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2,914篇文章
< 1 2 … 146 >
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：
Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes 认领 引用
1
作者 Junmin Lyu Guangyu Xu +4 位作者 Feng Bao Yu Zhou Yuxin Liu Siyu Lu Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1235-1256,共22页
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati... Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks. 展开更多
关键词 GNN social networks nodes multi-label classification model graphic convolution neural network coupling principle
暂未订购 下载PDF
A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping:Physically-based probabilistic model with convolutional neural network 认领 引用 被引量：7
2
作者 Hong-Zhi Cui Bin Tong +2 位作者 Tao Wang Jie Dou Jian Ji 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第8期4933-4951,共19页
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region... Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale. 展开更多
关键词 Rainfall landslides Landslide susceptibility mapping Hybrid model Physically-based model Convolution neural network(CNN) Probability of failure(POF)
暂未订购 下载PDF
Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process 认领 引用 被引量：1
3
作者 Ping Wu Yuxuan Ni +4 位作者 Huaimin Wang Xuguang Hu Zhenquan Wu Jian Jiang Yaowu Hu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期267-276,共10页
Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on li... Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T2 statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee. 展开更多
关键词 Partial least squares Dynamic-inner convolutional autoencoder Quality-related fault detection Neural networks Safety Dynamic modeling
暂未订购 下载PDF
Curvature-driven shifts of the Potts transition on spherical Fibonacci graphs:A graph-convolutional transfer-learning study 认领 引用
4
作者 Zheng Zhou Xu-Yang Hou Hao Guo 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期495-508,共14页
We investigate the ferromagnetic q-state Potts model on spherical Fibonacci graphs.These graphs are constructed by embedding quasi-uniform sites on a sphere and defining interactions via a chord-distance cutoff chosen... We investigate the ferromagnetic q-state Potts model on spherical Fibonacci graphs.These graphs are constructed by embedding quasi-uniform sites on a sphere and defining interactions via a chord-distance cutoff chosen so as to yield a network approximating four-neighbor connectivity.By combining Swendsen-Wang cluster Monte Carlo simulations with graph convolutional networks(GCNs),which operate directly on the adjacency structure and node spins,we develop a unified phase-classification framework applicable to both regular planar lattices and curved,irregular spherical graphs.Benchmarks on planar lattices demonstrate an efficient transfer strategy:after a fixed binarization of Potts spins into an effective Ising variable,a single GCN pre-trained on the Ising model can localize the transition region for different q values without retraining.Applying this strategy to spherical graphs,we find that curvature-and defect-induced connectivity irregularities induce only modest shifts in the inferred transition temperatures relative to their planar counterparts.Further analysis shows that the curvature-induced shift of the critical temperature is most pronounced at small q and diminishes rapidly as q increases.This trend is consistent with the physical picture that,in two dimensions,the Potts model undergoes a transition from a continuous phase transition to a weakly first-order one for q>4,accompanied by a pronounced reduction in the correlation length. 展开更多
关键词 q-state Potts model spherical fibonacci graphs graph convolutional networks(GCNs) critical temperature
暂未订购 下载PDF
Tensor Low-Rank Orthogonal Compression for Convolutional Neural Networks 认领 引用
5
作者 Yaping He Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期227-229,共3页
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression... Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices. 展开更多
关键词 model compression convolutional neural network cnn which tensor low rank orthogonal compression deep neural network dnn models embedded devices convolutional neural networks
暂未订购 下载PDF
Improving Convolutional Neural Network Performance Using Alpha-Based Adaptive Pooling for Image Classification 认领 引用
6
作者 Nahdi Saubari Kunfeng Wang +1 位作者 Rachmat Muwardi Andri Pranolo 《Computers, Materials & Continua》 SCIE EI 2026年第6期1502-1525,共24页
