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1,192篇文章
< 1 2 … 60 >
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：
3D Data Scattergram Image Classification Based Protection for Transmission Line Connecting BESS Using Depth-wise Separable Convolution Based CNN 认领 引用 被引量：1
1
作者 Yingyu Liang Yi Ren +1 位作者 Xiaoyang Yang Wenting Zha 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2025年第2期609-621,共13页
The distinctive fault characteristics of battery energy storage stations(BESSs)significantly affect the reliability of conventional protection methods for transmission lines.In this paper,the three-dimensional(3D)data... The distinctive fault characteristics of battery energy storage stations(BESSs)significantly affect the reliability of conventional protection methods for transmission lines.In this paper,the three-dimensional(3D)data scattergrams are constructed using current data from both sides of the transmission line and their sum.Following a comprehensive analysis of the varying characteristics of 3D data scattergrams under different conditions,a 3D data scattergram image classification based protection method is developed.The depth-wise separable convolution is used to ensure a lightweight convolutional neural network(CNN)structure without compromising performance.In addition,a Bayesian hyperparameter optimization algorithm is used to achieve a hyperparametric search to simplify the training process.Compared with artificial neural networks and CNNs,the depth-wise separable convolution based CNN(DPCNN)achieves a higher recognition accuracy.The 3D data scattergram image classification based protection method using DPCNN can accurately separate internal faults from other disturbances and identify fault phases under different operating states and fault conditions.The proposed protection method also shows first-class tolerability against current transformer(CT)saturation and CT measurement errors. 展开更多
关键词 Convolutional neural network(CNN) battery energy storage station(BESS) depth-wise separable convolution hyperparameter optimization fault classification line protection
Underwater Image Enhancement Based on Depthwise Separable Convolution-Based Generative Adversarial Network 认领 引用
2
作者 ZENG Jun-yang SI Zhan-jun 《印刷与数字媒体技术研究》 CAS 北大核心 2026年第1期60-66,共7页
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver... The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics. 展开更多
关键词 Underwater image enhancement Generating adversarial network Depthwise separable convolution
暂未订购 下载PDF
SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis 认领 引用 被引量：4
3
作者 Hongxing Wang Xilai Ju +1 位作者 Hua Zhu Huafeng Li 《Computers, Materials & Continua》 SCIE EI 2025年第1期1417-1437,共21页
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine... Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment. 展开更多
关键词 CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis
暂未订购 下载PDF
A depth-wise separable residual neural network for PCDH8 status prediction in thyroid cancer pathological images 认领 引用 被引量：1
4
作者 Linlin Qi Xiangyu Li +2 位作者 Zhihong Liu Pei Zhang Liangliang Liu 《Intelligent Oncology》 2025年第4期290-298,共9页
