Deep Learning Applications with Practical Measured Results in Electronics Industries
This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehi...
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Year of Publication: | 2020 |
Language: | English |
Physical Description: | 1 electronic resource (272 p.) |
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Kung, Hsu-Yang auth Deep Learning Applications with Practical Measured Results in Electronics Industries MDPI - Multidisciplinary Digital Publishing Institute 2020 1 electronic resource (272 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods. English faster region-based CNN visual tracking intelligent tire manufacturing eye-tracking device neural networks A* information measure oral evaluation GSA-BP tire quality assessment humidity sensor rigid body kinematics intelligent surveillance residual networks imaging confocal microscope update mechanism multiple linear regression geometric errors correction data partition Imaging Confocal Microscope image inpainting lateral stage errors dot grid target K-means clustering unsupervised learning recommender system underground mines digital shearography optimization techniques saliency information gated recurrent unit multivariate time series forecasting multivariate temporal convolutional network foreign object data fusion update occasion generative adversarial network CNN compressed sensing background model image compression supervised learning geometric errors UAV nonlinear optimization reinforcement learning convolutional network neuro-fuzzy systems deep learning image restoration neural audio caption hyperspectral image classification neighborhood noise reduction GA MCM uncertainty evaluation binary classification content reconstruction kinematic modelling long short-term memory transfer learning network layer contribution instance segmentation smart grid unmanned aerial vehicle forecasting trajectory planning discrete wavelet transform machine learning computational intelligence tire bubble defects offshore wind multiple constraints human computer interaction Least Squares method 3-03928-863-6 Chen, Chi-Hua auth Horng, Mong-Fong auth Hwang, Feng-Jang auth |
language |
English |
format |
eBook |
author |
Kung, Hsu-Yang |
spellingShingle |
Kung, Hsu-Yang Deep Learning Applications with Practical Measured Results in Electronics Industries |
author_facet |
Kung, Hsu-Yang Chen, Chi-Hua Horng, Mong-Fong Hwang, Feng-Jang |
author_variant |
h y k hyk |
author2 |
Chen, Chi-Hua Horng, Mong-Fong Hwang, Feng-Jang |
author2_variant |
c h c chc m f h mfh f j h fjh |
author_sort |
Kung, Hsu-Yang |
title |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_full |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_fullStr |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_full_unstemmed |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_auth |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_new |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
title_sort |
deep learning applications with practical measured results in electronics industries |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
physical |
1 electronic resource (272 p.) |
isbn |
3-03928-864-4 3-03928-863-6 |
illustrated |
Not Illustrated |
work_keys_str_mv |
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status_str |
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ids_txt_mv |
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Deep Learning Applications with Practical Measured Results in Electronics Industries |
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