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[null,"1.","Record Nr.",null,"UNINA9910457687003321"]
[null,null,"Autore",null,"Bose Sudhangshu"]
[null,null,"Titolo",null,"High temperature coatings [[electronic resource] /] / Sudhangshu Bose"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam ; ; Boston, : Elsevier, c2007"]
[null,null,"ISBN",null,"1-280-75210-6"]
[{"$nulls":4},"9786610752102"]
[{"$nulls":4},"0-08-046955-8"]
[null,null,"Descrizione fisica",null,"1 online resource (313 p.)"]
[null,null,"Disciplina",null,"671.7/3"]
[null,null,"Soggetti",null,"Refractory coating"]
[{"$nulls":4},"Heat resistant materials"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"Front Cover; High Temperature Coatings; Copyright Page; Table of Contents; ABOUT THE AUTHOR; PREFACE; Chapter 1 INTRODUCTION; 1.1 HIGH-TEMPERATURE ENVIRONMENT; REFERENCES; Chapter 2 FUNDAMENTAL CONCEPTS; 2.1 THERMODYNAMIC CONCEPTS; 2.2 CONCEPT OF KINETICS; 2.3 CRYSTAL STRUCTURE; 2.4 EQUILIBRIUM PHASES; REFERENCES; Chapter 3 SUBSTRATE ALLOYS; 3.1 TEMPERATURE CAPABILITY OF METALS AND ALLOYS; 3.2 STRENGTHENING MECHANISMS; 3.3 TITANIUM ALLOYS; 3.4 STEELS; 3.5 NICKEL-IRON ALLOYS; 3.6 NICKEL AND COBALT BASE SUPERALLOYS; 3.7 NEED FOR COATINGS; REFERENCES; Chapter 4 OXIDATION; 4.1 OXIDATION PROCESS"]
[{"$nulls":4},"4.2 OXIDATION TESTING AND EVALUATION4.3 OXIDATION OF ALLOYS; 4.4 ROLES OF SPECIFIC ALLOYING CONSTITUENTS; 4.5 OXIDATION IN THE PRESENCE OF WATER VAPOR; 4.6 OXIDATION OF POLYCRYSTALLINE VERSUS SINGLE-CRYSTAL ALLOYS; REFERENCES; Chapter 5 HIGH-TEMPERATURE CORROSION; 5.1 HOT CORROSION PROCESSES; 5.2 HOT CORROSION OF METALS AND ALLOYS; 5.3 ROLE OF SPECIFIC ALLOYING ELEMENTS IN HOT CORROSION OF NI AND CO BASED ALLOYS AND COATINGS; 5.4 INFLUENCE OF OTHER CONTAMINANTS; 5.5 HOT CORROSION OF TBCs; 5.6 HOT CORROSION-LIKE DEGRADATION; REFERENCES; Chapter 6 OXIDATION- AND CORROSION-RESISTANT COATINGS"]
[{"$nulls":4},"6.1 REQUIREMENTS FOR METALLIC COATINGS6.2 COATING PROCESSES; "]
[{"$nulls":4},"6.3 DIFFUSION COATINGS; 6.4 OVERLAY COATINGS; 6.5 OVERLAY COATINGS BY SPRAY AND ARC PROCESSES; 6.6 OVERLAY COATINGS BY PHYSICAL VAPOR DEPOSITION (PVD); 6.7 RELATIVE OXIDATION AND CORROSION RESISTANCE OF COATINGS; 6.8 MODELING OF OXIDATION AND CORROSION LIFE; 6.9 INTERACTION OF EROSION-OXIDATION AND EROSION-CORROSION; REFERENCES; Chapter 7 THERMAL BARRIER COATINGS (TBCs); 7.1 TEMPERATURE REDUCTION BY TBCs; 7.2 MATERIALS REQUIREMENT FOR TBCs; 7.3 PARTIALLY STABILIZED ZIRCONIA; 7.4 PLASMA SPRAYED TBCs"]
[{"$nulls":4},"7.5 ELECTRON BEAM PHYSICAL VAPOR DEPOSITED (EB-PVD) TBCs7.6 ENVIRONMENTAL BARRIER COATINGS (EBCs); REFERENCES; Chapter 8 NONDESTRUCTIVE INSPECTION (NDI) OF COATINGS; 8.1 NDI TECHNIQUES; REFERENCES; Chapter 9 COATINGS REPAIR; 9.1 LIMITS TO COATINGS REPAIR; 9.2 THE REPAIR PROCESS; 9.3 RECOATING AND MATERIAL RESTORATION; REFERENCES; Chapter 10 FIELD AND SIMULATED FIELD EXPERIENCE; 10.1 GAS TURBINE ENGINE APPLICATION; 10.2 OTHER APPLICATIONS; REFERENCES; APPENDIX; AUTHOR INDEX; SUBJECT INDEX"]
[null,null,null,"Sommario/riassunto","High Temperature Coatings demonstrates how to counteract the thermal effects of the rapid corrosion and degradation of exposed materials and equipment that can occur under high operating temperatures. This is the first true practical guide on the use of thermally-protective coatings for high-temperature applications, including the latest developments in materials used for protective coatings. It covers the make-up and behavior of such materials under thermal stress and the methods used for applying them to specific types of substrates, as well as invaluable advice on inspection and repair of e"]
[null,"2.","Record Nr.",null,"UNINA9910767552603321"]
[null,null,"Titolo",null,"Learning Theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings / / edited by Nader Bshouty, Claudio Gentile"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007"]
[null,null,"ISBN",null,"1-280-94078-6"]
[{"$nulls":4},"9786610940783"]
[{"$nulls":4},"3-540-72927-5"]
[null,null,"Edizione",null,"[1st ed. 2007.]"]
