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A Two-Timescale Neurodynamic Approach to Sharpness-Aware Minimization in Deep Learning 认领 引用
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作者 Dan Su Jun Wang +1 位作者 Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1530-1532,共3页
Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language pro... Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language processing,robotics and control.In deep learning,it is essential to boost their generalization power[1].Existing deep learning strategies for improving the generalization power include regularization,data augmentation,etc.[2]. 展开更多
关键词 deep learning computer visionnatural language processingrobotics generalization power boost their deep learningit deep learningdeep learning two timescale neurodynamic approach sharpness aware minimization
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Integrable Deep Learning 认领 引用
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作者 Yong Chen 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第6期1-9,共9页
We explain the motivation for proposing the concept and framework of integrable deep learning(IDL),and focuse on a series of advances we have made in IDL algorithms.1.Two-stage PINN methods based on conservation laws,... We explain the motivation for proposing the concept and framework of integrable deep learning(IDL),and focuse on a series of advances we have made in IDL algorithms.1.Two-stage PINN methods based on conservation laws,and PINN methods based on the Miura transformation;2.Lax pair-informed neural networks(LPNNs)and DT-LPNN combined with the Darboux transformation;3.Novel convolutional neural network architectures for integrable systems,including pseudo grid-based physics-informed convolutional-recurrent network(PG-PhyCRNet)and polynomial extractor for rogue wave patterns(PE-RWP). 展开更多
关键词 miura transformation PINN miura transformation lax convolutional neural network darboux transformation novel integrable deep learning idl conservation laws integrable deep learning
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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：2
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作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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AI and Deep Learning for Terahertz Ultra-Massive MIMO:From Model-Driven Approaches to Foundation Models 认领 引用 被引量：1
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作者 Wentao Yu Hengtao He +4 位作者 Shenghui Song Jun Zhang Linglong Dai Lizhong Zheng Khaled B.Letaief 《Engineering》 SCIE EI CSCD 2026年第1期14-33,共20页
This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the ch... This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design:computational complexity,modeling difficulty,and measurement limitations.The study posits that AI provides a promising solution to these challenges.Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems.The first roadmap,model-driven deep learning(DL),emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework.Four essential steps are discussed:algorithmic frameworks,basis algorithms,loss function design,and neural architecture design.The second roadmap presents channel state information(CSI)foundation models,aimed at unifying the design of different transceiver modules by focusing on their shared foundation,that is,the wireless channel.The training of a single compact foundation model is proposed to estimate the score function of wireless channels,which serve as a versatile prior for designing a wide variety of transceiver modules.Four essential steps are outlined:general frameworks,conditioning,site-specific adaptation,and the joint design of CSI foundation models and model-driven DL.The third roadmap aims to explore potential directions for applying pretrained large language models(LLMs)to terahertz UM-MIMO systems.Several application scenarios are envisioned,including LLM-based estimation,optimization,search,network management,and protocol understanding.Finally,the study highlights open problems and future research directions. 展开更多
关键词 Terahertz communications Ultra-massive multiple-input multiple-output Model-driven deep learning Foundation models Large language models
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RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
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作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
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A Deep Learning Framework for Heart Disease Prediction with Explainable Artificial Intelligence 认领 引用 被引量：1
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作者 Muhammad Adil Nadeem Javaid +2 位作者 Imran Ahmed Abrar Ahmed Nabil Alrajeh 《Computers, Materials & Continua》 SCIE EI 2026年第1期1944-1963,共20页
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni... Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction. 展开更多
关键词 Heart disease deep learning localized random affine shadowsampling local interpretable modelagnostic explanations shapley additive explanations 10-fold cross-validation