Introduction:Accurate prediction of protocadherin 8(PCDH8)gene expression status from whole-slide images(WSIs)is critical for thyroid cancer diagnosis and prognosis,as PCDH8 overexpression is associated with tumor agg... Introduction:Accurate prediction of protocadherin 8(PCDH8)gene expression status from whole-slide images(WSIs)is critical for thyroid cancer diagnosis and prognosis,as PCDH8 overexpression is associated with tumor aggressiveness and poor outcomes.Existing methods for PCDH8 detection are often costly,time-consuming,or require specialized expertise.To address these limitations,we developed a novel depth-wise separable residual neural network(DSRNet)for noninvasive PCDH8 status prediction directly from WSIs.Materials and methods:We collected 403 thyroid cancer WSIs from The Cancer Genome Atlas(TCGA),with PCDH8 expression status classified as high or low based on median expression values.Each WSI was divided into 512×512 pixel tiles,with the top 100 non-white tiles selected per slide.DSRNet integrates depth-wise separable convolutions,residual connections,and a deformable convolutional pyramid pooling module to efficiently capture multiscale and long-range features in gigapixel WSIs.The model was trained using tenfold cross-validation.Results:DSRNet achieved state-of-the-art performance with 92.76%accuracy,91.92%precision,92.69%recall,and 0.93 area under the curve on the thyroid cancer dataset(TCGA-THCA),significantly outperforming leading convolutional neural networks and Transformer models.Ablation studies confirmed the contributions of each component,and attention visualization showed that DSRNet focuses on biologically relevant regions.The model also generalized well to a breast cancer dataset(TCGA-BRCA),achieving 89.13%accuracy.Conclusions:We developed DSRNet,a deep learning-based model for predicting PCDH8 status directly from routine hematoxylin and eosin-stained pathological images.DSRNet combines the efficiency of convolutional operations with enhanced long-range dependency modeling,providing a noninvasive,accurate,and interpretable tool for auxiliary thyroid cancer diagnosis and prognosis.The results demonstrate its strong potential for clinical translation,though further multicenter validation is warranted. 展开更多
关键词 Thyroid cancer Biomarker Whole-slide image Depth-wise separable convolution Residual mechanism
暂未订购 下载PDF
Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3 认领 引用 被引量：10
5
作者 Yue-Yan Qin Jiang-Tao Cao Xiao-Fei Ji 《International Journal of Automation and computing》 CSCD 2021年第2期300-310,共11页
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee... Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate. 展开更多
关键词 Fire detection depthwise separable convolution fire classification You Only Look Once version 3(YOLOv3) target regression
暂未订购 下载PDF
Validation Research on the Application of Depthwise Separable Convolutional Al Facial Expression Recognition in Non-pharmacological Treatment of BPSD 认领 引用
6
作者 Xiangyu Liu 《Journal of Clinical and Nursing Research》 2021年第4期31-37,共7页
One of the most obvious clinical reasons of dementia or The Behavioral and Psychological Symptoms of Dementia(BPSD)are the lack of emotional expression,the increased frequency of negative emotions,and the impermanence... One of the most obvious clinical reasons of dementia or The Behavioral and Psychological Symptoms of Dementia(BPSD)are the lack of emotional expression,the increased frequency of negative emotions,and the impermanence of emotions.Observing the reduction of BPSD in dementia through emotions can be considered effective and widely used in the field of non-pharmacological therapy.At present,this article will verify whether the image recognition artificial intelligence(AI)system can correctly reflect the emotional performance of the elderly with dementia through a questionnaire survey of three professional elderly nursing staff.The ANOVA(sig.=0.50)is used to determine that the judgment given by the nursing staff has no obvious deviation,and then Kendall's test(0.722**)and spearman's test(0.863**)are used to verify the judgment severity of the emotion recognition system and the nursing staff unanimously.This implies the usability of the tool.Additionally,it can be expected to be further applied in the research related to BPSD elderly emotion detection. 展开更多
