Intelligent Soft Sensors

This Special Issue deals with the field of intelligent soft sensors that enable the online estimation of nonmeasurable process variables. Soft sensors or virtual sensors are common names for software algorithms in which multiple measurements are processed together. Typically, soft sensors are based...

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اللغة:الإنجليزية
منشور في: MDPI - Multidisciplinary Digital Publishing Institute 2023
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الوصول للمادة أونلاين:ONIX_20230911_9783036585222_54
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collection Directory of Open Access Books
description This Special Issue deals with the field of intelligent soft sensors that enable the online estimation of nonmeasurable process variables. Soft sensors or virtual sensors are common names for software algorithms in which multiple measurements are processed together. Typically, soft sensors are based on control theory and are also referred to as state observers. There may be dozens or even hundreds of measurements from hard sensors (big data). The interaction of signals can be used to compute new quantities that cannot be measured directly online or are difficult and expensive to measure. Soft sensors are particularly useful in data fusion, combining measurements of different characteristics and dynamics. They can be used for fault diagnosis (self-analysis, self-calibration, and self-maintenance) as well as for control applications. Well-known software algorithms that can be seen as soft sensors include, for example, Kalman filters. More recent implementations of soft sensors use neural networks, fuzzy logic, models based on evolving clustering, partial least squares, etc. In the digitized factories of the future, intelligent sensors represent one of the core building blocks for automating and optimizing production, as they make production more efficient in every respect.
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language eng
publishDate 2023
publishDateRange 2023
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publisher MDPI - Multidisciplinary Digital Publishing Institute
publisherStr MDPI - Multidisciplinary Digital Publishing Institute
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spelling doab-20.500.12854ir-1139212024-04-11T15:11:04Z Intelligent Soft Sensors Tomažič, Simon extended Kalman filter state estimation sensor selection observability non-linear models prognostic and health management extreme learning machine soft sensors computerized adaptive testing (CAT) soft-sensor based diagnosis executive functions neurodevelopmental disorders robust observer bioprocess monitoring nonlinear systems general anesthesia total intravenous anesthesia target-controlled infusion propofol BIS index depth of hypnosis improved mathematical model population-data-based model residual model early fire warning hybrid feature fusion intelligent building system D-S evidence theory affective computing EDA stress detection physiological signals frequency analysis shape memory coil joule heating effect self-sensing actuation variable stiffness actuation electrical resistance support vector machine regression model nonlinear regression model multi-source data fusion sintering quality prediction image feature extraction keyframe extraction soft sensor improved particle swarm algorithm least squares support vector machine transfer learning Pichia pastoris spectroscopy Raman modelling variable selection outliers simulator kinetic model n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This Special Issue deals with the field of intelligent soft sensors that enable the online estimation of nonmeasurable process variables. Soft sensors or virtual sensors are common names for software algorithms in which multiple measurements are processed together. Typically, soft sensors are based on control theory and are also referred to as state observers. There may be dozens or even hundreds of measurements from hard sensors (big data). The interaction of signals can be used to compute new quantities that cannot be measured directly online or are difficult and expensive to measure. Soft sensors are particularly useful in data fusion, combining measurements of different characteristics and dynamics. They can be used for fault diagnosis (self-analysis, self-calibration, and self-maintenance) as well as for control applications. Well-known software algorithms that can be seen as soft sensors include, for example, Kalman filters. More recent implementations of soft sensors use neural networks, fuzzy logic, models based on evolving clustering, partial least squares, etc. In the digitized factories of the future, intelligent sensors represent one of the core building blocks for automating and optimizing production, as they make production more efficient in every respect. 2023-09-11T12:01:21Z 2023-09-11T12:01:21Z 2023 book ONIX_20230911_9783036585222_54 9783036585222 9783036585239 https://directory.doabooks.org/handle/20.500.12854/113921 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/7764 https://mdpi.com/books/pdfview/book/7764 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8523-9 10.3390/books978-3-0365-8523-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036585222 9783036585239 230 open access
spellingShingle extended Kalman filter
state estimation
sensor selection
observability
non-linear models
prognostic and health management
extreme learning machine
soft sensors
computerized adaptive testing (CAT)
soft-sensor based diagnosis
executive functions
neurodevelopmental disorders
robust observer
bioprocess monitoring
nonlinear systems
general anesthesia
total intravenous anesthesia
target-controlled infusion
propofol
BIS index
depth of hypnosis
improved mathematical model
population-data-based model
residual model
early fire warning
hybrid feature fusion
intelligent building system
D-S evidence theory
affective computing
EDA
stress detection
physiological signals
frequency analysis
shape memory coil
joule heating effect
self-sensing actuation
variable stiffness actuation
electrical resistance
support vector machine regression model
nonlinear regression model
multi-source data fusion
sintering quality prediction
image feature extraction
keyframe extraction
soft sensor
improved particle swarm algorithm
least squares support vector machine
transfer learning
Pichia pastoris
spectroscopy
Raman
modelling
variable selection
outliers
simulator
kinetic model
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Intelligent Soft Sensors
title Intelligent Soft Sensors
title_full Intelligent Soft Sensors
title_fullStr Intelligent Soft Sensors
title_full_unstemmed Intelligent Soft Sensors
title_short Intelligent Soft Sensors
title_sort intelligent soft sensors
topic extended Kalman filter
state estimation
sensor selection
observability
non-linear models
prognostic and health management
extreme learning machine
soft sensors
computerized adaptive testing (CAT)
soft-sensor based diagnosis
executive functions
neurodevelopmental disorders
robust observer
bioprocess monitoring
nonlinear systems
general anesthesia
total intravenous anesthesia
target-controlled infusion
propofol
BIS index
depth of hypnosis
improved mathematical model
population-data-based model
residual model
early fire warning
hybrid feature fusion
intelligent building system
D-S evidence theory
affective computing
EDA
stress detection
physiological signals
frequency analysis
shape memory coil
joule heating effect
self-sensing actuation
variable stiffness actuation
electrical resistance
support vector machine regression model
nonlinear regression model
multi-source data fusion
sintering quality prediction
image feature extraction
keyframe extraction
soft sensor
improved particle swarm algorithm
least squares support vector machine
transfer learning
Pichia pastoris
spectroscopy
Raman
modelling
variable selection
outliers
simulator
kinetic model
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
topic_facet extended Kalman filter
state estimation
sensor selection
observability
non-linear models
prognostic and health management
extreme learning machine
soft sensors
computerized adaptive testing (CAT)
soft-sensor based diagnosis
executive functions
neurodevelopmental disorders
robust observer
bioprocess monitoring
nonlinear systems
general anesthesia
total intravenous anesthesia
target-controlled infusion
propofol
BIS index
depth of hypnosis
improved mathematical model
population-data-based model
residual model
early fire warning
hybrid feature fusion
intelligent building system
D-S evidence theory
affective computing
EDA
stress detection
physiological signals
frequency analysis
shape memory coil
joule heating effect
self-sensing actuation
variable stiffness actuation
electrical resistance
support vector machine regression model
nonlinear regression model
multi-source data fusion
sintering quality prediction
image feature extraction
keyframe extraction
soft sensor
improved particle swarm algorithm
least squares support vector machine
transfer learning
Pichia pastoris
spectroscopy
Raman
modelling
variable selection
outliers
simulator
kinetic model
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
url ONIX_20230911_9783036585222_54