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210512 ||| eng |
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|a 9783039288632
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|a books978-3-03928-864-9
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|a 9783039288649
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|a Kung, Hsu-Yang
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|a Deep Learning Applications with Practical Measured Results in Electronics Industries
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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|a 1 electronic resource (272 p.)
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|a visual tracking
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|a machine learning
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|a intelligent tire manufacturing
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|a supervised learning
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|a tire quality assessment
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|a multiple constraints
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|a forecasting
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|a binary classification
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|a unmanned aerial vehicle
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|a oral evaluation
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|a faster region-based CNN
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|a human computer interaction
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|a kinematic modelling
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|a data fusion
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|a underground mines
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|a generative adversarial network
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|a deep learning
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|a intelligent surveillance
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|a gated recurrent unit
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|a neural networks
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|a History of engineering and technology / bicssc
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|a foreign object
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|a hyperspectral image classification
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|a data partition
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|a residual networks
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|a instance segmentation
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|a unsupervised learning
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|a tire bubble defects
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|a neural audio caption
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|a GA
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|a content reconstruction
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|a saliency information
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|a dot grid target
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|a rigid body kinematics
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|a transfer learning
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|a reinforcement learning
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|a digital shearography
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|a MCM uncertainty evaluation
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|a information measure
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|a multiple linear regression
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|a discrete wavelet transform
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|a background model
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|a image restoration
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|a K-means clustering
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|a imaging confocal microscope
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|a image compression
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|a update mechanism
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|a geometric errors correction
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|a Least Squares method
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|a multivariate time series forecasting
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|a UAV
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|a smart grid
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|a nonlinear optimization
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|a eye-tracking device
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|a compressed sensing
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|a offshore wind
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|a neighborhood noise reduction
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|a humidity sensor
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|a CNN
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|a neuro-fuzzy systems
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|a long short-term memory
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|a trajectory planning
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|a Imaging Confocal Microscope
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|a GSA-BP
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|a computational intelligence
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|a optimization techniques
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|a lateral stage errors
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|a image inpainting
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|a A*
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|a geometric errors
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|a multivariate temporal convolutional network
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|a update occasion
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|a recommender system
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|a network layer contribution
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|a convolutional network
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|a Chen, Chi-Hua
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|a Horng, Mong-Fong
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|a Hwang, Feng-Jang
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|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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|a 10.3390/books978-3-03928-864-9
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|u https://www.mdpi.com/books/pdfview/book/2296
|7 0
|x Verlag
|3 Volltext
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|u https://directory.doabooks.org/handle/20.500.12854/44630
|z DOAB: description of the publication
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|a 900
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|a 000
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|a 140
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|a 600
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|a 620
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|a 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.
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