关键词 Depth-wise separable convolution Emotion BPSD Dementia Nursing
暂未订购 下载PDF
MSSTNet:Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention 认领 引用
7
作者 Changchen ZHAO Hongsheng WANG Yuanjing FENG 《虚拟现实与智能硬件（中英文）》 EI 2023年第2期124-141,共18页
Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale regi... Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction. 展开更多
关键词 Remote photoplethysmography Heart rate Separable spatiotemporal convolution Dimension separable attention Multi-scale Neural network
暂未订购 下载PDF
Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder 认领 引用
8
作者 Zhongmei Wang Min Li +2 位作者 Jing He Jianhua Liu Lin Jia 《Journal of Transportation Technologies》 2024年第2期137-160,共24页
In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di... In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set. 展开更多
关键词 Equipment Health Prognostics Remaining Useful Life Prediction Pulse Separable Convolution Attention Mechanism Transformer Encoder
暂未订购 下载PDF
Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight 认领 引用
9
作者 Zenglun Guan Murad S.Alfarzaeai +2 位作者 Eryi Hu Taqiaden Alshmeri Wang Peng 《Computers, Materials & Continua》 SCIE EI 2024年第4期279-306,共28页
In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using new... In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value. 展开更多
关键词 Coal coal gangue convolutional neural network CNN object classification volume estimation separation system
暂未订购 下载PDF
SepFE:Separable Fusion Enhanced Network for Retinal Vessel Segmentation 认领 引用 被引量：2
10
作者 Yun Wu Ge Jiao Jiahao Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第9期2465-2485,共21页
The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation... The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct fusion.Finally,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods. 展开更多
关键词 Retinal vessel segmentation U-Net depth-wise separable convolution feature fusion
暂未订购 下载PDF
Maximum Likelihood Blind Separation of Convolutively Mixed Discrete Sources 认领 引用 被引量：1
11
作者 辜方林 张杭 朱德生 《China Communications》 SCIE CSCD 2013年第6期60-67,共8页
In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation proce... In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation procedure of the EM algorithm with a less computational load,the algorithm named Iterative Maximum Likelihood algorithm(IML) is proposed to calculate the likelihood and recover the source signals.An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter.Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources.Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures.Furthermore,the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters. 展开更多
关键词 Blind Source Separation convolutive mixture EM Finite Alphabet
暂未订购 下载PDF
A WEIGHTED GENERAL DISCRETE FOURIER TRANSFORM FOR THE FREQUENCY-DOMAIN BLIND SOURCE SEPARATION OF CONVOLUTIVE MIXTURES 认领 引用 被引量：1
12
作者 Wang Chao Fang Yong Feng Jiuchao 《Journal of Electronics(China)》 2008年第6期830-833,共4页
