Deep Learning Applications with Practical Measured Results in Electronics Industries

Kung, Hsu-Yang ; Chen, Chi-Hua ; et al.

MDPI - Multidisciplinary Digital Publishing Institute, 2020

Online E-Book - 272

Zugriff:

View record at Directory of Open Access Books (Volltext)

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.

Titel:	Deep Learning Applications with Practical Measured Results in Electronics Industries
Autor/in / Beteiligte Person:	Kung, Hsu-Yang ; Chen, Chi-Hua ; Horng, Mong-Fong ; Hwang, Feng-Jang
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	MDPI - Multidisciplinary Digital Publishing Institute, 2020
Medientyp:	E-Book
Umfang:	272
ISBN:	978-3-03928-864-9 (print) ; 978-3-03928-863-2 (print)
DOI:	10.3390/books978-3-03928-864-9
Schlagwort:	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 thema EDItEUR:T Technology, Engineering, Agriculture, Industrial processes:TB Technology: general issues:TBX History of engineering and technology TA1-2040 T1-995
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: application/octet-stream Language: English Rights: Attribution-NonCommercial-NoDerivatives 4.0 International ; URL: https://creativecommons.org/licenses/by-nc-nd/4.0/ Notes: 46102

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.