00797nam0-22003011i-450-99000187681040332120080902160534.0000187681FED01000187681(Aleph)000187681FED0100018768120030910f1867----km-y0itay50------bafreFRf-------001yyCulture de l'aspergeT. LenormandParisLibrairie d'Horticulture de E. Donnaud[1867]54 p., 1 tav.16 cmAsparagi635.31Lenormand,T.358719ITUNINARICAUNIMARCBK99000187681040332160 094.3 C 12/1FAGBCFAGBCCulture de l'asperge400295UNINA01046nam a22002531i 450099100307731970753620040529183430.0040624s1981 xxu|||||||||||||||||eng b13020882-39ule_instARCHE-097856ExLDip.to Beni CulturaliitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.733J. Paul Getty Museum of art <Malibù>487752Roman portraits in the Getty Museum /[a cura di] Jirí Frel[S.l.] :[Philbrook Art Center and the J. Paul Getty Museum],[1981]VI, 137 p. ;17 cmRitratti romaniCollezioniMalibùPaul Getty MuseumCataloghiFrel, Jirí.b1302088202-04-1412-07-04991003077319707536LE001 AR V 154 8º12001000116034le001C. 1-E0.00-l- 00000.i1363418512-07-04Roman portraits in the Getty Museum288528UNISALENTOle00112-07-04ma -engxxu0102893nam 22005295 450 991062727230332120251009072000.0981-16-9131-210.1007/978-981-16-9131-7(MiAaPQ)EBC7119944(Au-PeEL)EBL7119944(CKB)25179516100041(PPN)265860598(DE-He213)978-981-16-9131-7(OCoLC)1493008948(EXLCZ)992517951610004120221019d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBig Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems /by Yaguo Lei, Naipeng Li, Xiang Li1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (292 pages)Engineering SeriesPrint version: Lei, Yaguo Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems Singapore : Springer,c2022 9789811691300 Includes bibliographical references.Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.Engineering SeriesMachineryMachinery and Machine ElementsMachinery.Machinery and Machine Elements.005.7Lei Yaguo983733Li NaipengLi XiangMiAaPQMiAaPQMiAaPQBOOK9910627272303321Big-data driven intelligent fault diagnosis and prognosis for mechanical systems3058476UNINA