This study proposes an Adaptive Pooling method based on an alpha(α)parameter to enhance the effectiveness and stability of convolutional neural networks(CNNs)in image classification tasks.Conventional pooling techniq... This study proposes an Adaptive Pooling method based on an alpha(α)parameter to enhance the effectiveness and stability of convolutional neural networks(CNNs)in image classification tasks.Conventional pooling techniques,such as max pooling and average pooling,often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity.To address this limitation,the proposed approach introduces anαparameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework.The proposed method is evaluated using two benchmark datasets,MNIST and CIFAR-10,representing grayscale and color image classification scenarios,respectively.Experiments are conducted across three CNN families with different depths LeNet-5,a deeper custom-built CNN,and ResNet-18 to assess robustness under varying representational capacity.Under the bestαsetting with a 4×4 pooling configuration,Adaptive Pooling exhibits architecture-dependent behavior.On LeNet-5,Adaptive Pooling achieves 87.2%on MNIST and 30.1%on CIFAR-10,compared with 97.8%(max/average pooling)on MNIST and 60.1%(max pooling)/53.9%(average pooling)on CIFAR-10.In contrast,on the deeper custom CNN,Adaptive Pooling becomes competitive,reaching 99.7%on MNIST and 86.1%on CIFAR-10,which is comparable to 99.6%-99.7%on MNIST and 84.5%-86.2%on CIFAR-10 achieved by conventional pooling.On ResNet-18,Adaptive Pooling attains 99.1%on MNIST,while CIFAR-10 performance decreases to 37.2%relative to the default global average pooling baseline(99.7%on MNIST and 89.0%on CIFAR-10),suggesting that performance also depends on where the pooling replacement is applied.Overall,these findings indicate thatα-controlled Adaptive Pooling provides a lightweight and configurable pooling strategy that can improve stability and achieve competitive accuracy in deeper CNNs,although it should be treated as a complementary mechanism rather than a universal replacement across all architectures. 展开更多
关键词 Adaptive pooling alpha parameter convolutional neural network(CNN) image classification MNIST CIFAR-10 model generalization
暂未订购 下载PDF
MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction 认领 引用 被引量：2
7
作者 Xinlu Zong Fan Yu +1 位作者 Zhen Chen Xue Xia 《Computers, Materials & Continua》 SCIE EI 2025年第2期3517-3537,共21页
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ... Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks. 展开更多
关键词 Graph convolutional network traffic flow prediction multi-scale traffic flow spatial-temporal model
暂未订购 下载PDF
Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model 认领 引用 被引量：6
8
作者 CAI Yingfeng WANG Hai +2 位作者 CHEN Xiaobo GAO Li CHEN Long 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第4期765-772,共8页
Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high ... Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81%correct rate and 0.78%false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle. 展开更多
关键词 vehicle detection visual saliency deep model convolution neural network
暂未订购 下载PDF
Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling 认领 引用 被引量：10
9
作者 Feng Hua Zhou Fang Tong Qiu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2018年第12期2562-2572,共11页
System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On... System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure.After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework. 展开更多
关键词 Convolutional neural network Network motif Naphtha pyrolysis Kinetic modeling
暂未订购 下载PDF
Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model 认领 引用 被引量：6
10
作者 LIU Yueming YANG Xiaomei +3 位作者 WANG Zhihua LU Chen LI Zhi YANG Fengshuo 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2019年第6期1941-1954,共14页
Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area... Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts. 展开更多
关键词 aquaculture area vulnerability assessment Richer Convolutional Features(RCF)network model deep learning high-resolution remote sensing
暂未订购 下载PDF
Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks 认领 引用
11
作者 Wasim Khan Afsaruddin Mohd +3 位作者 Mohammad Suaib Mohammad Ishrat Anwar Ahamed Shaikh Syed Mohd Faisal 《Data Science and Management》 EI CSCD 2025年第2期137-146,共10页
In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study in... In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks. 展开更多
