期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到77,284篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Review of the classification and related terminology of fine-grained sedimentary rocks 认领 引用 被引量：1
1
作者 ZHU Rukai SUN Longde +2 位作者 ZOU Caineng CHEN Yang MIAO Xue 《Petroleum Exploration and Development》 SCIE 2026年第1期61-78,共18页
Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a class... Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area. 展开更多
关键词 fine-grained sedimentary rock shale mudstone clay shale oil shale gas lamellation shale microfacies classification scheme fine-grained sedimentology
暂未订购 下载PDF
Multimodal Signal Processing of ECG Signals with Time-Frequency Representations for Arrhythmia Classification 认领 引用 被引量：1
2
作者 Yu Zhou Jiawei Tian Kyungtae Kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期990-1017,共28页
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin... Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification. 展开更多
关键词 Electrocardiogram arrhythmia classification multimodal time-frequency representation
暂未订购 下载PDF
A robust phylogenomic framework supports a revised intrafamilial classification of Urticaceae 认领 引用
3
作者 Xiao-Gang Fu Jie Liu +8 位作者 Richard I.Milne Alex K.Monro Shui-Yin Liu Qin Tian Gregory W.Stull Amos Kipkoech Ting-Shuang Yi De-Zhu Li Zeng-Yuan Wu 《Plant Diversity》 SCIE CAS CSCD 2026年第2期289-306,共18页
Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic s... Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic sequences and taxa within the nettle family(Urticaceae)are still lacking.Here,we reconstructed the phylogeny of Urticaceae(345 species across 89% of accepted genera)using concatenated and coalescent analyses from plastome and nuclear ribosomal DNA sequences.Different plastid datasets and tree inference methods yielded a consistent phylogenetic backbone,with 98% of nodes achieving>90% bootstrap support—a significant improvement compared to 54% of nodes in the latest published phylogenetic study of Urticaceae.Plastid and nuclear phylogenetic relationships were largely congruent,with several exceptions that warrant further study.In the context of the updated phylogenetic relationships,we propose dividing the family into seven tribes that correspond to seven major clades or subclades,including a newly established tribe,Sarcochlamydeae stat.nov.Our phylogenetic analysis indicates that Debregeasia and Phenax are non-monophyletic.By combing morphological,molecular and distributional evidence,we describe a new genus Chiajuia gen.nov.Additionally,we propose synonymizing the following genera:Cypholophus(to Boehmeria),Haroldiella(to Pilea),Hemistylus,Neodistemon,Rousselia(all to Pouzolzia),Hesperocnide(to Urtica),and Pellionia(to Elatostema),while recognizing Elatostematoides,Gonostegia,Leptocnide,Margarocarpus,Scepocarpus,and Sceptrocnide as distinct genera.This robust phylogenomic framework and revised classification lays a foundation for future studies on the evolution and ecology of Urticaceae.The approach applied here may also serve as an important reference for other large plant families in angiosperms. 展开更多
关键词 Chiajuia Intrafamilial classification Phylogenomics Plastome Sarcochlamydeae Urticaceae
暂未订购 下载PDF
Crack type classification of fissured sandstone using acoustic emission and Gaussian mixture modeling:Effects of fissure inclination angles 认领 引用
4
作者 XUE Kang-sheng PU Hai +5 位作者 LU Yin-long CHEN Yan-long WU Yu LI Ming LIU De-jun XU Jun-ce 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第5期2166-2188,共23页
Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification fram... Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification framework based on Gaussian mixture modeling(GMM)for distinguishing acoustic emission(AE)signatures associated with different fracture modes in sandstone samples that contain prefabricated fissures at varying inclination angles.The frequency-domain characteristics of the AE signals were extracted using fast Fourier transform(FFT),while the RA-AF ratio(rise time/amplitude versus average frequency)parameter space was employed to characterize the crack mechanisms.To increase classification accuracy and model robustness,the Bayesian information criterion(BIC)was introduced to determine the optimal number of Gaussian components.Experimental results from uniaxial compression tests reveal that fissure inclination significantly affects crack evolution behavior:low-angle fissures favor shear and hybrid cracks,whereas high-angle fissures cause tensile failure.The proposed GMM-based method effectively identifies tensile,shear,and hybrid cracks with increased objectivity and accuracy,outperforming traditional empirical RA-AF thresholding techniques.This research provides a reliable and generalizable approach for AE signal classification,which presents theoretical insights and practical support for real-time monitoring,early warning,and structural health assessment in fractured rock masses. 展开更多