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Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey 认领 引用 被引量：1
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作者 Binglei Yue Aili Jiang +3 位作者 Chun Yang Junwei Lei Heng Liu Yin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1-28,共28页
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I... With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing. 展开更多
关键词 Channel State Information(CSI) human sensing human activity recognition deep learning
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Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage 认领 引用 被引量：3
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作者 Yu-Han Yang Yuan Li 《World Journal of Radiology》 2026年第1期30-45,共16页
BACKGROUND Spontaneous intracerebral hemorrhage(ICH)is a severe form of stroke with high early mortality,and hematoma enlargement(HE)occurs in roughly one-third of patients and strongly predicts poor outcomes.Quantita... BACKGROUND Spontaneous intracerebral hemorrhage(ICH)is a severe form of stroke with high early mortality,and hematoma enlargement(HE)occurs in roughly one-third of patients and strongly predicts poor outcomes.Quantitative image analysis using handcrafted radiomics and deep learning-derived features can capture hematoma and perihematomal edema(PHE)heterogeneity objectively that the combination of these approaches with clinical data may improve early prediction of HE and in-hospital mortality.AIM To evaluate and validate the predictive performance of hematoma-and PHE-derived features on non-contrast computed tomography via handcrafted radiomics and automatic deep learning analysis for prediction of early HE and hospital mortality in spontaneous ICH.METHODS Of 322 patients with basal ganglia ICHs were included retrospectively between June 2018 and June 2020,and assigned into the training cohort(n=225)and the testing cohort(n=97).We extracted features on hematoma and PHE subregions via handcrafted radiomics analysis manually and deep learning analysis of pretrained convolutional neural networks via transfer learning automatically.Support vector machine was adopted as the classifier for prediction of HE and hospital mortality.The clinical-radiological integrated models for HE and hospital mortality were constructed on clinical data and radiological signatures generated from the radiological models with the optimal area under the receiver operating characteristics curve in the testing cohort.RESULTS The clinical-radiological model combining clinical information and hematoma-and PHE-derived computed tomography features for prediction of HE implied an area under the receiver operating characteristics curve of 0.828 with 95%confidence interval of 0.714 to 0.942 with accuracy of 72.89%,sensitivity of 70.00%,and specificity of 74.52%in the testing cohort.The model integrating clinical and radiological features showed great identification performance for predicting hospital mortality,demonstrating significant classification and discrimination abilities after validation.CONCLUSION Quantitative radiomics features from hematoma and PHE regions on non-contrast computed tomography images showed good performance for predicting HE and hospital mortality in patients with ICH. 展开更多
关键词 Intracerebral hemorrhage Enlargement Computed tomography Radiomics Deep learning Hospital mortality
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An integrated deep learning framework for full-cycle CCUS-EOR evaluation and optimization under carbon neutrality 认领 引用
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作者 Bin Shen Sheng-Lai Yang +5 位作者 Yi-Qi Zhang Xin-Yuan Gao Lu-Fei Bi Kai Du Er-Meng Zhao Hong-Bo Zeng 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期2288-2307,共20页
Carbon capture,enhanced oil recovery(EOR)-utilization and storage(CCUS-EOR)is recognized as an effective approach to mitigate greenhouse gas emissions while delivering economic benefits.However,its practical deploymen... Carbon capture,enhanced oil recovery(EOR)-utilization and storage(CCUS-EOR)is recognized as an effective approach to mitigate greenhouse gas emissions while delivering economic benefits.However,its practical deployment is limited by the absence of advanced deep learning models for petroleum tabular data,the limited adaptability of existing optimization methods,and the lack of comprehensive evaluation for full-cycle CCUS-EOR.Here,we introduce a generalizable framework that integrates mechanism experiments,numerical simulations,and deep learning methods to address these challenges.Three-stage experiments are conducted to clarify microscopic displacement mechanisms and provide key parameters for numerical simulation.Based on field-scale simulations of 20 years of CO2 water-alternating-gas(WAG)injection followed by 19 years of pure CO2 storage until 2060,we develop a TabPFN-based meta-learning surrogate model for joint prediction of oil recovery,CO2 storage,and net present value(NPV),achieving high accuracy(prediction