Deep Learning Applications with Practical Measured Results in Electronics Industries

• Autore: Kung Hsu-Yang
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (272 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: A*; background model; binary classification; CNN; compressed sensing; computational intelligence; content reconstruction; convolutional network; data fusion; data partition; deep learning; digital shearography; discrete wavelet transform; dot grid target; eye-tracking device; faster region-based CNN; forecasting; foreign object; GA; gated recurrent unit; generative adversarial network; geometric errors; geometric errors correction; GSA-BP; human computer interaction; humidity sensor; hyperspectral image classification; image compression; image inpainting; image restoration; imaging confocal microscope; Imaging Confocal Microscope; information measure; instance segmentation; intelligent surveillance; intelligent tire manufacturing; K-means clustering; kinematic modelling; lateral stage errors; Least Squares method; long short-term memory; machine learning; MCM uncertainty evaluation; multiple constraints; multiple linear regression; multivariate temporal convolutional network; multivariate time series forecasting; neighborhood noise reduction; network layer contribution; neural audio caption; neural networks; neuro-fuzzy systems; nonlinear optimization; offshore wind; optimization techniques; oral evaluation; recommender system; reinforcement learning; residual networks; rigid body kinematics; saliency information; smart grid; supervised learning; tire bubble defects; tire quality assessment; trajectory planning; transfer learning; UAV; underground mines; unmanned aerial vehicle; unsupervised learning; update mechanism; update occasion; visual tracking
• 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