[null,null,"Descrizione fisica",null,"1 online resource (644 p.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 4539"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Machine theory"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Formal Languages and Automata Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Invited Presentations -- Property Testing: A Learning Theory Perspective -- Spectral Algorithms for Learning and Clustering -- Unsupervised, Semisupervised and Active Learning I -- Minimax Bounds for Active Learning -- Stability of k-Means Clustering -- Margin Based Active Learning -- Unsupervised, Semisupervised and Active Learning II -- Learning Large-Alphabet and Analog Circuits with Value Injection Queries -- Teaching Dimension and the Complexity of Active Learning -- Multi-view Regression Via Canonical Correlation Analysis -- Statistical Learning Theory -- Aggregation by Exponential Weighting and Sharp Oracle Inequalities -- Occam’s Hammer -- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector -- Suboptimality of Penalized Empirical Risk Minimization in Classification -- Transductive Rademacher Complexity and Its Applications -- Inductive Inference -- U-Shaped, Iterative, and Iterative-with-Counter Learning -- Mind Change Optimal Learning of Bayes Net Structure -- Learning Correction Grammars -- Mitotic "]
[{"$nulls":4},"Classes -- Online and Reinforcement Learning I -- Regret to the Best vs. Regret to the Average -- Strategies for Prediction Under Imperfect Monitoring -- Bounded Parameter Markov Decision Processes with Average Reward Criterion -- Online and Reinforcement Learning II -- On-Line Estimation with the Multivariate Gaussian Distribution -- Generalised Entropy and Asymptotic Complexities of Languages -- Q-Learning with Linear Function Approximation -- Regularized Learning, Kernel Methods, SVM -- How Good Is a Kernel When Used as a Similarity Measure? -- Gaps in Support Vector Optimization -- Learning Languages with Rational Kernels -- Generalized SMO-Style Decomposition Algorithms -- Learning Algorithms and Limitations on Learning -- Learning Nested Halfspaces and UphillDecision Trees -- An Efficient Re-scaled Perceptron Algorithm for Conic Systems -- A Lower Bound for Agnostically Learning Disjunctions -- Sketching Information Divergences -- Competing with Stationary Prediction Strategies -- Online and Reinforcement Learning III -- Improved Rates for the Stochastic Continuum-Armed Bandit Problem -- Learning Permutations with Exponential Weights -- Online and Reinforcement Learning IV -- Multitask Learning with Expert Advice -- Online Learning with Prior Knowledge -- Dimensionality Reduction -- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections -- Sparse Density Estimation with ?1 Penalties -- ?1 Regularization in Infinite Dimensional Feature Spaces -- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking -- Other Approaches -- Observational Learning in Random Networks -- The Loss Rank Principle for Model Selection -- Robust Reductions from Ranking to Classification -- Open Problems -- Rademacher Margin Complexity -- Open Problems in Efficient Semi-supervised PAC Learning -- Resource-Bounded Information Gathering for Correlation Clustering -- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? -- When Is There a Free Matrix Lunch?."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. The 41 revised full papers presented together with 5 articles on open problems and 2 invited lectures were carefully reviewed and selected from a total of 92 submissions. The papers cover a wide range of topics and are organized in topical sections on unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, other approaches, and open problems."]