Results 51 to 60 of about 2,004,297 (387)
IEEE Access, 2020 It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain.
Jinhua Lin, Lin Ma, Jingxia Cui
doaj +1 more source
A Survey on Graph Classification and Link Prediction based on GNN [PDF]
arXiv, 2023 Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks. The pivotal step in transferring convolutional neural networks to graph data analysis and processing lies in the ...
arxiv
ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM
Jixie qiangdu, 2021 Aiming at the problem that it is difficult to extract subtle fault features in the process of rolling bearing fault identification,this paper proposes a rolling bearing fault diagnosis method based on fusion convolutional neural network and support ...
WANG YongDing, JIN ZiQi
doaj
Test-object recognition in thermal images [PDF]
Компьютерная оптика, 2019 The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode.
Aleksandr Mingalev +4 more
doaj +1 more source
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
IEEE Access, 2021 A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao +4 more
doaj +1 more source
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction [PDF]
Italian National Conference on Sensors, 2017 This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and
Xiaolei Ma +5 more
semanticscholar +1 more source
A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
doaj
Miners' facial expression recognition method based on convolutional neural network
Gong-kuang zidonghua, 2018 In view of problems of low recognition rate and complex algorithm of traditional miner's facial expression recognition methods, based on convolutional neural network and combining with nonlinear mapping function in support vector machine algorithm, a ...
DU Yun, ZHANG Lulu, PAN Tao
doaj +1 more source
Convolutional neural network-based onboard band selection for hyperspectral data with coarse band-to-band alignment [PDF]
Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands.
Camps Carmona, Adriano José +4 more
core +1 more source
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2019 We propose a deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNNs), specializing in two distortion ...
Weixia Zhang +4 more
semanticscholar +1 more source