error0.97)compared to baseline models.We further apply an improved multi-objective optimization using the Adaptive Crossover and Adaptive Mutation Non-dominated Sorting Genetic Algorithm II(ACAM-NSGA-II)to obtain optimal Pareto solutions.Compared to baseline cases,the proposed framework significantly enhances CCUS-EOR performance,enhancing oil recovery by 27.05%(from 5.95×105t,35.17%to 1.05×106t,62.22%),tripling CO2 storage capacity(from 1.33×106 to 4.45×106 t),and improving NPV by 68.0%(from RDSCHARDOLLAR344 million to RDSCHARDOLLAR578 million).The Pareto front is further divided into three different solution regions,thereby elucidating the underlying physical mechanisms associated with each cluster and providing clear operational insights for target-oriented CO2-WAG design.This study offers a scalable blueprint framework for large-scale engineering design in petroleum engineering,particularly in tabular prediction and multi-objective optimization contexts. 展开更多
关键词 CCUS-EOR CO₂storage Deep learning Tabular prior-data fit network(TabPFN) ACAM-NSGA-II Multi-objective optimization
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Deep Learning-Driven Surface-Enhanced Raman Scattering-Lateral Flow Immunoassay With Au Nanostars for High-Accuracy Respiratory Virus Detection 认领 引用
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作者 Shuai Zhao Yan-Yan Li +7 位作者 Cheng-Long Lin Mei-Mei Xu Wei-Da Zhang Dan Li Yu-Si Peng Masaki Tanemura Tian-Shuo Zhao Yong Yang 《Rare Metals》 SCIE EI CAS CSCD 2026年第2期492-505,共14页
The high accuracy in surface-enhanced Raman scattering-lateral flow immunoassays(SERS-LFIAs)is critical for reliable pointof-care testing(POCT)in clinical diagnostics.Conventional approaches are often affected by samp... The high accuracy in surface-enhanced Raman scattering-lateral flow immunoassays(SERS-LFIAs)is critical for reliable pointof-care testing(POCT)in clinical diagnostics.Conventional approaches are often affected by sampling variability and uneven distribution of immunoprobes,leading to unreliable signal fluctuations.To address this challenge,we developed a highperformance SERS-LFIA strip based on gold nanostars(Au NSs)and integrated it with an artificial intelligence(AI)-powered diagnostic framework.Specifically,Au NSs with exceptional SERS enhancement were synthesized via an optimized“twostep”method and utilized as nanoprobes to construct an influenza B(FluB)SERS-LFIA strip for performance validation.A novel large-area Raman scanning technique was then employed to generate intensity maps depicting the immunoprobe distribution around the test(T)line.A deep residual neural network(ResNet-18)was subsequently applied to analyze these SERS images,minimizing subjective interpretation and significantly improving accuracy.The optimized framework achieved 100%training accuracy and 95%validation accuracy,significantly outperforming conventional peak intensity analysis and support vector machine(SVM)-based full-spectrum discrimination methods.The Au NSs-based SERS-LFIA platform and the optimized ResNet-18 model were integrated into a portable Raman spectrometer to create an automated diagnostic system.To further evaluate the stability and versatility of the system,the detection target was switched to influenza A(FluA)by altering the capture and detection antibodies.This reengineered system demonstrated a 95%accuracy rate in testing 40 simulated human clinical samples.Our work establishes a machine learning-enhanced,automated SERS-LFIA system that leverages Au NSs for superior signal enhancement and utilizes deep learning for robust image analysis.This integrated approach provides a scalable and high-performance POCT framework,paving the way for automated clinical diagnostics. 展开更多
关键词 automated diagnosis system deep learning gold nanostars ResNet-18 SERS-LFIA
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Multi-antiderivative transformation alternating iterative deep learning method for solving anisotropic scattering neutron transport equations 认领 引用
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作者 Dong Liu Bin Zhang +4 位作者 Qi Luo Heng Zhang Yong Jiang Xian-Tao Cui Chen Zhao 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第5期375-393,共19页
Deep learning methods have achieved significant progress in solving partial differential equations.However,when applied to the widely used anisotropic scattering neutron transport equations in reactor engineering,thes... Deep learning methods have achieved significant progress in solving partial differential equations.However,when applied to the widely used anisotropic scattering neutron transport equations in reactor engineering,these encounter significant challenges.To address this issue,this study introduces a multi-antiderivative transformation alternating iterative deep learning method(M-AIM).This method transforms the integral terms of the scattering and fission sources in the transport equation into multiple antiderivative functions corresponding to the integrand,converts the differential-integral form of the transport equation into an