Deep Learning Applications with Practical Measured Results in Electronics Industries

• Autore: Kung Hsu-Yang
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 electronic resource (272 p.)
• Soggetto non controllato: 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
• Persona (resp. second.): ChenChi-Hua; HorngMong-Fong; HwangFeng-Jang
• Sommario/riassunto: 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.
• Titolo autorizzato: Deep Learning Applications with Practical Measured Results in Electronics Industries
• ISBN: 3-03928-864-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910404080403321
• Lo trovi qui: Univ. Federico II