关键词 Anomaly detection Deep learning Hypersphere learning Residual modeling Graph convolution network Attention mechanism
暂未订购 下载PDF
Hierarchical Shape Pruning for 3D Sparse Convolution Networks 认领 引用
12
作者 Haiyan Long Chonghao Zhang +2 位作者 Xudong Qiu Hai Chen Gang Chen 《Computers, Materials & Continua》 SCIE EI 2025年第8期2975-2988,共14页
3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems,enabling selective feature extraction from non-empty voxels while suppressing computational waste.Des... 3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems,enabling selective feature extraction from non-empty voxels while suppressing computational waste.Despite its theoretical efficiency advantages,practical implementations face under-explored limitations:the fixed geometric patterns of conventional sparse convolutional kernels inevitably process non-contributory positions during sliding-window operations,particularly in regions with uneven point cloud density.To address this,we propose Hierarchical Shape Pruning for 3D Sparse Convolution(HSP-S),which dynamically eliminates redundant kernel stripes through layer-adaptive thresholding.Unlike static soft pruning methods,HSP-S maintains trainable sparsity patterns by progressively adjusting pruning thresholds during optimization,enlarging original parameter search space while removing redundant operations.Extensive experiments validate effectiveness of HSP-S acrossmajor autonomous driving benchmarks.On KITTI’s 3D object detection task,our method reduces 93.47%redundant kernel computations whilemaintaining comparable accuracy(1.56%mAP drop).Remarkably,on themore complexNuScenes benchmark,HSP-S achieves simultaneous computation reduction(21.94%sparsity)and accuracy gains(1.02%mAP(mean Average Precision)and 0.47%NDS(nuScenes detection score)improvement),demonstrating its scalability to diverse perception scenarios.This work establishes the first learnable shape pruning framework that simultaneously enhances computational efficiency and preserves detection accuracy in 3D perception systems. 展开更多
关键词 Shape pruning model compressing 3D sparse convolution
暂未订购 下载PDF
Optimization of convolutional neural networks for predicting water pollutants using spectral data in the middle and lower reaches of the Yangtze River Basin,China 认领 引用
13
作者 ZHANG Guohao LI Song +3 位作者 WANG Cailing WANG Hongwei YU Tao DAI Xiaoxu 《Journal of Mountain Science》 SCIE CSCD 2025年第8期2851-2869,共19页
Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising t... Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control. 展开更多
关键词 Water pollutants Convolutional neural networks Data augmentation Optimization algorithms Model evaluation methods Deep Learning
暂未订购 下载PDF
Deep Convolution Neural Networks for Image-Based Android Malware Classification 认领 引用
14
作者 Amel Ksibi Mohammed Zakariah +1 位作者 Latifah Almuqren Ala Saleh Alluhaidan 《Computers, Materials & Continua》 SCIE EI 2025年第3期4093-4116,共24页
The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ... The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future. 展开更多
关键词 Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model
暂未订购 下载PDF
PML Absorbing Boundary Condition for Seismic Numerical Modeling by Convolutional Differentiator in Fluid-Saturated Porous Media 认领 引用 被引量：3
15
作者 李信富 《Journal of Earth Science》 SCIE EI CAS CSCD 2011年第3期377-385,共9页
The perfectly matched layer（PML） was first introduced by Berenger as an absorbing boundary condition for electromagnetic wave propagation.In this article,a method is developed to ex-tend the PML to simulating seismi... The perfectly matched layer（PML） was first introduced by Berenger as an absorbing boundary condition for electromagnetic wave propagation.In this article,a method is developed to ex-tend the PML to simulating seismic wave propagation in fluid-saturated porous medium.This non-physical boundary is used at the computational edge of a Forsyte polynomial convolutional differenti-ator（FPCD） algorithm as an absorbing boundary condition to truncate unbounded media.The incor-poration of PML in Biot＇s equations is given.Numerical results show that the PML absorbing bound-ary condition attenuates the outgoing waves effectively and eliminates the reflections adequately. 展开更多
关键词 seismic wave numerical modeling convolutional differentiator PML absorbing boundary condition fluid-saturated porous medium.