exact differential equation,and establishes the necessary constraints for a unique solution.The M-AIM uses multiple deep neural networks to map the unknown angular flux density of transport equations and represents various forms of antiderivative functions.It constructs the corresponding weighted loss functions.By alternating iterative training with deep learning methods applied to these neural networks,the loss is reduced gradually.When the loss decreases to a preset minimum,the neural network approaches a numerical solution for both angular flux density and antiderivative functions.This paper presents a numerical verification of geometries such as flat plates and spheres.It verifies the validity of the theoretical framework and associated methods.The study contributes to the development of novel technical approaches for applying deep learning to solve anisotropic scattering neutron transport equations in reactor engineering. 展开更多
关键词 Neutron transport equations Anisotropic scattering Multi-antiderivative Alternating iteration Deep learning
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Prediction of Regional Surface Wave Parameters in the Qinhuangdao Sea Using a Deep Learning Model with Limited Observational Data 认领 引用
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作者 WANG Lei FANG Kezhao +2 位作者 ZHOU Long GONG Lixin HUO Yongwei 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第1期74-90,共17页
Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area loca... Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area located in the Bohai Sea,China.Herein,we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts(ECMWF),improving the accuracy of boundary conditions.Then,we use the Simulating WAves Nearshore(SWAN)model to simulate the regional wave field over time.A regional wave-parameter prediction model is then developed using a limited number of sampled data(covering only 2 years,2020–2021);the model is based on the Whale Optimization Algorithm(WOA),convolutional neural networks(CNNs),and long short-term memory(LSTM)neural networks.WOA is used to optimize the CNN and LSTM framework;in this framework,CNN extracts spatial features,and the LSTM network captures temporal features,enabling accurate short and long-term predictions of wave height,period,and direction.The experimental results showed that despite the small sample size,the model achieves a goodness of fit of 0.9957 for wave height prediction,0.9973 for period,and 0.9749 for wave direction in short-term forecasting.As the prediction step size increases,the accuracy of the model decreases.When the prediction step size reaches 9 h,the root mean square error for the prediction of wave height,period,and direction increases to 0.2060 m,0.4582 s,and32.5358°,respectively.The reliability and applicability of the model are further validated by the experimental results.Our findings highlighted the potential of the developed model in operational wave forecasting,even with a limited number of sampled data. 展开更多
关键词 regional wave prediction deep learning WOA-CNN-LSTM numerical simulation Bohai Sea
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Deep learning-based cognitive impairment brain imaging analysis:New methods,new technologies,and new paradigms 认领 引用
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作者 Qingqin Xu Jianwei Lu +2 位作者 Zhongfu Zhang Dongsheng Xu Chengxiang Guo 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第9期4135-4147,共13页
Cognitive impairment arising from ischemic stroke,Alzheimer's disease,and Parkinson's disease presents distinct structural and network-level alterations.Brain magnetic resonance imaging offers a non-invasive a... Cognitive impairment arising from ischemic stroke,Alzheimer's disease,and Parkinson's disease presents distinct structural and network-level alterations.Brain magnetic resonance imaging offers a non-invasive and high-resolution approach to assess these changes,while deep learning provides powerful tools for automated analysis.Given that accurate lesion delineation,precise localization of abnormal regions,and reliable disease classification are fundamental to clinical decision-making.This review aims to explore the application of deep learning techniques to brain magnetic resonance imaging analysis of cognitive impairments caused by these disorders,with a focus on three core tasks:lesion segmentation,object detection,and image classification.Recent widely accepted findings indicate that ischemic stroke studies have achieved state-of-the-art lesion segmentation performance,with optimized U-shaped convolutional network(U-Net)and hybrid convolutional neural network-transformer models reaching Dice scores up to 0.911 in delineating focal damage.Alzheimer's disease research has advanced classification and staging accuracy by more than 10%compared with unimodal baselines through three-dimensional convolutional neural network,Transformers,and multimodal fusion,enabling more precise detection of diffuse cortical atrophy.Parkinson's disease imaging,despite lacking overt structural lesions,has