Results 51 to 60 of about 6,343,053 (206)
A DEEP LEARNING BASED SURROGATE MODEL FOR ESTIMATING THE FLUX AND POWER DISTRIBUTION SOLVED BY DIFFUSION EQUATION [PDF]
EPJ Web of Conferences, 2021 A deep learning based surrogate model is proposed for replacing the conventional diffusion equation solver and predicting the flux and power distribution of the reactor core. Using the training data generated by the conventional diffusion equation solver,
Zhang Qian +4 more
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Correction of uneven illumination in color microscopic image based on fully convolutional network.
Optics Express, 2021 The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images.
Jianhang Wang +6 more
semanticscholar +1 more source
Remote Sensing, 2018 Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions.
Guangming Wu +7 more
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Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
Applied Sciences, 2022 Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this
Adnan Riaz +7 more
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Remote Sensing, 2021 Mangroves are grown in intertidal zones along tropical and subtropical climate areas, which have many benefits for humans and ecosystems. The knowledge of mangrove conditions is essential to know the statuses of mangroves. Recently, satellite imagery has
Ilham Jamaluddin +4 more
semanticscholar +1 more source
A Plankton Detection Method Based on Neural Networks and Digital Holographic Imaging
Chemosensors, 2022 Detecting marine plankton by means of digital holographic microscopy (DHM) has been successfully deployed in recent decades; however, in most previous studies, the identification of the position, shape, and size of plankton has been neglected, which may ...
Kaiqi Lang, Hui Cai, Xiaoping Wang
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Look-behind fully convolutional neural network for computer-aided endoscopy
, 2019 In this paper, we propose a novel Fully Convolutional Neural Network (FCN) architecture aiming to aid the detection of abnormalities, such as polyps, ulcers and blood, in gastrointestinal (GI) endoscopy images.
Diamantis D.E., Iakovidis D.K., Koulaouzidis A.
core +1 more source
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
IEEE Access, 2019 The electrocardiogram (ECG) is an efficient and noninvasive indicator for arrhythmia detection and prevention. In real-world scenarios, ECG signals are prone to be contaminated with various noises, which may lead to wrong interpretation.
Hsin-Tien Chiang +5 more
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Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
Comput. Aided Civ. Infrastructure Eng., 2018 The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming.
Xincong Yang +5 more
semanticscholar +1 more source
Sparse fully convolutional network for face labeling
, 2019 © 2018 Elsevier B.V. This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation.
Huang, T +4 more
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