检索结果-南通市图书馆

作者： Xin LI Bin JIAO Wei-tian LIN School of Electrical Engineering Shanghai Dianji University

Based on the problem that the traditional motor fault diagnosis method relies on the signal processing power and the model generalization ability is poor,this paper proposes a fault diagnosis method based on generative adversarial network under unbalanced data sets.It builds a small sample training set to train generative adversarial network,and adds the generated sample to the original small sample training set.A deep convolutional neural network（DCNN） model that is suitable for motor fault diagnosis is proposed,and the fault characteristics are learned from the original data layer by layer,so as to realize the identification of different faults.After a lot of experimental analysis,the method is better than the existing depth model in terms of detection rate and error rate.

关键词： Generative adversarial network deep convolution neural network Unbalance sample Motor fault identification

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Human Behavior Recognition Method Based on Double-Branch deep convolution neural network

Human Behavior Recognition Method Based on Double-Branch Dee...

引用

第30届中国控制与决策会议

作者： Zhou Zhigang Duan Guangxue Lei Huan Zhou Guangbing Wang Nan Yang Wenjie School of Automation Guangdong University of Technology Guangdong Institute of Intelligent Manufacturing School of information Science and Engineering Shenyang University of Technology

Aiming at the poor robustness and the low accuracy of 2 D image recognition, this paper presents a method of human behaviors recognition based on double-branch deep convolution neural network. Firstly, the features of the input image are extracted, and the feature maps are input into the double-branch deep convolution neural network to obtain the joints information of human body and the joints connection information of human body respectively. And we used the Hopcroft-Karp algorithm to optimally match skeletal joints to obtain the human skeletal sequence diagram. The human behaviors are identified by the multi-classification support vector machines. Finally, through the training and testing of standing, walking, running, waving, bending, squatting and sitting on seven kinds of general human behaviors, the experimental results show that the proposed approach has good accuracy and robustness.

关键词： 2D image deep convolution neural network behavior recognition human skeleton multi-classification support vector machines

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

deep neural network Based Detection and Segmentation of Ships for Maritime Surveillance

引用

Computer Systems Science & Engineering 2023年第1期44卷 647-662页

作者： Kyamelia Roy Sheli Sinha Chaudhuri Sayan Pramanik Soumen Banerjee Department of Electronics and Tele-Communication Engineering Jadavpur UniversityKolkata700032India Department of Electronics and Communication Engineering University of Engineering and ManagementKolkata700160India

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

关键词： Auto-encoder computer vision deep convolution neural network satellite imagery semantic segmentation ship detection

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

引用

Computers, Materials & Continua 2022年第7期72卷 597-609页

作者： S.Sudha A.Srinivasan T.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonamIndia

Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCN

关键词： deep convolution neural network artificial neural network nonproliferative diabetic retinopathy biological ERG sensor network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

USSL Net:Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation

引用

Journal of Shanghai Jiaotong university(Science) 2022年第4期27卷 485-497页

作者：蒋志国常青 School of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

Automatic segmentation of ischemic stroke lesions from computed tomography(CT)images is of great significance for identifying and curing this life-threatening condition.However,in addition to the problem of low image contrast,it is also challenged by the complex changes in the appearance of the stroke area and the difficulty in obtaining image data.Considering that it is difficult to obtain stroke data and labels,a data enhancement algorithm for one-shot medical image segmentation based on data augmentation using learned transformation was proposed to increase the number of data sets for more accurate segmentation.A deep convolutional neural network based algorithm for stroke lesion segmentation,called structural similarity with light U-structure(USSL)Net,was proposed.We embedded a convolution module that combines switchable normalization,multi-scale convolution and dilated convolution in the network for better segmentation performance.Besides,considering the strong structural similarity between multi-modal stroke CT images,the USSL Net uses the correlation maximized structural similarity loss(SSL)function as the loss function to learn the varying shapes of the lesions.The experimental results show that our framework has achieved results in the following aspects.First,the data obtained by adding our data enhancement algorithm is better than the data directly segmented from the multi-modal image.Second,the performance of our network model is better than that of other models for stroke segmentation tasks.Third,the way SSL functioned as a loss function is more helpful to the improvement of segmentation accuracy than the cross-entropy loss function.

关键词： structural similarity medical image segmentation deep convolution neural network automatic data enhancement algorithm

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：