leveraged ResNet and Vision Transformer backbones to identify subtle and spatially distributed abnormalities,improving early-stage differentiation.Persistent challenges include the scarcity of large,high-quality annotated datasets,substantial inter-site variability,high annotation costs,and limited interpretability,hindering clinical integration.Addressing these barriers will require advances in federated learning to mitigate data scarcity while preserving privacy,domain adaptation techniques to reduce inter-site variability,automated annotation,and low-resource training strategies to lower labeling costs,and explainable artificial intelligence to improve interpretability,thereby ensuring model robustness,privacy,and transparency.This review highlights emerging methods,innovative technologies,and novel paradigms that are redefining brain imaging analysis in cognitive impairment.Mechanistically,deep learning improves cognitive impairment analysis by integrating hierarchical and multiscale spatial features,modeling longrange functional connectivity disruptions,and fusing structural with functional imaging to better represent networklevel pathology.In conclusion,aligning network architectures with disease-specific imaging characteristics and task requirements can greatly enhance the accuracy,robustness,and generalizability of magnetic resonance imaging analyses for cognitive impairment.Future work should focus on multimodal fusion,structure-function coupling,cross-disease evaluations,and embedding artificial intelligence tools into clinical workflows to support early detection,individualized treatment planning,and large-scale clinical adoption. 展开更多
关键词 Alzheimer’s disease cognitive dysfunction deep learning image classification ischemic stroke lesion segmentation magnetic resonance imaging neuroimaging object detection Parkinson’s disease
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Deep learning advances high-throughput toxicity screening of chemicals at multi-biological levels 认领 引用
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作者 Xinyue Wu Ya Zhu +3 位作者 Jie Hou Jianying Zhang Jiang Xu Daohui Lin 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第6期706-719,共14页
Deep learning(DL)has emerged as a powerful tool for modeling unstructured data,thereby improving prediction accuracy and expanding the application of machine learning(ML)in toxicity assessment.However,selecting suitab... Deep learning(DL)has emerged as a powerful tool for modeling unstructured data,thereby improving prediction accuracy and expanding the application of machine learning(ML)in toxicity assessment.However,selecting suitable DL architectures and training methods for toxicity prediction remains challenging due to the lack of systematic comparisons regarding data types and modeling tasks across biological levels,which hinders the development of optimal models.To address these challenges,we review the current DL applications for predicting toxic events at four stages within the adverse outcome pathway framework:toxicophore-induced effects at the chemical exposure stage,activation of toxic pathways at the macro-molecular level(molecular initiating events),toxicogenomic responses at the cellular level(key events),and observable toxic effects(adverse outcomes)at the tissue/organ/individual levels.We compare the technical aspects of various DL methods for toxicity prediction and discuss how interpretability analyses can reveal the underlying molecular mechanisms and modes of toxic action.We also summarize current solutions to the challenges of increased data requirements and reduced interpretability of DL compared to traditional ML,and propose the development of a general environmental toxicological model.We hope that the interdisciplinary insights provided in this review can accelerate the development and application of new DL models in high-throughput toxicity screening,thereby advancing risk management strategies based on modes of toxic action. 展开更多
关键词 Deep learning Toxicity prediction Adverse outcome pathway High-throughput screening
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Inverse design of 3D integrated high-efficiency grating couplers using deep learning 认领 引用
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作者 Yu Wang Yue Wang +4 位作者 Guohui Yang Kuang Zhang Xing Yang Chunhui Wang Yu Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期363-373,共11页
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le... In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices. 展开更多
关键词 deep learning inverse design grating couplers photonic devices
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Erratum to: Deep learning technique based efficient optimizationmethod for cone dielectric energy generator 认领 引用
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作者 Demin Zhao Aoyu Xiao +4 位作者 Zewen Gu Menghang Chen Guoqiang Xu Bin Zhao Jianlin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期142-142,共1页
The name of Bin Zhao in the original published Chinese section is“赵斌”,which should be corrected to“赵彬”.
