Advanced Mathematical Techniques in Engineering Sciences

Advanced Mathematical Techniques in Engineering Sciences /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Ram, Mangey (Editor), Davim, J. Paulo (Editor)
Format: Electronic eBook
Language:English
Published: Boca Raton, FL : CRC Press, 2018.
Edition:First edition.
Series:Science, technology, and management series.
Subjects:
Production management.
Mathematical physics.
Mechanics, Applied.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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245 0 0 |a Advanced Mathematical Techniques in Engineering Sciences /  |c editors, Mangey Ram, J. Paulo Davim. 
250 |a First edition. 
264 1 |a Boca Raton, FL :  |b CRC Press,  |c 2018. 
300 |a 1 online resource :  |b text file, PDF. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Science, technology, and management series 
504 |a Includes bibliographical references and index. 
505 0 0 |a Machine generated contents note:   |g 1.1.  |t Designation /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.2.  |t Laplace transform and operations mapping /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.3.  |t Linear substitutions /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.4.  |t Differentiation and integration /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.5.  |t Multiplication and curtailing /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.6.  |t image of a unit function and some other simple functions /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.7.  |t Examples of solving some problems of mechanics /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.8.  |t Laplace transform in problems of studying oscillation of rods /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.9.  |t Relationship between the velocities of the particles of an elementary volume of a cylindrical rod with stresses /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.10.  |t inertial disk rotating at the end of the rod /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.11.  |t Equations of torsional oscillations of a disk /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.12.  |t Equations of longitudinal oscillations of a disk /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.13.  |t Application of the Laplace transform in engineering technology /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.13.1.  |t Method of studying oscillations of the velocities of motion and stresses in mechanisms containing rod systems /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.13.2.  |t Features of functioning of a drive with a long force line /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 1.13.3.  |t Investigation of dynamic features of the system in the technologies of deephole machining /  |r Leonid Kondratenko /  |r Lubov Mironova --   |t References /  |r Leonid Kondratenko /  |r Lubov Mironova --   |g 2.1.  |t Introduction /  |r Alka Munjal /  |r Smita Sonker --   |g 2.2.  |t Periodic functions /  |r Alka Munjal /  |r Smita Sonker --   |g 2.3.  |t Orthogonality of sine and cosine functions /  |r Alka Munjal /  |r Smita Sonker --   |g 2.4.  |t Fourier series /  |r Alka Munjal /  |r Smita Sonker --   |g 2.5.  |t Dirichlet's theorem /  |r Alka Munjal /  |r Smita Sonker --   |g 2.6.  |t Riemann-Lebesgue lemma /  |r Alka Munjal /  |r Smita Sonker --   |g 2.7.  |t Term-wise differentiation /  |r Alka Munjal /  |r Smita Sonker --   |g 2.8.  |t Convergence of Fourier series /  |r Alka Munjal /  |r Smita Sonker --   |g 2.9.  |t Small order /  |r Alka Munjal /  |r Smita Sonker --   |g 2.10.  |t Big "oh" for functions /  |r Alka Munjal /  |r Smita Sonker --   |g 2.11.  |t Fourier analysis and Fourier transform /  |r Alka Munjal /  |r Smita Sonker --   |g 2.12.  |t Fourier transform /  |r Alka Munjal /  |r Smita Sonker --   |g 2.13.  |t Gibbs phenomenon /  |r Alka Munjal /  |r Smita Sonker --   |g 2.13.1.  |t Gibbs phenomenon with an example /  |r Alka Munjal /  |r Smita Sonker --   |g 2.13.2.  |t Results related to Gibbs phenomenon /  |r Alka Munjal /  |r Smita Sonker --   |g 2.14.  |t Trigonometric Fourier approximation /  |r Alka Munjal /  |r Smita Sonker --   |g 2.15.  |t Summability /  |r Alka Munjal /  |r Smita Sonker --   |g 2.15.1.  |t Ordinary summability /  |r Alka Munjal /  |r Smita Sonker --   |g 2.15.2.  |t Absolute summability /  |r Alka Munjal /  |r Smita Sonker --   |g 2.15.3.  |t Strong summability /  |r Alka Munjal /  |r Smita Sonker --   |g 2.16.  |t Methods for summability /  |r Alka Munjal /  |r Smita Sonker --   |g 2.17.  |t Regularity condition /  |r Alka Munjal /  |r Smita Sonker --   |g 2.18.  |t Norm /  |r Alka Munjal /  |r Smita Sonker --   |g 2.19.  |t Modulus of continuity /  |r Alka Munjal /  |r Smita Sonker --   |g 2.20.  |t Lipschitz condition /  |r Alka Munjal /  |r Smita Sonker --   |g 2.21.  |t Various Lipschitz classes /  |r Alka Munjal /  |r Smita Sonker --   |g 2.22.  |t Degree of approximation /  |r Alka Munjal /  |r Smita Sonker --   |g 2.23.  |t Fourier series and music /  |r Alka Munjal /  |r Smita Sonker --   |g 2.24.  |t Applications and significant uses /  |r Alka Munjal /  |r Smita Sonker --   |t References /  |r Alka Munjal /  |r Smita Sonker --   |g 3.1.  |t Introduction: Soft computing /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.2.  |t Fuzzy logic /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.2.1.  |t Evolution of fuzzy logic /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.  |t Fuzzy sets /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.1.  |t Equal fuzzy sets /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.2.  |t Membership function /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.2.1.  |t Z-Shaped membership function /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.2.2.  |t Triangular membership function /  |r Dinesh Bisht /  |r Pankaj Kumar Srivastava /  |r Mangey Ram --   |g 3.3.2.3.  |t Trapezoidal membership function /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.3.2.4.  |t Gaussian membership function /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.4.  |t Fuzzy rule base system /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.  |t Fuzzy defuzzification /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.1.  |t Center of area (CoA) method /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.2.  |t Max-membership function /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.3.  |t Weighted average method /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.4.  |t Mean-max method /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.5.5.  |t Center of sums /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.6.  |t Comparison of crisp to fuzzy /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.7.  |t Examples of uses of fuzzy logic /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.  |t Artificial neural networks /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.1.  |t Artificial neurons /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.2.  |t Firing rule /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.3.  |t Different types of neural networks /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.3.1.  |t Feedback ANN /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.3.2.  |t Feed-forward ANN /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.8.3.3.  |t Classification-prediction ANN /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.9.  |t Training of neural networks /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.9.1.  |t Supervised training /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.9.2.  |t Unsupervised training /  |r Mangey Ram /  |r Dinesh Bisht /  |r Pankaj Kumar Srivastava --   |g 3.9.3.  |t Reinforced training /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.10.  |t Adaptive neuro fuzzy inference system /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.11.  |t Genetic algorithms /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.12.  |t Working of genetic algorithm /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 3.13.  |t Applications of soft computing /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |t References /  |r Pankaj Kumar Srivastava /  |r Mangey Ram /  |r Dinesh Bisht --   |g 4.1.  |t Introduction /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.2.  |t Preliminaries /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.2.1.  |t Concepts of solution /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.3.  |t Mathematical model /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.  |t Solution procedure /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.1.  |t Fuzzy programming /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.2.  |t Goal programming /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.3.  |t Revised multi-choice programming /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.4.  |t Vogel approximation method /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.4.5.  |t Merits and demerits /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.5.  |t Numerical example /  |r Gurupada Maity /  |r Sankar Kumar Roy --   |g 4.5.1.  |t Fuzzy programming /  |r Gurupada Maity /  |r Sankar Kumar Roy --   |g 4.5.2.  |t Goal programming /  |r Gurupada Maity /  |r Sankar Kumar Roy --   |g 4.5.3.  |t Revised multi-choice goal programming /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.5.4.  |t Vogel approximation method /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.6.  |t Comparison /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 4.7.  |t Conclusion and future study /  |r Gurupada Maity /  |r Sankar Kumar Roy --   |t Acknowledgment /  |r Gurupada Maity /  |r Sankar Kumar Roy --   |t References /  |r Sankar Kumar Roy /  |r Gurupada Maity --   |g 5.1.  |t Introduction /  |r Boby John /  |r K.K. Chowdhury --   |g 5.2.  |t Simultaneous optimization of multiple characteristics /  |r K.K. Chowdhury /  |r Boby John --   |g 5.2.1.  |t Derringer's desirability function method /  |r K.K. Chowdhury /  |r Boby John --   |g 5.2.2.  |t Taguchi's loss function approach /  |r Boby John /  |r K.K. Chowdhury --   |g 5.2.3.  |t Fuzzy logic approach /  |r Boby John /  |r K.K. Chowdhury --   |g 5.2.4.  |t Dual-response surface methodology /  |r K.K. Chowdhury /  |r Boby John --   |g 5.3.  |t Data collection and modeling /  |r Boby John /  |r K.K. Chowdhury --   |g 5.4.  |t Optimization /  |r Boby John /  |r K.K. Chowdhury --   |g 5.5.  |t Validation /  |r Boby John /  |r K.K. Chowdhury --   |g 5.6.  |t Conclusion /  |r K.K. Chowdhury /  |r Boby John --   |t References /  |r K.K. Chowdhury /  |r Boby John --   |g 6.1.  |t Introduction /  |r Sangeeta Pant /  |r S.B. Singh /  |r Shshank Chaube /  |r Anuj Kumar --   |g 6.2.  |t Basic concept of time-dependent CBFS and some definitions /  |r Sangeeta Pant /  |r S.B. Singh /  |r Shshank Chaube /  |r Anuj Kumar --   |g 6.2.1.  |t Time-dependent CBFS /  |r Shshank Chaube /  |r S.B. Singh /  |r Sangeeta Pant /  |r Anuj Kumar --   |g 6.2.2.  |t Normal CBFS /  |r Shshank Chaube /  |r S.B. Singh /  |r Anuj Kumar /  |r Sangeeta Pant --   |g 6.2.3.  |t Convex CBFS /  |r Sangeeta Pant /  |r Anuj Kumar /  |r Shshank Chaube /  |r S.B. Singh --   |g 6.2.4.  |t Conflicting bifuzzy number /  |r Shshank Chaube /  |r S.B. Singh /  |r Sangeeta Pant /  |r Anuj Kumar --   |g 6.2.5.  |t (α, β)-Cut of a time-dependent CBFS /  |r Shshank Chaube /  |r S.B. Singh /  |r Sangeeta Pant /  |r Anuj Kumar --   |g 6.2.6.  |t Triangular time-dependent CBFS /  |r Shshank Chaube /  |r S.B. Singh /  |r Anuj Kumar /  |r Sangeeta Pant -- 
505 0 0 |a Contents note continued:   |g 6.3.  |t Problem formulation /  |r Shshank Chaube /  |r S.B. Singh /  |r Sangeeta Pant /  |r Anuj Kumar --   |g 6.4.  |t Reliability evaluation with time-dependent CBFN /  |r Shshank Chaube /  |r Sangeeta Pant /  |r S.B. Singh /  |r Anuj Kumar --   |g 6.5.  |t Reliability evaluation of series and parallel system having components following time-dependent conflicting bifuzzy failure rate /  |r Shshank Chaube /  |r S.B. Singh /  |r Sangeeta Pant /  |r Anuj Kumar --   |g 6.5.1.  |t Series system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.5.2.  |t Parallel system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.5.3.  |t Parallel-series system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.5.4.  |t Series-parallel system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.6.  |t Examples /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.6.1.  |t Series system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.6.2.  |t Parallel system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.6.3.  |t Parallel-series system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.6.4.  |t Series-parallel system /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 6.7.  |t Conclusion /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |t References /  |r Shshank Chaube /  |r Anuj Kumar /  |r Sangeeta Pant /  |r S.B. Singh --   |g 7.1.  |t Introduction /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.2.  |t Model description /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.3.  |t Parametric estimation method /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.3.1.  |t Single failure-occurrence time data case /  |r Yasuhiro Saito /  |r Tadashi Dohi --   |g 7.3.2.  |t Multiple failure-occurrence time data case /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.  |t Nonparametric estimation methods /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.1.  |t Constrained nonparametric ML estimator /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.1.1.  |t Single failure-occurrence time data case /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.1.2.  |t Multiple failure-occurrence time data case /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.2.  |t Kernel-based approach /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.2.1.  |t Single failure-occurrence time data case /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.4.2.2.  |t Multiple failure-occurrence time data case /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.5.  |t Numerical examples /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.5.1.  |t Simulation experiments with single minimal repair data /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.5.2.  |t Real example with multiple minimal repair data sets /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 7.6.  |t Conclusions /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |t References /  |r Tadashi Dohi /  |r Yasuhiro Saito --   |g 8.1.  |t Introduction /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.2.  |t YouTube view count: A twofold perspective /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.3.  |t Literature review /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.4.  |t Model development /  |r N. Aggrawal /  |r A. Arora /  |r A. Anand /  |r M.S. Irshad --   |g 8.4.1.  |t Model I: Linear growth /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.4.2.  |t Model II: Exponential growth /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.4.3.  |t Model III: Repeat viewing /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.5.  |t Data analysis and model validation /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |g 8.6.  |t Conclusion /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand /  |r A. Arora --   |t References /  |r A. Arora /  |r N. Aggrawal /  |r M.S. Irshad /  |r A. Anand --   |g 9.1.  |t Introduction /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.2.  |t Mathematical modeling /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.2.1.  |t Early market adoption model /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.2.2.  |t Main market adoption model /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.2.3.  |t Total adoption modeling /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.3.  |t Parameter estimation /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 9.4.  |t Discussion and summary /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |t References /  |r A. Anand /  |r O. Singh /  |r R. Aggarwal --   |g 10.1.  |t Introduction /  |r Piotr Kulczycki --   |g 10.2.  |t Methodology of kernel estimators /  |r Piotr Kulczycki --   |g 10.2.1.  |t Modification of smoothing parameter /  |r Piotr Kulczycki --   |g 10.2.2.  |t Support boundary /  |r Piotr Kulczycki --   |g 10.3.  |t Identification of atypical elements /  |r Piotr Kulczycki --   |g 10.3.1.  |t Basic version of the procedure /  |r Piotr Kulczycki --   |g 10.3.2.  |t Extended pattern of population /  |r Piotr Kulczycki --   |g 10.3.3.  |t Equal-sized patterns of atypical and typical elements /  |r Piotr Kulczycki --   |g 10.3.4.  |t Comments for Section 10.3 /  |r Piotr Kulczycki --   |g 10.4.  |t Clustering /  |r Piotr Kulczycki --   |g 10.4.1.  |t Procedure /  |r Piotr Kulczycki --   |g 10.4.2.  |t Influence of the parameters values on obtained results /  |r Piotr Kulczycki --   |g 10.4.3.  |t Comments for Section 10.4 /  |r Piotr Kulczycki --   |g 10.5.  |t Classification /  |r Piotr Kulczycki --   |g 10.5.1.  |t Bayes classification /  |r Piotr Kulczycki --   |g 10.5.2.  |t Correction of values of smoothing parameter and modification intensity /  |r Piotr Kulczycki --   |g 10.5.3.  |t Reduction to pattern sizes /  |r Piotr Kulczycki --   |g 10.5.4.  |t Structure for nonstationary patterns (concept drift) /  |r Piotr Kulczycki --   |g 10.5.5.  |t Comments for Section 10.5 /  |r Piotr Kulczycki --   |g 10.6.  |t Example practical application and final comments /  |r Piotr Kulczycki --   |t Acknowledgments /  |r Piotr Kulczycki --   |t References /  |r Piotr Kulczycki --   |g 11.1.  |t Introduction /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.2.  |t Two-parameter Weibull distribution /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.3.  |t Lower statistical γ-content tolerance limit with expected (1 - α)-confidence /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.4.  |t Upper statistical γ-content tolerance limit with expected (1 - α)-confidence /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.5.  |t Lower statistical (1 - α)-expectation tolerance limit /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.6.  |t Upper statistical (1 - α)-expectation tolerance limit /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.7.  |t Numerical example 1 /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.8.  |t Numerical example 2 /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 11.9.  |t Conclusion /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |t References /  |r N.A. Nechval /  |r G. Berzins /  |r K.N. Nechval --   |g 12.1.  |t Introduction /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.2.  |t Kinematics and dynamics of the biped robot /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.2.1.  |t Dynamic balance margin while ascending the staircase /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.2.2.  |t Dynamic balance margin while descending the staircase /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.2.3.  |t Design of torque-based PID controllers for the biped robot /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.3.  |t MCIWO-based PID controller /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.4.  |t MCIWO-NN-based PID controller /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.5.  |t Results and discussion /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.5.1.  |t Ascending the staircase /  |r Ravi Kumar Mandava /  |r Pandu R. Vundavilli --   |g 12.5.2.  |t Descending the staircase /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 12.6.  |t Conclusions /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |t References /  |r Pandu R. Vundavilli /  |r Ravi Kumar Mandava --   |g 13.1.  |t Introduction /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.  |t Materials and methods /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.1.  |t Data /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.  |t Machine learning algorithms /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.1.  |t Neural network models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.2.  |t NN model building with R programming tools /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.3.  |t Support vector regression models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.4.  |t Decision tree models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.5.  |t Decision tree model building with R programming tools /  |r Adesh Kumar Sharma /  |r Ravinder Malhotra /  |r Atish Kumar Chakravarty --   |g 13.2.2.6.  |t Random forest models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.7.  |t Linear regression models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.2.2.8.  |t Model evaluation error metrics /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.  |t Results and discussion /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.1.  |t Neural Network Models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.2.  |t Support vector regression models /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.3.  |t Decision tree regression model /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.4.  |t Random forest regression model /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.5.  |t Linear model for regression /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.3.6.  |t Machine learning models vis-a-vis linear regression model /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |g 13.4.  |t Conclusion /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra --   |t References /  |r Adesh Kumar Sharma /  |r Atish Kumar Chakravarty /  |r Ravinder Malhotra -- 
505 0 0 |a Contents note continued:   |g 14.1.  |t Introduction /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.2.  |t Governing equations /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.2.1.  |t Spalart-Allrnaras model /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.2.2.  |t k-e Model /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.2.3.  |t SST k-w model /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.2.4.  |t k-e-C-f Model /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.3.  |t Grid independence test and solution methodology /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.4.  |t Results and discussion /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 14.5.  |t Conclusions /  |r Michele Trancossi /  |r Maharshi Subhash --   |t Acknowledgments /  |r Michele Trancossi /  |r Maharshi Subhash --   |t Nomenclature /  |r Michele Trancossi /  |r Maharshi Subhash --   |t References /  |r Michele Trancossi /  |r Maharshi Subhash --   |g 15.1.  |t Introduction /  |r Geeta Arora --   |g 15.2.  |t Collocation method /  |r Geeta Arora --   |g 15.3.  |t B-spline /  |r Geeta Arora --   |g 15.3.1.  |t B-spline of degree zero /  |r Geeta Arora --   |g 15.3.2.  |t First-degree (linear) B-spline /  |r Geeta Arora --   |g 15.3.3.  |t Second-degree (quadratic) B-spline /  |r Geeta Arora --   |g 15.4.  |t Characteristics of B-spline basis functions /  |r Geeta Arora --   |g 15.5.  |t Types of B-spline /  |r Geeta Arora --   |g 15.5.1.  |t Trigonometric B-spline basis functions /  |r Geeta Arora --   |g 15.5.2.  |t Exponential B-spline basis functions /  |r Geeta Arora --   |g 15.6.  |t Methodology: Collocation method using B-spline basis function /  |r Geeta Arora --   |g 15.7.  |t Numerical solution of advection diffusion equation using collocation method /  |r Geeta Arora --   |g 15.7.1.  |t Using B-spline basis functions /  |r Geeta Arora --   |g 15.7.2.  |t Using trigonometric B-spline basis functions /  |r Geeta Arora --   |g 15.8.  |t Numerical example /  |r Geeta Arora --   |t References /  |r Geeta Arora --   |g 16.1.  |t Introduction /  |r Neelima Bhengra --   |g 16.2.  |t Problem formulation and its solution /  |r Neelima Bhengra --   |g 16.2.1.  |t Solution for the lower highly anisotropic half-space /  |r Neelima Bhengra --   |g 16.2.2.  |t Solution for the upper fluid-saturated poroelastic half-space /  |r Neelima Bhengra --   |g 16.3.  |t Boundary conditions /  |r Neelima Bhengra --   |g 16.4.  |t Solution of the first-order approximation of the corrugation /  |r Neelima Bhengra --   |g 16.5.  |t Solution for second-order approximation of the corrugation /  |r Neelima Bhengra --   |g 16.6.  |t Special case of a simple harmonic interface /  |r Neelima Bhengra --   |g 16.7.  |t Particular cases for special case /  |r Neelima Bhengra --   |g 16.8.  |t Energy distribution /  |r Neelima Bhengra --   |g 16.9.  |t Numerical discussion and results /  |r Neelima Bhengra --   |g 16.9.1.  |t Effect of corrugation amplitude /  |r Neelima Bhengra --   |g 16.9.2.  |t Effect of corrugation wavelength /  |r Neelima Bhengra --   |g 16.9.3.  |t Effect of frequency factor /  |r Neelima Bhengra --   |g 16.9.4.  |t Influence of initial stress parameter on poroelastic half-space /  |r Neelima Bhengra --   |g 16.9.5.  |t Influence of initial stress parameter on highly anisotropic half-space /  |r Neelima Bhengra --   |g 16.10.  |t Concluding remarks /  |r Neelima Bhengra --   |t References /  |r Neelima Bhengra. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
650 0 |a Production management. 
650 0 |a Mathematical physics. 
650 0 |a Mechanics, Applied. 
700 1 |a Ram, Mangey,  |e editor. 
700 1 |a Davim, J. Paulo,  |e editor. 
710 2 |a ProQuest (Firm) 
776 0 8 |z 9781351371896  |z 9781351371889  |z 9781315148977  |z 9781351371872  |z 9781138554399 
830 0 |a Science, technology, and management series. 
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919 |a .ulebk  |b 2017-02-14 
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