关键词 acoustic emission sandstone crack classification Gaussian mixture model(GMM) RA-AF analysis fissure inclination
暂未订购 下载PDF
A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification 认领 引用
5
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
DCETEN:a lightweight ECG automatic classification network based on Transformer model 认领 引用
6
作者 Fan Jiang Jiayi Xiao +1 位作者 Lei Liu Chaowei Wang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第5期789-801,共13页
Currently,Cardiovascular Disease(CVD)remains a significant contributor to premature mortality and escalating health care expenses.Early and accurate detection is crucial for the successful treatment,intervention,and m... Currently,Cardiovascular Disease(CVD)remains a significant contributor to premature mortality and escalating health care expenses.Early and accurate detection is crucial for the successful treatment,intervention,and monitoring of heart health.Electrocardiograms(ECGs)are essential for diagnosing and monitoring cardiovascular diseases.However,the increasing demand for ECG signal detection,coupled with a shortage of specialized ECG doctors,has made automatic classification and diagnosis of ECG signals a prominent research area.Traditional ECG signal classification models often involve numerous parameters,rendering them unsuitable for resource-limited IoT devices in smart healthcare scenarios.In response,this paper proposes a novel lightweight ECG signal classification network based on the Transformer model,named DCETEN.Specifically,we introduce a lightweight Efficient Channel Attention(ECA)module,integrating it with Depthwise Separable Convolution(DSC)to design a One-dimensional Convolutional Neural Network(1D-CNN)that enhances feature extraction capabilities.Additionally,we fuse hand-crafted RR interval features and features learned by the Transformer to comprehensively capture the ECG signal characteristics.Finally,to make the proposed method suitable for resource-constrained IoT-based edge devices,we employ pruning techniques to reduce the number of model parameters.We validated the proposed model on the MIT-BIH Arrhythmia Database,achieving 99.84%accuracy and a 99.67%F1 score with low computational and memory requirements,making it suitable for deployment in smart healthcare settings with prevalent resource limitations. 展开更多
关键词 Electrocardiogram Arrhythmia classification Transformer model Lightweight Weight pruning
暂未订购 下载PDF
Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
7
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
暂未订购 下载PDF
MTSCFNet:a novel framework for improving tree species classification in a subtropical forest using RGB,LiDAR-derived,and GF-2 data 认领 引用
8
作者 Linlong Wang Huaiqing Zhang +5 位作者 Rurao Fu Kexin Lei Yang Liu Tingdong Yang Jing Zhang Xiaoning Ge 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期135-158,共24页
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)... Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments. 展开更多
关键词 Tree species classification Deep learning Multi-source remote sensing data Subtropical forest
暂未订购 下载PDF
High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
9
作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
暂未订购 下载PDF
Beach morphodynamic characteristics and classifications around Hainan Island,China 认领 引用
10
作者 HU Taihuan CHEN Shenliang +4 位作者 ZHONG Xiaojing JI Hongyu SANG Wenxiu LI Peng XU Wei 《Journal of Geographical Sciences》 SCIE CSCD 2026年第2期449-470,共22页
Climate change and anthropogenic activities have profoundly affected coastal systems,making geomorphological research a critical focus for coastal protection and sustainable development.In this study,a comprehensive c... Climate change and anthropogenic activities have profoundly affected coastal systems,making geomorphological research a critical focus for coastal protection and sustainable development.In this study,a comprehensive classification of beach states around Hainan Island is conducted for the first time by utilizing theΩ-RTR model and geological control modes.Six distinct classic beach states ranging from dissipative to reflective are identified:barred dissipative beaches or no-barred dissipative beaches(BD or NBD),barred beaches(B),low-tide terrace or low-tide bar with rip(LTTR or LTBR),and reflective state(R).Among these,the BD and B types are predominant on Hainan Island.Notably,the beach states are subject to multiple factors,such as hydrodynamic forcings,geomorphic features and underlying substrates,and exhibit remarkable spatiotemporal