LEADER 04290nam 2200997z- 450 001 9910743269003321 005 20230911 035 $a(CKB)5690000000228618 035 $a(oapen)doab113921 035 $a(EXLCZ)995690000000228618 100 $a20230920c2023uuuu -u- - 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIntelligent Soft Sensors 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2023 215 $a1 online resource (230 p.) 311 08$a3-0365-8523-0 330 $aThis 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. 606 $aHistory of engineering and technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aaffective computing 610 $abioprocess monitoring 610 $aBIS index 610 $acomputerized adaptive testing (CAT) 610 $aD-S evidence theory 610 $adepth of hypnosis 610 $aearly fire warning 610 $aEDA 610 $aelectrical resistance 610 $aexecutive functions 610 $aextended Kalman filter 610 $aextreme learning machine 610 $afrequency analysis 610 $ageneral anesthesia 610 $ahybrid feature fusion 610 $aimage feature extraction 610 $aimproved mathematical model 610 $aimproved particle swarm algorithm 610 $aintelligent building system 610 $ajoule heating effect 610 $akeyframe extraction 610 $akinetic model 610 $aleast squares support vector machine 610 $amodelling 610 $amulti-source data fusion 610 $an/a 610 $aneurodevelopmental disorders 610 $anon-linear models 610 $anonlinear regression model 610 $anonlinear systems 610 $aobservability 610 $aoutliers 610 $aphysiological signals 610 $aPichia pastoris 610 $apopulation-data-based model 610 $aprognostic and health management 610 $apropofol 610 $aRaman 610 $aresidual model 610 $arobust observer 610 $aself-sensing actuation 610 $asensor selection 610 $ashape memory coil 610 $asimulator 610 $asintering quality prediction 610 $asoft sensor 610 $asoft sensors 610 $asoft-sensor based diagnosis 610 $aspectroscopy 610 $astate estimation 610 $astress detection 610 $asupport vector machine regression model 610 $atarget-controlled infusion 610 $atotal intravenous anesthesia 610 $atransfer learning 610 $avariable selection 610 $avariable stiffness actuation 615 7$aHistory of engineering and technology 615 7$aTechnology: general issues 906 $aBOOK 912 $a9910743269003321 996 $aIntelligent Soft Sensors$93560490 997 $aUNINA