exploration,with small satellites playing a pivotal role in advancing this field.However,the deployment of machine learning(ML)models in space faces distinct challenges,such as single event upsets(SEUs),which are triggered by space radiation and can corrupt the outputs of neural networks.To defend against this threat,we investigate laser-based fault injection techniques on 55-nm SRAM cells,aiming to explore the impact of SEUs on neural network performance.In this paper,we propose a novel solution in the form of Bin-DNCNN,a binary neural network(BNN)-based model that significantly enhances robustness to radiation-induced faults.We conduct experiments to evaluate the denoising effectiveness of different neural network architectures,comparing their resilience to weight errors before and after fault injections.Our experimental results demonstrate that binary neural networks(BNNs)exhibit superior robustness to weight errors compared to traditional deep neural networks(DNNs),making them a promising candidate for spaceborne AI applications. 展开更多
关键词 single event effects convolutional neural network fault injection SRAM
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Research on the X-ray polarization deconstruction method based on hexagonal convolutional neural network
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作者 Ya-Nan Li Jia-Huan Zhu +5 位作者 Huai-Zhong Gao Hong Li Ji-Rong Cang Zhi Zeng Hua Feng Ming Zeng 《Nuclear Science and Techniques》 2025年第2期49-61,共13页
Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagon... Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagonal grid track images obtained using gas pixel detectors(GPDs)for better anisotropy do not match the classical rectangle-based CNN,and converting the track images from hexagonal to square results in a loss of information.We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters,which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncer-tainty of the predicted emission angles.The simulated data from the PolarLight test were used to train and test the hexagonal CNN models.For individual energies,the hexagonal CNN algorithm produced 15%-30%improvements in the modulation factor compared to the moment analysis method for 100%polarized data,and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team,but at a lower computational and storage cost for preprocessing. 展开更多
关键词 X-ray polarization Track reconstruction Deep learning Hexagonal conventional neural network
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Causally enhanced initial conditions: A novel soft constraints strategy for physics informed neural networks
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作者 Wenshu Zha Dongsheng Chen +2 位作者 Daolun Li Luhang Shen Enyuan Chen 《Chinese Physics B》 2025年第4期365-375,共11页
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev... Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods. 展开更多
关键词 initial condition physics informed neural networks temporal march causality coefficient
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A graph neural network and multi-task learning-based decoding algorithm for enhancing XZZX code stability in biased noise
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作者 Bo Xiao Zai-Xu Fan +2 位作者 Hui-Qian Sun Hong-Yang Ma Xing-Kui Fan 《Chinese Physics B》 2025年第5期250-257,共8页
Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The... Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code. 展开更多
关键词 quantum error correction XZZX code biased noise graph neural network
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Atmospheric neutron single event effects for multiple convolutional neural networks based on 28-nm and 16-nm SoC
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作者 Xu Zhao Xuecheng Du +3 位作者 Chao Ma Zhiliang Hu Weitao Yang Bo Zheng 《Chinese Physics B》 2025年第1期477-484,共8页
The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spect... The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips. 展开更多
关键词 single event effects atmospheric neutron system on chip convolutional neural network
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A Basis Function Generation Based Digital Predistortion Concurrent Neural Network Model for RF Power Amplifiers
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作者 SHAO Jianfeng HONG Xi +2 位作者 WANG Wenjie LIN Zeyu LI Yunhua 《ZTE Communications》 2025年第1期71-77,共7页
This paper proposes a concurrent neural network model to mitigate non-linear distortion in power amplifiers using a basis function generation approach.The model is designed using polynomial expansion and comprises a f... This paper proposes a concurrent neural network model to mitigate non-linear distortion in power amplifiers using a basis function generation approach.The model is designed using polynomial expansion and comprises a feedforward neural network(FNN)and a convolutional neural network(CNN).The proposed model takes the basic elements that form the bases as input,defined by the generalized memory polynomial(GMP)and dynamic deviation reduction(DDR)models.The FNN generates the basis function and its output represents the basis values,while the CNN generates weights for the corresponding bases.Through the concurrent training of FNN and CNN,the hidden layer coefficients are updated,and the complex multiplication of their outputs yields the trained in-phase/quadrature(I/Q)signals.The proposed model was trained and tested using 300 MHz and 400 MHz broadband data in an orthogonal frequency division multiplexing(OFDM)communication system.The results show that the model achieves an adjacent channel power ratio(ACPR)of less than-48 d B within a 100 MHz integral bandwidth for both the training and test datasets. 展开更多
关键词 basis function generation digital predistortion generalized memory polynomial dynamic deviation reduction neural network
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An efficient and accurate numerical method for simulating close-range blast loads of cylindrical charges based on neural network
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作者 Ting Liu Changhai Chen +2 位作者 Han Li Yaowen Yu Yuansheng Cheng 《Defence Technology(防务技术)》 2025年第2期257-271,共15页
To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based sim... To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures. 展开更多
关键词 Close-range air blast load Cylindrical charge Numerical method neural network CEL method CONWEP model
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Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection
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作者 Fei Yu Dan Su +3 位作者 Shaoqi He Yiya Wu Shankou Zhang Huige Yin 《Chinese Physics B》 2025年第5期289-301,共13页
Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural networks.As a type of neural network whose dynamic behavior can be explained,the coupling of resonant ... Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural networks.As a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the literature.Therefore,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the RTD-MCNN.Based on this model,a simple encryption scheme for the protection of digital images in police forensic applications is proposed.The results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting multistability.Furthermore,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear variations.Undoubtedly,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection.Encryption tests and security analyses validate the effectiveness of this scheme. 展开更多
关键词 MEMRISTOR HYPERCHAOS resonant tunneling diode-based cellular neural network(RTD-CNN) dynamic analysis image encryption
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TDNN:A novel transfer discriminant neural network for gear fault diagnosis of ammunition loading system manipulator
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作者 Ming Li Longmiao Chen +3 位作者 Manyi Wang Liuxuan Wei Yilin Jiang Tianming Chen 《Defence Technology(防务技术)》 2025年第3期84-98,共15页
The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau... The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods. 展开更多
关键词 Manipulator gear fault diagnosis Reciprocating machine Domain adaptation Pseudo-label training strategy Transfer discriminant neural network
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Neural network analysis for prediction of heat transfer of aqueous hybrid nanofluid flow in a variable porous space with varying film thickness over a stretched surface
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作者 Abeer S Alnahdi Taza Gul 《Chinese Physics B》 2025年第2期316-326,共11页
The high thermal conductivity of the nanoparticles in hybrid nanofluids results in enhanced thermal conductivity associated with their base fluids.Enhanced heat transfer is a result of this high thermal conductivity,w... The high thermal conductivity of the nanoparticles in hybrid nanofluids results in enhanced thermal conductivity associated with their base fluids.Enhanced heat transfer is a result of this high thermal conductivity,which has significant applications in heat exchangers and engineering devices.To optimize heat transfer,a liquid film of Cu and TiO_(2)hybrid nanofluid behind a stretching sheet in a variable porous medium is being considered due to its importance.The nature of the fluid is considered time-dependent and the thickness of the liquid film is measured variable adjustable with the variable porous space and favorable for the uniform flow of the liquid film.The solution of the problem is acquired using the homotopy analysis method HAM,and the artificial neural network ANN is applied to obtain detailed information in the form of error estimation and validations using the fitting curve analysis.HAM data is utilized to train the ANN in this study,which uses Cu and TiO_(2)hybrid nanofluids in a variable porous space for unsteady thin film flow,and it is used to train the ANN.The results indicate that Cu and TiO_(2)play a greater role in boosting the rate. 展开更多
关键词 thin film of Cu and TiO_(2)hybrid nanofluids variable porous space unsteady stretching sheet viscous dissipation heat transfer optimization artificial neural network
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Pluggable multitask diffractive neural networks based on cascaded metasurfaces 被引量:7
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作者 Cong He Dan Zhao +8 位作者 Fei Fan Hongqiang Zhou Xin Li Yao Li Junjie Li Fei Dong Yin-Xiao Miao Yongtian Wang Lingling Huang 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2024年第2期23-31,共9页
Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been c... Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems. 展开更多
关键词 optical neural networks diffractive deep neural networks cascaded metasurfaces
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A multiscale adaptive framework based on convolutional neural network:Application to fluid catalytic cracking product yield prediction 被引量:1
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作者 Nan Liu Chun-Meng Zhu +1 位作者 Meng-Xuan Zhang Xing-Ying Lan 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2849-2869,共21页
Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial pro... Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications. 展开更多
关键词 Fluid catalytic cracking Product yield Data-driven modeling Multiscale prediction Data decomposition Convolution neural network
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A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs 被引量:1
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作者 Yan-Wei Wang Zhen-Xue Dai +3 位作者 Gui-Sheng Wang Li Chen Yu-Zhou Xia Yu-Hao Zhou 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期286-301,共16页
To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) s... To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs. 展开更多
关键词 Deep learning Physics-informed data-driven neural network Depleted shale reservoirs CO_(2)storage Transport mechanisms
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Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network 被引量:1
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作者 Xue-Qiang Zhang Qing-Lin Cheng +2 位作者 Wei Sun Yi Zhao Zhi-Min Li 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期621-640,共20页
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud... As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems. 展开更多
关键词 Crude oil gathering and transportation system GA-BP neural network Energy efficiency evaluation TOPSIS evaluation method Energy saving and consumption reduction
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Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer 被引量:1
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作者 Wenxue Chen Yudong Hu +1 位作者 Changsheng Gao Ruoming An 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期412-429,共18页
This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance system... This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots. 展开更多
关键词 BP network neural Integral sliding mode control(ISMC) Missile defense Prescribed performance function(PPF) State observer Tracking guidance system
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A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network 被引量:1
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作者 Yunhe Liu Tengfei Guo +2 位作者 Jinda Li Chunxing Pei Jianqiang Liu 《High-Speed Railway》 2024年第1期64-70,共7页
The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining d... The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules. 展开更多
关键词 IGBT Electro-thermal coupling model Junction temperature monitoring Loss model neural networks
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