variability.During extreme events,hydrodynamic forcings impact beach states more substantially than geological and geomorphic features do,leading to a more homogeneous distribution of beach states.Under normal circumstance,beach states are predominantly controlled by geological and geomorphic features.Coastal geological and geomorphic features have a pronounced influence on beach morphology and stability.For example,hard substrates underpin wide and stable dissipative beaches,whereas softer substrates lead to narrower,erosion-prone beaches.Three geological control modes are identified,namely,gently sloping hard substrates with dissipative beaches,moderately sloping hard substrates with seasonally variable reflective beaches,and steeply sloping soft substrates with dynamic sandbar-dominated beaches.These findings highlight the necessity of integrating geological settings in tandem with hydrodynamic forcings into coastal management practices.A dual-mode strategy is proposed:maintaining geomorphic self-organization on hard-substrate coasts under normal conditions and implementing hybrid engineering–ecological measures(e.g.,artificial sand replenishment and vegetation restoration)on erosion-prone soft substrates. 展开更多
关键词 beach morphodynamic classification Ω-RTR model underlying substrate geological control extreme climate coastal management
暂未订购 下载PDF
Enhancing Surface Water Classification:Integrating Time Series Features and Automated Sampling on Google Earth Engine 认领 引用
11
作者 FU Yi YAO Yunlong +3 位作者 WANG Lei SHAN Yuanqi LI Weineng LIU Yuna 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第2期337-350,I0007,共14页
Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus pr... Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data. 展开更多
关键词 surface water mapping machine learning classification performance Sentinel-2 Google Earth Engine(GEE) Songnen Plain,China
暂未订购 下载PDF
Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning 认领 引用
12
作者 Dingran Song Feng Dai +2 位作者 Yi Liu Hao Tan Mingdong Wei 《Engineering》 SCIE EI CSCD 2026年第1期267-283,共17页
Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe ... Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe noise interference,and data scarcity.To address these issues,this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework(LRE-UDAF)for cross-domain MS signal classification.The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation(UDA)module utilizing a bi-classifier disparity metric and entropy regularization.The feature extractor derives high-level representations from the preprocessed signals,which are subsequently fed into two classifiers to predict class probability.Through three-stage adversarial learning,the feature extractor and classifiers progressively align the distributions of the source and target domains,facilitating knowledge transfer from the labeled source to the unlabeled target domain.Source-domain experiments reveal that the feature extractor achieves high effectiveness,with a classification accuracy of up to 97.7%.Moreover,LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness.Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals.Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules.This framework presents an efficient solution for resource-constrained,noise-prone,and data-scarce environments in deep underground engineering,offering significant promise for practical implementations in early disaster warning. 展开更多
关键词 Deep learning Microseismic classification Lightweight design Noise robustness Unsupervised domain adaptation
暂未订购 下载PDF
Pre-Trained RS-VGG19 Model for Remote Sensing Natural Scene Image Classification Using Spark Distributed Framework 认领 引用
13
作者 LI Zhaofei ZHANG Yijie +2 位作者 ZHAO Na LIU Guoquan ZHENG Ruiyu 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期843-857,I0003,共15页
Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by ... Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by issues like class similarity and spatial distribution.Current convolutional neural networks rely on stacking small convolutional kernels for feature learning,which results in relatively low classification accuracy,while their dependence on centralized learning architectures with high-performance GPUs/CPUs incurs substantial training costs.Therefore,this paper proposes a distributed rapid classification method for high-similarity natural scene remote sensing images using an improved VGG19 model(RS-VGG19)that combines residual connections and attention mechanisms.By introducing residual connections,the method improves training convergence speed and high-level feature learning ability,effectively preventing gradient vanishing during training.Embedding the SENet visual attention module in the tenth convolution