关键词 deep learning bin zhao cone dielectric energy generator optimization method
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Nowcasting of cloud-to-ground lightning location and frequency based on a deep learning technique 认领 引用
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作者 Fengquan Li Jian Li +6 位作者 Shanqiang Gu Yu Wang Zhe Li Lei Zhang Ze Liu Bingjie Bai Zhibo Jiang 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第3期1-6,共6页
Predicting lightning that can cause power grid trips is significant for disaster prevention.This paper integrates cloud-to-ground lightning detection,water vapor and infrared channel as well as channel differences fro... Predicting lightning that can cause power grid trips is significant for disaster prevention.This paper integrates cloud-to-ground lightning detection,water vapor and infrared channel as well as channel differences from the Himawari satellite,to nowcast lightning locations and frequencies in Central China based on deep learning.The model utilized is Convolutional Gated Recurrent Unit with attention mechanisms.Unlike previous studies that typically predict lightning locations and probabilities,this study forecasts both lightning locations and frequencies.Evaluation of the model test set shows that(1)within a lead time of 0 to 120 min,the average probability of detection(POD)is 0.439 and the average critical success index(CSI)is 0.207;(2)as the lead time extends from 10to 120 min,the performance gradually declines,with the accuracy(ACC)decreasing from 0.993 to 0.987,POD decreasing from 0.586 to 0.371,false alarm rate(FAR)increasing from 0.543 to 0.771,CSI decreasing from 0.336to 0.132,and mean absolute error(MAE)increasing from 0.01 to 0.014;and(3)the model performs well for organized storms but faces challenges with isolated cells or new cells near the domain boundary.The constructed warm-season lightning nowcasting model for Central China is tested with a winter thunderstorm in Central China and a spring tornadic storm in South China that caused transmission line trip incidents.The model has strong generalization capabilities over time and space,providing practical value in mitigating lightning-induced power grid trips. 展开更多
关键词 Lightning frequency nowcasting Deep learning Conv-GRU-attention Generalization capabilities Power grid Tornadic storm
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A novel deep learning-based framework for forecasting 认领 引用
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作者 Congqi Cao Ze Sun +2 位作者 Lanshu Hu Liujie Pan Yanning Zhang 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第1期22-26,共5页
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep... Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance. 展开更多
关键词 Weather forecasting Deep learning Semantic segmentation models Learnable Gaussian noise Cascade prediction
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A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification 认领 引用
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作者 Hamzeh Asgharnezhad Pegah Tabarisaadi +2 位作者 Abbas Khosravi Roohallah Alizadehsani U.Rajendra Acharya 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1849-1874,共26页
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and... Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and limited uncertainty awareness.This study presents a comprehensive evaluation of deep learning-based skin lesion classification with transfer learning and UQ on the HAM10000 dataset.We benchmark several pre-trained feature extractors(including Contrastive Language-Image Pre-training(CLIP)variants,ResNet50,DenseNet121,VGG16,EfficientNet-V2-Large,and ConvNeXt Large)combined with traditional classifiers such as SVM,XGBoost,and logistic regression.Multiple PCA settings(64,128,256,512)are explored,with LAION CLIP ViT-H/14 and ViT-L/14 at PCA-256 achieving the strongest baseline results.In the UQ phase,Monte Carlo Dropout(MCD),Ensemble,and Ensemble Monte Carlo Dropout(EMCD)are applied and evaluated using uncertainty-aware metrics(UAcc,USen,USpe,UPre).Ensemble methods with PCA-256 provide the best balance between accuracy and reliability.Further improvements are obtained through feature fusion of top-performing extractors at PCA-256.Finally,we propose a feature-fusion-based model trained with a Predictive Entropy(PE)loss function,which outperforms all prior configurations across both standard and uncertainty-aware evaluations,advancing trustworthy deep learning-based skin cancer diagnosis. 展开更多
关键词 Deep learning Machine learning Classification Uncertainty quantification Skin cancer
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