LEADER 05024nam 2201249z- 450 001 9910404080403321 005 20210211 010 $a3-03928-864-4 035 $a(CKB)4100000011302334 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/44630 035 $a(oapen)doab44630 035 $a(EXLCZ)994100000011302334 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDeep Learning Applications with Practical Measured Results in Electronics Industries 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (272 p.) 311 08$a3-03928-863-6 330 $aThis 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. 606 $aHistory of engineering and technology$2bicssc 610 $aA* 610 $abackground model 610 $abinary classification 610 $aCNN 610 $acompressed sensing 610 $acomputational intelligence 610 $acontent reconstruction 610 $aconvolutional network 610 $adata fusion 610 $adata partition 610 $adeep learning 610 $adigital shearography 610 $adiscrete wavelet transform 610 $adot grid target 610 $aeye-tracking device 610 $afaster region-based CNN 610 $aforecasting 610 $aforeign object 610 $aGA 610 $agated recurrent unit 610 $agenerative adversarial network 610 $ageometric errors 610 $ageometric errors correction 610 $aGSA-BP 610 $ahuman computer interaction 610 $ahumidity sensor 610 $ahyperspectral image classification 610 $aimage compression 610 $aimage inpainting 610 $aimage restoration 610 $aimaging confocal microscope 610 $aImaging Confocal Microscope 610 $ainformation measure 610 $ainstance segmentation 610 $aintelligent surveillance 610 $aintelligent tire manufacturing 610 $aK-means clustering 610 $akinematic modelling 610 $alateral stage errors 610 $aLeast Squares method 610 $along short-term memory 610 $amachine learning 610 $aMCM uncertainty evaluation 610 $amultiple constraints 610 $amultiple linear regression 610 $amultivariate temporal convolutional network 610 $amultivariate time series forecasting 610 $aneighborhood noise reduction 610 $anetwork layer contribution 610 $aneural audio caption 610 $aneural networks 610 $aneuro-fuzzy systems 610 $anonlinear optimization 610 $aoffshore wind 610 $aoptimization techniques 610 $aoral evaluation 610 $arecommender system 610 $areinforcement learning 610 $aresidual networks 610 $arigid body kinematics 610 $asaliency information 610 $asmart grid 610 $asupervised learning 610 $atire bubble defects 610 $atire quality assessment 610 $atrajectory planning 610 $atransfer learning 610 $aUAV 610 $aunderground mines 610 $aunmanned aerial vehicle 610 $aunsupervised learning 610 $aupdate mechanism 610 $aupdate occasion 610 $avisual tracking 615 7$aHistory of engineering and technology 700 $aKung$b Hsu-Yang$4auth$01328954 702 $aChen$b Chi-Hua$4auth 702 $aHorng$b Mong-Fong$4auth 702 $aHwang$b Feng-Jang$4auth 906 $aBOOK 912 $a9910404080403321 996 $aDeep Learning Applications with Practical Measured Results in Electronics Industries$93039220 997 $aUNINA