This letter deals with the frequency domain Blind Source Separation of Convolutive Mixtures (CMBSS). From the frequency representation of the "overlap and save", a Weighted General Discrete Fourier Transform... This letter deals with the frequency domain Blind Source Separation of Convolutive Mixtures (CMBSS). From the frequency representation of the "overlap and save", a Weighted General Discrete Fourier Transform (WGDFT) is derived to replace the traditional Discrete Fourier Transform (DFT). The mixing matrix on each frequency bin could be estimated more precisely from WGDFT coefficients than from DFT coefficients, which improves separation performance. Simulation results verify the validity of WGDFT for frequency domain blind source separation of convolutive mixtures. 展开更多
关键词 Blind Source Separation of Convolutive Mixtures （CMBSS） Frequency representation of overlap and save Weighted General Discrete Fourier Transform （WGDFT）
暂未订购 下载PDF
AN NMF ALGORITHM FOR BLIND SEPARATION OF CONVOLUTIVE MIXED SOURCE SIGNALS WITH LEAST CORRELATION CONSTRAINS 认领 引用
13
作者 Zhang Ye Fang Yong 《Journal of Electronics(China)》 2009年第4期557-563,共7页
Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are non- negative and mutual stati... Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are non- negative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic com- putations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm. 展开更多
关键词 Nonnegative matrix factorization Convolutive blind source separation Correlation constrain
暂未订购 下载PDF
A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification 认领 引用 被引量：2
14
作者 Adama Dembele Ronald Waweru Mwangi Ananda Omutokoh Kube 《Journal of Computer and Communications》 2024年第2期173-200,共28页
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso... Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline. 展开更多
关键词 MobileNet Image Classification Lightweight Convolutional Neural Network Depthwise Dilated Separable Convolution Hierarchical Multi-Scale Feature Fusion
暂未订购 下载PDF
A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines 认领 引用
15
作者 Qi Wang Zhaoying Liu +3 位作者 Ting Zhang Shanshan Tu Yujian Li Muhammad Waqas 《Journal on Artificial Intelligence》 2022年第1期37-48,共12页
Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classifi... Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification.However,the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters.To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters,this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines(LC-CKMSVM).The framework consists of a feature extraction module and a classification module.The feature extraction module first maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through cross-connections;then,it uses depthwise separable convolution to replace part of the original convolution to reduce the number of parameters in the module;The classification module uses a soft margin support vector machine for classification.The results on 6 different visual datasets show that LC-CKMSVM obtains better classification accuracies on most cases than the other five models. 展开更多
关键词 Convolutional neural network cross-connected lightweight framework depthwise separable convolution
暂未订购 下载PDF
一种基于多尺度特征的船舶图像去模糊算法 认领 引用 被引量：1
16
作者 郑元洲 秦瑞朋 +2 位作者 柳镭 李果 侯文波 《武汉理工大学学报（交通科学与工程版）》 2026年第2期462-469,共8页
文中提出了一种基于多尺度特征的船舶图像去模糊算法,旨在提高船舶图像的清晰度和质量.利用长江大桥和长江二桥上的桥基摄像头采集船舶图像,制作去模糊SID数据集,提出基于U-Net算法框架的去模糊网络MMF-UNet,并针对网络传递过程中的信... 文中提出了一种基于多尺度特征的船舶图像去模糊算法,旨在提高船舶图像的清晰度和质量.利用长江大桥和长江二桥上的桥基摄像头采集船舶图像,制作去模糊SID数据集,提出基于U-Net算法框架的去模糊网络MMF-UNet,并针对网络传递过程中的信息损失进行优化,提出了多尺度图像生成模块MIGM、多尺度特征提取模块MFEM、多尺度特征融合模块MFFM与多感受野感知模块MFPM,得到完整的去模糊网络,并与DeblurGAN-V2方法、DMPHN方法和MIMO-UNet方法进行对比实验,实验结果表明MMF-UNet有效提高了船舶模糊图像去模糊的效果,且具有一定的泛用性. 展开更多
