03283nam 2200481 450 991081993530332120200520144314.00-12-804176-50-12-804193-5(CKB)4230000000000413(Au-PeEL)EBL4054145(CaPaEBR)ebr11117453(CaONFJC)MIL328145(OCoLC)929532314(MiAaPQ)EBC4054145(EXLCZ)99423000000000041320151119h20162016 uy 0engurcnu||||||||rdacontentrdamediardacarrierApplied welding engineering processes, codes, and standards /Ramesh SinghSecond edition.Amsterdam, [Netherlands] :Butterworth-Heinemann,2016.©20161 online resource (444 pages) illustrations, tablesIncludes index.Section 1: Introduction to Basic Metallurgy -- Section 2: Welding Metallurgy & Welding Processes -- Section 3: Nondestructive Testing -- Section 4: Codes and Standards.A practical and in-depth guide to materials selection, welding techniques, and procedures, Applied Welding Engineering: Processes, Codes and Standards, provides expert advice for complying with international codes as well as working them into "day to day" design, construction and inspection activities. New content in this edition covers the standards and codes of the Canadian Welding Society, and the DNV standards in addition to updates to existing coverage of the American Welding Society, American Society of Mechanical Engineers, The Welding Institute (UK). The books four part treatment starts with a clear and rigorous exposition of the science of metallurgy including but not limited to: Alloys, Physical Metallurgy, Structure of Materials, Non-Ferrous Materials, Mechanical Properties and Testing of Metals and Heal Treatment of Steels. This is followed by applications: Welding Metallurgy & Welding Processes, Nondestructive Testing, and Codes and Standards. Case studies are included in the book to provide a bridge between theory and the real world of welding engineering. Other topics addressed include: Mechanical Properties and Testing of Metals, Heat Treatment of Steels, Effect of Heat on Material During Welding, Stresses, Shrinkage and Distortion in Welding, Welding, Corrosion Resistant Alloys-Stainless Steel, Welding Defects and Inspection, Codes, Specifications and Standards. *Rules for developing efficient welding designs and fabrication procedures *Expert advice for complying with international codes and standards from the American Welding Society, American Society of Mechanical Engineers, and The Welding Institute(UK) *Practical in-depth instruction for the selection of the materials incorporated in the joint, joint inspection, and the quality control for the final product.WeldingMetallurgyWelding.Metallurgy.671.52Singh Ramesh1594623MiAaPQMiAaPQMiAaPQBOOK9910819935303321Applied welding engineering3915199UNINA04485nam 22006975 450 991025407210332120200630134537.03-319-30515-810.1007/978-3-319-30515-8(CKB)3890000000006140(DE-He213)978-3-319-30515-8(MiAaPQ)EBC4518917(PPN)194077942(EXLCZ)99389000000000614020160502d2016 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierSearch Techniques in Intelligent Classification Systems /by Andrey V. Savchenko1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XIII, 82 p. 28 illus., 19 illus. in color.) SpringerBriefs in Optimization,2190-83543-319-30513-1 Includes bibliographical references at the end of each chapters.1.Intelligent Classification Systems -- 2. Statistical Classification of Audiovisual Data -- 3. Hierarchical Intelligent Classification Systems -- 4. Approximate Nearest Neighbor Search in Intelligent Classification Systems -- 5. Search in Voice Control Systems -- 6. Conclusion. .A unified methodology for categorizing various complex objects is presented in this book. Through probability theory, novel asymptotically minimax criteria suitable for practical applications in imaging and data analysis are examined including the special cases such as the Jensen-Shannon divergence and the probabilistic neural network. An optimal approximate nearest neighbor search algorithm, which allows faster classification of databases is featured. Rough set theory, sequential analysis and granular computing are used to improve performance of the hierarchical classifiers. Practical examples in face identification (including deep neural networks), isolated commands recognition in voice control system and classification of visemes captured by the Kinect depth camera are included. This approach creates fast and accurate search procedures by using exact probability densities of applied dissimilarity measures. This book can be used as a guide for independent study and as supplementary material for a technically oriented graduate course in intelligent systems and data mining. Students and researchers interested in the theoretical and practical aspects of intelligent classification systems will find answers to: - Why conventional implementation of the naive Bayesian approach does not work well in image classification? - How to deal with insufficient performance of hierarchical classification systems? - Is it possible to prevent an exhaustive search of the nearest neighbor in a database?SpringerBriefs in Optimization,2190-8354Mathematical optimizationPattern perceptionMachinerySystem theoryPotential theory (Mathematics)Optimizationhttps://scigraph.springernature.com/ontologies/product-market-codes/M26008Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XMachinery and Machine Elementshttps://scigraph.springernature.com/ontologies/product-market-codes/T17039Systems Theory, Controlhttps://scigraph.springernature.com/ontologies/product-market-codes/M13070Complex Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/M13090Potential Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/M12163Mathematical optimization.Pattern perception.Machinery.System theory.Potential theory (Mathematics)Optimization.Pattern Recognition.Machinery and Machine Elements.Systems Theory, Control.Complex Systems.Potential Theory.005.74Savchenko Andrey Vauthttp://id.loc.gov/vocabulary/relators/aut756076MiAaPQMiAaPQMiAaPQBOOK9910254072103321Search techniques in intelligent classification systems1523621UNINA