暂未订购 下载PDF
Micro-expression recognition algorithm based on graph convolutional network and Transformer model 认领 引用 被引量：1
16
作者 吴进 PANG Wenting +1 位作者 WANG Lei ZHAO Bo 《High Technology Letters》 EI CAS 2023年第2期213-222,共10页
Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most ... Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%. 展开更多
关键词 micro-expression recognition graph convolutional network(GCN) action unit(AU)detection Transformer model
暂未订购 下载PDF
Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model 认领 引用
17
作者 Jian Liu Xiaodong Xia +2 位作者 Chunyang Han Jiao Hui Jim Feng 《Computers, Materials & Continua》 SCIE EI 2022年第10期265-278,共14页
As a common and high-risk type of disease,heart disease seriously threatens people’s health.At the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical... As a common and high-risk type of disease,heart disease seriously threatens people’s health.At the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases.Therefore,the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases.In this paper,we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network(CNN)and Encoder-Decoder model.The model uses Long Short-Term Memory(LSTM)to consider the influence of time series features on classification results.Simultaneously,it is trained and tested by the MIT-BIH arrhythmia database.Besides,Generative Adversarial Networks(GAN)is adopted as a method of data equalization for solving data imbalance problem.The simulation results show that for the inter-patient arrhythmia classification,the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy,of which the accuracy can reach 94.05%.Especially,it has a better advantage for the classification effect of supraventricular ectopic beats(class S)and fusion beats(class F). 展开更多
关键词 Electroencephalography convolutional neural network long short-term memory encoder-decoder model generative adversarial network
暂未订购 下载PDF
Convolutional BiLSTM Variational Sequence-To-Sequence Based Video Captioning for Capturing Intricate Temporal Dependencies 认领 引用 被引量：1
18
作者 M.Gowri Shankar D.Surendran 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2700-2716,共17页
In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solu... In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory(BiLSTM)constructed Variational Sequence-to-Sequence(CBVSS)approach.The proposed framework is adept at capturing intricate temporal dependencies within video sequences,enabling a more nuanced and contextually relevant description of dynamic scenes.However,optimizing its parameters for improved performance remains a crucial challenge.In response,in this research Golden Eagle Optimization(GEO)a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters.The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions.The proposed attains an overall higher Recall of 59.75%and Precision of 63.78%for both datasets.Additionally,the proposed CBVSS method demonstrated superior performance across both datasets,achieving the highest METEOR(25.67)and CIDER(39.87)scores on the ActivityNet dataset,and further outperforming all compared models on the YouCook2 dataset with METEOR(28.67)and CIDER(43.02),highlighting its effectiveness in generating semantically rich and contextually accurate video captions. 展开更多
关键词 Video captioning Convolutional BiLSTM Variational sequence-to-sequence model Golden eagleoptimization Intricate temporal dependencies
Improvement of Emotion Detection by Fusing Speech and Image Based on CNN with Temporal Models 认领 引用
19
作者 Shing-Tai Pan Yi-Zhen Huang Zhi-Qing Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期1677-1697,共21页
This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition.The principal contribution lies in extending the visual component from single... This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition.The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition.Specifically,the proposed framework employs an InceptionV3 Convolutional Neural Network(CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance.These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory(LSTM)or Hidden Markov Model(HMM)for temporal modeling.For the speech modality,Mel-Frequency Cepstral Coefficients(MFCC)or filter bank features are extracted from processed audio signals and fed into a hybrid CNN-time-series model.The two modalities are then integrated through model-level and decision-level fusion strategies.Since recognition accuracy tends to degrade as the number of utterances and speakers increases,the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS),which contains a moderate number of sentences and speakers,is adopted in this study.Experimental results demonstrate that the proposed multi-image approach improves recognition accuracy from 91%to 96%compared with the single-image baseline,and that the multimodal fusion framework consistently outperforms its single-modal counterpart. 展开更多
关键词 Speech emotion recognition consecutive facial image emotion recognition convolutional neural network(CNN) long short-term memory(LSTM) hidden markov model(HMM) support vector machine(SVM)
暂未订购 下载PDF
Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group 认领 引用 被引量：3
20
作者 Yadong Xu Weixing Hong +3 位作者 Mohammad Noori Wael A.Altabey Ahmed Silik Nabeel S.D.Farhan 《Structural Durability & Health Monitoring》 EI 2024年第6期763-783,共21页
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb... This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure. 展开更多
关键词 Structural Health Monitoring(SHM) bridges big model Convolutional Neural Network(CNN) Finite Element Method(FEM)
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 … 146 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