Reprint
Deep Learning Applications with Practical Measured Results in Electronics Industries
Edited by
May 2020
272 pages
- ISBN978-3-03928-863-2 (Paperback)
- ISBN978-3-03928-864-9 (PDF)
This book is a reprint of the Special Issue Deep Learning Applications with Practical Measured Results in Electronics Industries that was published in
Computer Science & Mathematics
Engineering
Physical Sciences
Summary
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.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
computational intelligence; offshore wind; forecasting; machine learning; neural networks; neuro-fuzzy systems; humidity sensor; data fusion; nonlinear optimization; multiple linear regression; GSA-BP; geometric errors correction; kinematic modelling; lateral stage errors; Imaging Confocal Microscope; K-means clustering; data partition; Least Squares method; deep learning; multivariate time series forecasting; multivariate temporal convolutional network; CNN; hyperspectral image classification; information measure; transfer learning; neighborhood noise reduction; visual tracking; update occasion; update mechanism; background model; network layer contribution; saliency information; geometric errors; rigid body kinematics; lateral stage errors; imaging confocal microscope; MCM uncertainty evaluation; dot grid target; smart grid; foreign object; binary classification; convolutional network; image inpainting; content reconstruction; instance segmentation; underground mines; intelligent surveillance; residual networks; compressed sensing; image compression; image restoration; discrete wavelet transform; intelligent tire manufacturing; digital shearography; faster region-based CNN; tire bubble defects; tire quality assessment; unmanned aerial vehicle; UAV; trajectory planning; GA; A*; multiple constraints; recommender system; human computer interaction; eye-tracking device; deep learning; oral evaluation; generative adversarial network; neural audio caption; gated recurrent unit; long short-term memory; deep learning; machine learning; supervised learning; unsupervised learning; reinforcement learning; optimization techniques