LEADER 04568nam 22007455 450 001 9910411936303321 005 20250610110145.0 010 $a3-030-49395-4 024 7 $a10.1007/978-3-030-49395-0 035 $a(CKB)4100000011325666 035 $a(DE-He213)978-3-030-49395-0 035 $a(MiAaPQ)EBC6270515 035 $a(MiAaPQ)EBC6252669 035 $a(MiAaPQ)EBC29092726 035 $a(EXLCZ)994100000011325666 100 $a20200701d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint /$fby Mark K. Hinders 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIV, 346 p. 208 illus., 143 illus. in color.) 311 08$a3-030-49394-6 327 $aBackground and history -- Intelligent structural health monitoring with ultrasonic lamb waves -- Automatic detection of flaws in recorded music -- Pocket depth determination with an ultrasonographic periodontal probe -- Spectral intermezzo: Spirit security systems -- Lamb wave tomographic rays in pipes -- Classification of RFID tags with wavelet fingerprinting -- Pattern classification for interpreting sensor data from a walking-speed robot -- Cranks and charlatans and deepfakes. 330 $aThis book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement technologies for the quantitative characterization of materials, tissues and structures by non-invasive means. Intelligent Feature Selection for Machine Learning using the Dynamic Wavelet Fingerprint begins by providing background information on machine learning and the wavelet fingerprint technique. It then progresses through six technical chapters, applying the methods discussed to particular real-world problems. Theses chapters are presented in such a way that they can be read on their own, depending on the reader?s area of interest, or read together to provide a comprehensive overview of the topic. Given its scope, the book will be of interest to practitioners, engineers and researchers seeking to leverage the latest advances in machine learning in order to develop solutions to practical problems in structural health monitoring, medical imaging, autonomous vehicles, wireless technology, and historical conservation. 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aBiomedical engineering 606 $aMaterials science 606 $aAutomatic control 606 $aRobotics 606 $aMechatronics 606 $aComputer science 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 606 $aMaterials Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/Z00000 606 $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aBiomedical engineering. 615 0$aMaterials science. 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aMechatronics. 615 0$aComputer science. 615 14$aSignal, Image and Speech Processing. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aMaterials Science, general. 615 24$aControl, Robotics, Mechatronics. 615 24$aComputer Science, general. 676 $a006.31 700 $aHinders$b Mark K$4aut$4http://id.loc.gov/vocabulary/relators/aut$053566 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910411936303321 996 $aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint$92543440 997 $aUNINA LEADER 03012oam 2200577I 450 001 9910971624903321 005 20251117110939.0 010 $a1-4987-6019-8 010 $a0-429-10232-1 010 $a1-4665-8406-8 024 7 $a10.1201/b18384 035 $a(CKB)2670000000560202 035 $a(EBL)1707786 035 $a(SSID)ssj0001535645 035 $a(PQKBManifestationID)11841000 035 $a(PQKBTitleCode)TC0001535645 035 $a(PQKBWorkID)11499746 035 $a(PQKB)11057517 035 $a(MiAaPQ)EBC1707786 035 $a(OCoLC)907924065 035 $a(EXLCZ)992670000000560202 100 $a20180331h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aArtificial intelligence tools $edecision support systems in condition monitoring and diagnosis /$fDiego Galar Pascual 205 $a1st ed. 210 1$aBoca Raton, Florida :$cCRC Press,$d[2015] 210 4$dİ2015 215 $a1 online resource (528 p.) 300 $aDescription based upon print version of record. 311 08$a1-4665-8405-X 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover 330 $aArtificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of each technique as well as the issues associated with real-life applicationCovers classification methods, from neural networks to Bayesian and support vector machinesProposes fuzzy logic to explain the uncertainties associated with diagnostic processes 606 $aIndustrial equipment$xMaintenance and repair$xData processing 606 $aMachinery$xMonitoring 606 $aArtificial intelligence$xIndustrial applications 615 0$aIndustrial equipment$xMaintenance and repair$xData processing. 615 0$aMachinery$xMonitoring. 615 0$aArtificial intelligence$xIndustrial applications. 676 $a658.2020285 700 $aPascual$b Diego Galar$01874905 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910971624903321 996 $aArtificial intelligence tools$94485731 997 $aUNINA