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Deep Learning Applications with Practical Measured Results in Electronics Industries
Deep Learning Applications with Practical Measured Results in Electronics Industries
Autore Kung Hsu-Yang
Pubbl/distr/stampa 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
ISBN 3-03928-864-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910404080403321
Kung Hsu-Yang  
MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
Autore Lee Saro
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (438 p.)
Soggetto non controllato artificial neural network
model switching
sensitivity analysis
neural networks
logit boost
Qaidam Basin
land subsidence
land use/land cover (LULC)
naïve Bayes
multilayer perceptron
convolutional neural networks
single-class data descriptors
logistic regression
feature selection
mapping
particulate matter 10 (PM10)
Bayes net
gray-level co-occurrence matrix
multi-scale
Logistic Model Trees
classification
Panax notoginseng
large scene
coarse particle
grayscale aerial image
Gaofen-2
environmental variables
variable selection
spatial predictive models
weights of evidence
landslide prediction
random forest
boosted regression tree
convolutional network
Vietnam
model validation
colorization
data mining techniques
spatial predictions
SCAI
unmanned aerial vehicle
high-resolution
texture
spatial sparse recovery
landslide susceptibility map
machine learning
reproducible research
constrained spatial smoothing
support vector machine
random forest regression
model assessment
information gain
ALS point cloud
bagging ensemble
one-class classifiers
leaf area index (LAI)
landslide susceptibility
landsat image
ionospheric delay constraints
spatial spline regression
remote sensing image segmentation
panchromatic
Sentinel-2
remote sensing
optical remote sensing
materia medica resource
GIS
precise weighting
change detection
TRMM
traffic CO
crop
training sample size
convergence time
object detection
gully erosion
deep learning
classification-based learning
transfer learning
landslide
traffic CO prediction
hybrid model
winter wheat spatial distribution
logistic
alternating direction method of multipliers
hybrid structure convolutional neural networks
geoherb
predictive accuracy
real-time precise point positioning
spectral bands
ISBN 3-03921-216-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910367564103321
Lee Saro  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui