03498nam a2200349Ii 4500991003241669707536070806s2001 maua s 001 0 eng d97808841534740884153479b13654019-39ule_inst95981:95980Elsevier Science & Technologyhttp://www.sciencedirect.comBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. Ingegneria Innovazioneeng660.28322Pennock, James O.1204553Piping engineering leadership for process plant projects[e-book] /J.O. PennockBoston :Gulf Professional Pub.,c2001xii, 273 p. :ill. ;24 cmPart I -- Roles and Responsibilities:; Ch. 1. Piping; Ch. 2. Engineering Management and other Engineering Disciplines; Ch. 3. Nonengineering Groups; ; Part II -- Project Descriptions:; Ch. 4. Project Types, Terms, and Execution Philosophy; Ch. 5. Grassroots Projects; Ch. 6. Revamp and Rebuild Projects; ; Part III -- Procurement, Pipe Fabrication, and Contracts; Ch. 7. Procurement Responsibilities; Ch. 8. Pipe Shop Fabrication; Ch. 9. Contracts and Construction Work Packages (CWP); ; Part IV -- Project Execution:; Ch. 10. Project Fedinition--Scope of Work; Ch. 11. Estimating; Ch. 12. Scheduling; Ch. 13. Planning and Organizing; Ch. 14. Staffing and Directing; Ch. 15. Controlling Change; Ch. 16. Reporting; Ch. 17. Project Completion; ; Part V -- The Future; Ch. 18. Where Do We Go from Here?; ; Appendices:; Typical Piping Deliverables; The Cradle-to-Grave Concept; Glossary; Suggested Piping File Index; IndexJames O. Pennock has compiled 45 years of personal experience into this how-to guide. Focusing on the position of "lead in charge," this book is an indispensable resource for anyone, new or seasoned veteran, whose job it is to lead the piping engineering and design of a project. The "lead" person is responsible for the successful execution of all piping engineering and design for a project, technical and non-technical aspects alike. The author defines the roles and responsibilities a lead will face and the differences found in various project types. Incorporates four decades of personal experience in a How-To guide Focuses on the position of "lead in charge" Includes coverage of topics often ignored in other books yet essential for success: management, administrative, and control responsibilitiesElectronic reproduction.Amsterdam :Elsevier Science & Technology,2007.Mode of access: World Wide Web.System requirements: Web browser.Title from title screen (viewed on Aug. 2, 2007).Access may be restricted to users at subscribing institutionsPipingChemical plantsEquipment and suppliesElectronic books.localOriginal08841534799780884153474(DLC) 00062301(OCoLC)44969480Referexhttp://www.sciencedirect.com/science/book/9780884153474An electronic book accessible through the World Wide Web; click for informationTable of contentshttp://www.loc.gov/catdir/toc/els051/00062301.htmlPublisher descriptionhttp://catdir.loc.gov/catdir/description/els031/00062301.html.b1365401903-03-2224-01-08991003241669707536Piping engineering leadership for process plant projects2779912UNISALENTOle02624-01-08m@ -engmau0003220nam 22005295 450 991048378920332120200701155539.03-030-04663-X10.1007/978-3-030-04663-7(CKB)4100000007158850(DE-He213)978-3-030-04663-7(MiAaPQ)EBC5925982(PPN)243769024(EXLCZ)99410000000715885020181123d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierDealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods /by Sarah Vluymans1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XVIII, 249 p. 23 illus., 10 illus. in color.) Studies in Computational Intelligence,1860-949X ;8073-030-04662-1 Introduction -- Classification -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classification of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography.This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.Studies in Computational Intelligence,1860-949X ;807Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.511.3223511.3223Vluymans Sarahauthttp://id.loc.gov/vocabulary/relators/aut1225886MiAaPQMiAaPQMiAaPQBOOK9910483789203321Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods2846217UNINA