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Entropy Measures for Data Analysis: Theory, Algorithms and Applications
Entropy Measures for Data Analysis: Theory, Algorithms and Applications
Autore Keller Karsten
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (260 p.)
Soggetto non controllato fault diagnosis
empirical mode decomposition
auditory attention
Dempster-Shafer evidence theory
simulation
uncertainty of basic probability assignment
center of pressure displacement
particle size distribution
multivariate analysis
symbolic analysis
permutation entropy
short time records
co-evolution
plausibility transformation
experiment of design
cross-entropy method
weighted Hartley entropy
firefly algorithm
embedded dimension
entropy measure
effective transfer entropy
treadmill walking
ordinal patterns
complex fuzzy set
entropy visualization
belief entropy
signal classification
machine learning evaluation
novelty detection
selfsimilar measure
Permutation entropy
automatic learning
cross wavelet transform
cross-visibility graphs
Kolmogorov-Sinai entropy
distance
Shannon-type relations
Tsallis entropy
market crash
support vector machine (SVM)
conditional entropy of ordinal patterns
sample entropy
learning
electroencephalography (EEG)
meta-heuristic
entropy
data transformation
information entropy
signal analysis
synchronization analysis
similarity indices
data analysis
geodesic distance
auditory attention classifier
entropy measures
distance induced vague entropy
analog circuit
vague entropy
complex vague soft set
entropy balance equation
parametric t-distributed stochastic neighbor embedding
global optimization
learning systems
image entropy
algorithmic complexity
support vector machine
system coupling
relevance analysis
Chinese stock sectors
Shannon entropy
linear discriminant analysis (LDA)
information
information transfer
dual-tasking
non-probabilistic entropy
ISBN 3-03928-033-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Entropy Measures for Data Analysis
Record Nr. UNINA-9910367736303321
Keller Karsten  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Swarm Robotics
Swarm Robotics
Autore Spezzano Giandomenico
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (310 p.)
Soggetto non controllato self-organization
signal source localization
multi-robot system
sensor deployment
parallel technique
shape normalization
genetic algorithm
multiple robots
optimization
improved potential field
optimal configuration
autonomous docking
asymmetrical interaction
comparison
behaviors
patterns
self-assembly robots
congestion control
surface-water environment
target recognition
coordinate motion
UAV swarms
formation reconfiguration
swarm robotics
swarm intelligence
artificial bee colony algorithm
obstacle avoidance
fish swarm optimization
search algorithm
robotics
time-difference-of-arrival (TDOA)
formation
mobile robots
formation control
meta-heuristic
event-triggered communication
search
virtual structure
3D model identification
surveillance
event-driven coverage
scale-invariant feature transform
system stability
Swarm intelligence algorithm
bionic intelligent algorithm
unmanned aerial vehicle
underwater environment
artificial flora (AF) algorithm
swarm behavior
weighted implicit shape representation
Cramer-Rao low bound (CRLB)
environmental perception
particle swarm optimization
modular robots
cooperative target hunting
virtual linkage
multi-AUV
consensus control
panoramic view
nonlinear disturbance observer
sliding mode controller
path optimization
Swarm Chemistry
multi-agents
ISBN 3-03897-923-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910674019203321
Spezzano Giandomenico  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Symmetry in Mechanical Engineering
Symmetry in Mechanical Engineering
Autore Glowacz Adam
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (372 p.)
Soggetto topico History of engineering & technology
Soggetto non controllato traveling waves
vibration stability
self-excited vibration
thin spur gear
variational mode decomposition
signal analysis
time-frequency analysis
center frequency method of double thresholds
Hilbert transform
finite-time control
terminal sliding mode
chattering
neural network
thin-walled structures
higher-order deformation modes
identification
doubly symmetric cross-sections
shell-like deformation
dual mass flywheel
absolute sensitivity
relative sensitivity
torsional vibration
spring
Dempster's rule
evidence distance
pattern recognition
maritime surveillance
customer needs
bad parameters
core problems
design process
conceptual design
time-varying P-type IL method
MFA control
imprecise probability
active control
piezoelectric cantilever plate
image processing
particle
distribution
rolling bearings
Fault diagnosis
second-order tristable stochastic resonance
seeker optimization algorithm
output signal-to-noise ratio
global optimization
meta-heuristic
butterfly optimization algorithm
cross-entropy method
engineering design problems
grinding
rheology
breakage parameters
relative viscosity
tool wear state
CNN
BiLSTM
attention mechanism
signal features
vibrations
symmetries
two-engine vehicle
rear clutch
front clutch
vibration modes
granulation
on-line monitoring
fertilizers
parallel robot
minimally invasive procedures
algebraic modeling
Study parameters
fractional damping
vibration
dynamic behaviour
hybrid mechanism
helicopter
abrasive belt grinding
predictive model
regression
material removal
electric vehicles
PMSM
auxiliary slot
response surface methodology
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557134803321
Glowacz Adam  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui