01123nam0 22002891i 450 UON0034210120231205104303.9820091030d1922 |0itac50 baengGB|||| 1||||Preface to ShakespeareHamletHarley Granville-Barkerthird seriesLondonSidgwick & Jacson,1937rist. 1951x, 329 p.19 cm.001UON003421062001 HamletHarley Granville-Barker.Shakespeare WilliamUONC038333FIGBLondonUONL003044822.33Letteratura drammatica inglese. William Shakespeare21GRANVILLE-BARKERHarleyUONV190122163926Sidgwick & JacksonUONV257978650ITSOL20250704RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00342101SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI Angl II SHA 3 b GRA 8 SI MR 39536 5 8 Preface to Shakespeare522161UNIOR03821nam 2201021z- 450 991055750660332120220111(CKB)5400000000044490(oapen)https://directory.doabooks.org/handle/20.500.12854/76611(oapen)doab76611(EXLCZ)99540000000004449020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierSensors Fault Diagnosis Trends and ApplicationsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (236 p.)3-0365-1048-6 3-0365-1049-4 Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.Technology: general issuesbicsscacoustic emission signalsacoustic-based diagnosisactuator and sensor faultadaptive noise reducerartificial neural networkattention mechanismautomotiveautonomous vehiclebearing fault diagnosisbraking controlcontrol valveconvolutional neural networkcryptographydecision treedeep learningfault detectionfault detection and diagnosisfault detection and isolationfault detection and isolation (FDIR)fault diagnosisfault identificationfault isolationfault recoveryfault tolerant controlfaults estimationgaussian reference signalgear fault diagnosisgearbox fault diagnosishybrid kernel functionintelligent leak detectioniterative learning controlkrill herd algorithmlidarmachine learningmodel predictive controln/aNARXneural networksnonlinear systemsobserver designone against on multiclass support vector machinepath tracking controlperception sensorperformance degradationrolling bearingscan-chain diagnosisShannon entropysignature matrixstacked auto-encoderstatistical parameterssupport vector machineSVRTakagi-Sugeno fuzzy systemsvarying rotational speedwavelet denoisingweighting strategywireless sensor networksTechnology: general issuesWitczak Piotredt1322282Witczak PiotrothBOOK9910557506603321Sensors Fault Diagnosis Trends and Applications3034754UNINA