layer allows the model to more specifically extract similar and significant features in remote sensing images.By employing a combination of cross-entropy and center loss functions,the model is able to learn features with reduced intra-class variance and increased inter-class variance,further enhancing classification accuracy.The distributed inference framework Spark is employed for decentralized model training,storing large-scale remote sensing images in the distributed file system HDFS,and accessing the pre-trained RS-VGG19 model in Docker containers on cluster nodes for distributed inference and classification using PySpark.Experimental results show that on two commonly used high-similarity remote sensing image datasets,NWPU-RESISC45 and UCMerced Land-Use,the RS-VGG19 model improves classification accuracy by 6.57%and 8.76%respectively compared to the original VGG19 model,and significantly enhances accuracy compared to other related classification models.This demonstrates the superior performance of the proposed structure and loss function fusion strategy in remote sensing image classification tasks.On the large-scale remote sensing image inference dataset NWPU-RESISC45,while maintaining classification accuracy,the distributed inference framework achieved a speedup of 11.9 when using six nodes,an improvement of 98.33%over theoretical linear speedup(6.00),reducing dependency on high-end hardware resources and significantly improving the classification speed of high-similarity natural scene remote sensing images. 展开更多
关键词 remote sensing image VGG19 residual connection SENet visual attention Spark Docker distributed classification
暂未订购 下载PDF
Concept,technology generation classification and field practices of enhanced gas recovery 认领 引用
14
作者 WEI Yunsheng YAN Haijun +7 位作者 GUO Jianlin WANG Junlei TANG Haifa GUO Zhi QI Yadong ZHU Hanqing WANG Zhongnan GAO Yanling 《Petroleum Exploration and Development》 SCIE 2026年第2期473-488,共16页
Starting from the first principle thinking,this study systematically reviews the development mechanisms of gas reservoirs and proposes the development concept of“full life cycle enhanced gas recovery(EGR)”.Following... Starting from the first principle thinking,this study systematically reviews the development mechanisms of gas reservoirs and proposes the development concept of“full life cycle enhanced gas recovery(EGR)”.Following the principles of scientificity,practicality and comparability,a generational classification system for EGR technologies is established.The research indicates that the properties of natural gas dictate a development mechanism primarily driven by pressure depletion to release the elastic expansion energy of gas.This leads to a development model centered on primary depletion,supplemented by limited adjustments in late stages.Early development essentially lies in well pattern optimization and risk pre-control,while late development focuses on targeted local adjustments and integrated collaborative control.Primary gas recovery,relying on natural energy depletion,achieves a recovery factor of 25%–55%.Secondary gas recovery,through active regulation of the reservoir pressure field via techniques like blockage removal,and injection-production optimization,can enhance the recovery factor by 10–15 percentage points.Tertiary gas recovery,employing multiple mechanisms to alter the reservoir's physical and chemical fields synergistically,offers a potential further increase of 5–10 percentage points.Currently,primary recovery technologies are mature and well-established.Synergistic optimization of well patterns and fracture networks enables effective production from gas-drive reservoirs,while optimized development strategies facilitate orderly production from water-drive gas reservoirs.Secondary recovery technologies,in the field pilot stage currently,adopt active measures like enhanced water drainage,water shutoff,and gas injection to effectively control water influx and release trapped gas.Tertiary recovery remains largely in the laboratory or pilot test stage.Future efforts should focus on cross-generational technologies,such as“primary+secondary”and“primary+tertiary”combinations,to continuously improve recovery factors throughout the full lifecycle of gas reservoirs. 展开更多
关键词 natural gas reservoir enhanced gas recovery technology generation classification depletion development pressure field regulation physical and chemical field regulation multi-mechanism synergy
暂未订购 下载PDF
Seismic event classification in North China based on machine learning 认领 引用
15
作者 Yixiao Zhang Tingting Wang +1 位作者 Ruifeng Liu Zibo Wang 《Earthquake Science》 CAS CSCD 2026年第3期257-272,共16页
Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and e... Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and explosions are highly similar.This study compared feature-based machine learning(ML)and image-based deep learning(DL)methods in event-and stationlevel classification frameworks.The dataset consisted of 1,847 events and more than 43,000 vertical-component waveforms with two input types,40-dimensional feature vectors for ML and spectrogram images for DL.The results showed that the eventlevel models consistently outperformed the station-level models,achieving over 98%accuracy;the station-level models performed well above 94%.On the test set,the ML and DL models exhibited comparable performance;however,the ML models demonstrated better generalization and lower computational demands.In contrast,DL models required fewer manual interventions.The misclassification analysis revealed distinct error patterns across the model types,indicating potential complementarity.These findings highlight the importance of model choice based on the input type,data granularity,and generalization needs.Although DL models are well suited to automated processing,ML approaches provide more robust and efficient solutions for real-world deployment. 展开更多
关键词 seismic event classification machine learning XGBoost EfficientNet time-frequency analysis
暂未订购 下载PDF
AgroGeoDB-Net: A DBSCAN-Guided Augmentation and Geometric-Similarity Regularised Framework for GNSS Field-Road Classification in Precision Agriculture 认领 引用
16
作者 Fengqi Hao Yawen Hou +4 位作者 Conghui Gao Jinqiang Bai Gang Liu Hoiio Kong Xiangjun Dong 《Computers, Materials & Continua》 SCIE EI 2026年第6期1186-1213,共28页
Field-road classification,a fine-grained form of agricultural machinery operation-mode identification,aims to use Global Navigation Satellite System(GNSS)trajectory data to assign each trajectory point a semantic labe... Field-road classification,a fine-grained form of agricultural machinery operation-mode identification,aims to use Global Navigation Satellite System(GNSS)trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads.Existing methods struggle with highly imbalanced class distributions,noisy measurements,and intricate spatiotemporal dependencies.This paper presents AgroGeoDB-Net,a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations:(i)a Density-Aware Local Interpolator(DALI),which balances the minority road class via density-aware interpolation while preserving road-segment structure;and(ii)a geometry-aware training objective that couples a DBSCAN-weighted focal loss with a density-regularised KL divergence,ensuring that both classification and latent representations reflect local trajectory density.The workflow first converts each enriched GNSS point into a 14-dimensional motion-spatial descriptor,projects it into a compact latent space through a variational auto-encoder,and then applies a residual BiLSTM to model bidirectional temporal dependencies before a linear classifier produces point-wise field-road predictions.Experiments on wheat,corn,and paddy datasets show overall accuracies of 98.62%,95.46%,and 93.35%,with consistently stronger road class and overall performance than existing methods.Ablation studies further confirm that both the residual shortcut and DALI contribute positively,with DALI providing the greatest benefit for the minority road class.Tests on the unseen Harvester and Tractor datasets also demonstrate strong generalisation to previously unseen datasets.Taken together,the results show that AgroGeoDB-Net delivers reliable and scalable field-road classification from GNSS trajectories. 展开更多
关键词 Field-road classification imbalanced dataset spatiotemporal features deep learning
暂未订购 下载PDF
Federated Dynamic Aggregation Selection Strategy-Based Multi-Receptive Field Fusion Classification Framework for Point Cloud Classification 认领 引用
17
作者 Yuchao Hou Biaobiao Bai +3 位作者 Shuai Zhao Yue Wang Jie Wang Zijian Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期1889-1918,共30页
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva... Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment. 展开更多
关键词 Point cloud classification federated learning multi-receptive field fusion dynamic aggregation
暂未订购 下载PDF
Enhancing Epileptic Seizure Classification via Multi-Feature Fusion in a Transformer-LSTM Architecture 认领 引用
18
作者 Gaoteng Yuan Ping Qiu +3 位作者 Qika Lin Jianchu Lin Xiang Li Dongping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1134-1154,共21页
Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective interventio... Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective intervention.This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram(EEG)signals.The proposed method begins with signal preprocessing,including denoising,segmentation,and label construction.Subsequently,a comprehensive set of temporal,spectral,and wavelet-based features—such as signal mean,power,heart rate,and wavelet coefficients—is extracted.Feature selection is then performed using the Maximal Information Coefficient(MIC)to identify the most discriminative inputs.A hybrid model combining a Transformer encoder and a Long Short-Term Memory(LSTM)network is developed to effectively capture both long-range dependencies and temporal dynamics in EEG sequences for seizure classification.Evaluated on the Bonn dataset using 5-fold cross-validation,the proposed method achieves an accuracy of 96.43%in distinguishing between epileptic patients and healthy subjects,with a sensitivity of 97.53%in detecting seizure states.It also attains a multi-class classification accuracy of 90.14%across different epileptic signal types.Ablation studies confirm that MICbased feature selection improves accuracy by over 2O%compared to using raw features without selection.The results demonstrate that the integration of multi-feature analysis with the Transformer-LSTM architecture offers an effective and reliable solution for EEG-based seizure classification. 展开更多
关键词 Epileptic classification EEG signal wavelet analysis feature selection transformer-LSTM Algorithm 2:MIC algorithm feature selection
暂未订购 下载PDF
Graph Attention Networks for Skin Lesion Classification with CNN-Driven Node Features 认领 引用
19
作者 Ghadah Naif Alwakid Samabia Tehsin +3 位作者 Mamoona Humayun Asad Farooq Ibrahim Alrashdi Amjad Alsirhani 《Computers, Materials & Continua》 SCIE EI 2026年第1期1964-1984,共21页
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ... Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems. 展开更多
关键词 Graph neural network image classification DermaMNIST dataset graph representation
暂未订购 下载PDF
Addressing Background Bias in Explainable Orange Fruit Disease Classification Using Deep Learning 认领 引用
20
作者 Naeem Ullah Javed Ali Khan +3 位作者 Michelina Ruocco Antonio Della Cioppa Ivanoe De Falco Giovanna Sannino 《Computers, Materials & Continua》 SCIE EI 2026年第8期1998-2030,共33页
Fruit diseases significantly impact agricultural productivity,yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images,particularly in orange... Fruit diseases significantly impact agricultural productivity,yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images,particularly in orange fruit disease datasets.Current deep learning approaches are prone to background bias,which reduces explainability and generalization.To address this,we propose a deep learning framework that explicitly reduces background noise and bias in orange fruit disease image classification while providing interpretable,pixel-level predictions.The framework integrates existing architectural components,including grouped convolutions with channel shuffling,Leaky ReLU and clipped ReLU activations,and attention-based feature extraction,within a bias-aware design motivated by explainability analysis.The contribution lies in the problem-driven integration of these components and a background standardization preprocessing step to improve explanation reliability.A Grid Search algorithm is used to optimize the hyperparameters.Data augmentation is applied to enhance generalization.We used perceptual hashing to ensure no duplicate images existed between training and testing sets,thereby preventing data leakage and maintaining dataset integrity.For interpretability,we employ Local Interpretable Model-agnostic Explanations(LIME);however,initial explanations highlighted irrelevant background regions.To address this,we introduce a novel preprocessing step using the GrabCut algorithm and morphological operations to standardize image backgrounds,ensuring explanations focus solely on diseased regions.Unlike existing methods,our background standardization technique,based on GrabCut and white background standardization,improves the relevance of LIME explanations by reducing background-focused attributions from 62.2%to 7.7%of cases,while yielding modest,consistent improvements in classification accuracy(0.15%–0.24%).We further evaluate DeepOrangeNet’s feature extraction by classifying its learned representations using six classifiers,including linear discriminant analysis,fine decision tree,Gaussian Naive Bayes,fine k-nearest neighbors,linear support vector machine,and logistic regression,demonstrating its superior adaptability.DeepOrangeNet has been compared with the state-of-the-art methods,proving not only its accuracy but also its explainable and lightweight architecture for real-world agricultural implementation. 展开更多
关键词 Background bias orange fruit disease classification agricultural productivity explainable artificial intelligence deep learning
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