关键词 U-Net 空洞卷积 可分离卷积 去模糊
暂未订购 下载PDF
基于SegFormer-CG的煤矸石识别技术 认领 引用 被引量：1
17
作者 王妍玮 陶文彬 +2 位作者 陈凯云 孟祥林 张玉 《煤炭学报》 EI CAS CSCD 北大核心 2026年第4期2771-2782,共12页
煤矸石分选机器人对煤矿智能化发展意义重大,煤矸石识别是煤矸石分选机器人的核心技术,针对传统识别技术在面对高噪声、运动模糊等复杂工况时,存在识别效率低、准确性不足的问题,提出一种基于SegFormer-CG的煤矸石识别技术,以提升识别... 煤矸石分选机器人对煤矿智能化发展意义重大,煤矸石识别是煤矸石分选机器人的核心技术,针对传统识别技术在面对高噪声、运动模糊等复杂工况时,存在识别效率低、准确性不足的问题,提出一种基于SegFormer-CG的煤矸石识别技术,以提升识别的实时性和准确率。该模型的编码器采用SegFormer的Transformer架构提取多尺度特征,并以轻量级的MiT-B0作为编码器,解码器设计融合模块以增强语义分割性能。在解码器的C1、C2、C3特征图后引入瓶颈模块(Bottleneck)增强模型特征提取能力,并采用深度可分离卷积(Depthwise Separable Convolution,DSConv)与全维度动态卷积(Omni-Dimensional Dynamic Convolution,ODConv)改进瓶颈模块,降低参数量与计算量;同时在C4特征图引入空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块,并采用深度可分离卷积和5×5卷积对ASPP改进,提升模型多尺度融合能力;在C3、C4特征图后加入交叉注意力机制(Criss-Cross Attention,CCA)使模型聚焦于关键信息,增强模型关键特征提取能力。训练采用2阶段迁移学习策略,先冻结主干网络进行50轮特征适配训练,再解冻全局参数进行优化,有效增强模型对煤矸石图像的泛化能力。结果表明:SegFormer-CG模型的精确率达到96.39%,召回率达到96.29%,平均交并比达到93.03%,相较原模型精确率提升1.32%,召回率提升0.59%,平均交并比提升1.73%。参数量为5.14×106,浮点计算量为5.90×109,帧率为50.92帧/s。与其他常见模型如PSPNet、DeepLabV3+和UNet对比,SegFormer-CG模型均取得更优秀的识别效果,且在参数量、浮点计算量上都有明显优势,在加噪、运动模糊和低光照的复杂工况下仍保持稳定识别效果,且对新疆、陕西矿区样本具有泛化能力,为选矸机器人高效识别提供了可靠技术支持。 展开更多
关键词 煤矸石识别 瓶颈模块 深度可分离卷积 注意力机制 迁移学习
暂未订购 下载PDF
基于深度可分离卷积与注意力的SSD目标检测模型 认领 引用 被引量：1
18
作者 卜子渝 杨哲 刘纯平 《计算机应用与软件》 北大核心 2026年第1期149-157,共9页
SSD是基于深度学习的单阶段目标检测模型,但其特征金字塔中的特征图缺乏多尺度信息融合,导致对中小型目标识别效果不佳。针对该问题,提出一种基于注意力机制与深度可分离卷积的SSD目标检测模型(Attention&DSC Single Shot MultiBox ... SSD是基于深度学习的单阶段目标检测模型,但其特征金字塔中的特征图缺乏多尺度信息融合,导致对中小型目标识别效果不佳。针对该问题,提出一种基于注意力机制与深度可分离卷积的SSD目标检测模型(Attention&DSC Single Shot MultiBox Detector,AD-SSD)。AD-SSD首先归一化融合特征金字塔中的特征图,再引入注意力机制加强对目标信息的表征,并采用深度可分离卷积降低参数量。该方法提高了SSD的检测精度的同时,还加快了检测速度。在PASCAL VOC07+12数据集中,AD-SSD获得了81.7%的平均精度(mAP),小型目标精度提高6.3百分点,中型目标精度提高6.4百分点,检测速度达到55.1 FPS。 展开更多
关键词 目标检测 注意力机制 深度可分离卷积 特征金字塔
暂未订购 下载PDF
轻量化输电线路缺陷检测方法 认领 引用 被引量：2
19
作者 黄萍 李清 +3 位作者 邱海枫 王程斯 黄安子 樊龙 《计算机应用》 CSCD 北大核心 2026年第3期969-979,共11页
作为电力系统的核心输配电载体,高压输电线路的运行状态直接关系到电网安全。针对传统人工巡检效率低和漏检率高的问题,提出一种基于两阶段多模态注意力机制与动态特征解耦的轻量化输电线路缺陷检测方法。在第一阶段,基于改进型轻量检... 作为电力系统的核心输配电载体,高压输电线路的运行状态直接关系到电网安全。针对传统人工巡检效率低和漏检率高的问题,提出一种基于两阶段多模态注意力机制与动态特征解耦的轻量化输电线路缺陷检测方法。在第一阶段,基于改进型轻量检测网络Light-YOLO实现关键组件的精准定位;在第二阶段,构建基于双分支对比学习的缺陷检测网络Dual-DifferNet实现缺陷的精确分类与识别。在Light-YOLO的设计中,引入分层可分离视觉Transformer(SepViT)与深度可变形卷积网络(DCN)的混合结构,并通过交替堆叠局部感知卷积层与全局注意力Transformer块,在降低计算量的同时,增强模型对长程依赖关系的建模能力,从而有效提升绝缘子和导线接头等小目标的检测精度。针对缺陷分类任务,Dual-DifferNet采用双分支结构在每个分支中嵌入空间-通道双重注意力(SCDA)模块,利用交叉注意力机制促进双模态特征交互,从而提高缺陷识别的鲁棒性与泛化能力。实验结果表明,所提方法的平均精度均值(mAP@50)达到了96.9%,较基准模型YOLOv8提升16.1个百分点,同时浮点运算量降低了56.73%,充分验证了该方法在保证高精度检测的同时,具备优异的计算效率与部署潜力。 展开更多
关键词 输电线路缺陷检测 分层可分离视觉Transformer 双向递归特征金字塔网络 双重注意力 可变形卷积
暂未订购 下载PDF
基于关键特征传递的KFT-GAN水下图像增强模型 认领 引用 被引量：1
20
作者 麦仁贵 王骥 《电子测量与仪器学报》 EI CSCD 北大核心 2026年第2期76-85,共10页
针对水体对光的吸收和散射等现象导致的水下图像出现色偏、模糊和细节丢失等问题,提出了基于关键特征传递的KFT-GAN水下图像增强模型。设计了KFT关键特征传递模块,解决了色彩、边缘和纹理等关键特征在网络中高效传递的问题,并结合深度... 针对水体对光的吸收和散射等现象导致的水下图像出现色偏、模糊和细节丢失等问题,提出了基于关键特征传递的KFT-GAN水下图像增强模型。设计了KFT关键特征传递模块,解决了色彩、边缘和纹理等关键特征在网络中高效传递的问题,并结合深度可分离卷积构建了轻量化的网络模型,与使用普通卷积构建的模型相比,参数量降低了60.6%,增强了模型的学习效率。同时该模块有利于生成器网络的编码阶段从输入图像中获取充足的关键特征,并通过下采样和跳跃连接将提取的关键特征传递至解码阶段进行图像重建,增强了重建图像的质量。另外,基于感知损失原理提出了混合损失函数,同时强调图像的多个关键属性,获得了视觉质量更好的图像效果。模型在EUVP和UIEB数据集上均取得了较好的性能,PSNR值分别为21.3842和18.0256,SSIM值分别为0.7413和0.6889,通过与传统算法和深度学习算法进行定性和定量的对比实验都证明了该模型的有效性和优越性。 展开更多
关键词 KFT-GAN 关键特征传递模块 深度可分离卷积 混合损失函数
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 … 60 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