03184oam 2200457 450 991048372200332120210623123927.0981-334-524-110.1007/978-981-33-4524-9(CKB)4100000011728476(DE-He213)978-981-33-4524-9(MiAaPQ)EBC6462834(PPN)253252075(EXLCZ)99410000001172847620210623d2021 uy 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMatrix discrete element analysis of geological and geotechnical engineering /Chun Liu1st ed. 2021.Singapore :Springer,[2021]©20211 online resource (XXIII, 294 p. 349 illus., 117 illus. in color.) 981-334-523-3 Chapter 1 Principles and Implementation of DEM -- Chapter 2 The Basic Structure of MatDEM -- Chapter 3 Geometric Modeling and Material Setup -- Chapter 4　Load Settings and Numerical Calculations -- Chapter 5 Postprocessing and System Functions -- Chapter 6 Basic Application of Geotechnical Engineering -- Chapter 7 Rock-Soil Body Discrete Element Test -- Chapter 8　Modeling of Complex 3D Models -- Chapter 9 Numerical Simulations of Dynamic Action -- Chapter 10 Multi-field Coupled Numerical Simulation -- Appendix　Properties, Functions and Frequently Asked Questions.This book introduces the basic structure, modeling methods, numerical calculation processes, post-processing, and system functions of MatDEM, which applies the basic principles and algorithm of the discrete element method. The discrete element method can effectively simulate the discontinuity, inhomogeneity, and large deformation damage of rock and soil. It is widely used in both research and industry. Based on the innovative matrix discrete element computing method, the author developed the high-performance discrete element software MatDEM from scratch, which can handle millions of elements in discrete element numerical simulations. This book also presents several examples of applications in geological and geotechnical engineering, including basic geotechnical engineering problems, discrete element tests, three dimensional landslides, and dynamic and multi-field coupling functions. Teaching videos and the relevant software can be accessed on the MATDEM website (http://matdem.com). The book serves as a useful reference for research and engineering staff, undergraduates, and postgraduates who work in the fields of geology, geotechnical, water conservancy, civil engineering, mining, and physics.Geotechnical engineeringMathematicsDiscrete element methodGeotechnical engineeringMathematics.Discrete element method.624.1510151352Liu Chun877952MiAaPQMiAaPQUtOrBLWBOOK9910483722003321Matrix Discrete Element Analysis of Geological and Geotechnical Engineering2595653UNINA04900oam 2200481 450 991081265640332120190911112729.0981-4531-75-8(OCoLC)860388704(MiFhGG)GVRL8REB(EXLCZ)99255000000116008620130716h20142014 uy 0engurun|---uuuuatxtccrStatistical tests of nonparametric hypotheses asymptotic theory /Odile Pons, French National Institute for Agronomical Research, FranceNew Jersey :World Scientific,[2014]�20141 online resource (x, 293 pages) illustrationsGale eBooksDescription based upon print version of record.981-4531-74-X 1-306-12040-3 Includes bibliographical references and index.Preface; Contents; 1. Introduction; 1.1 Definitions; 1.2 Rank tests and empirical distribution functions; 1.3 Hypotheses of the tests; 1.4 Weak convergence of the test statistics; 1.5 Tests for densities and curves; 1.6 Asymptotic levels of tests; 1.7 Permutation and bootstrap tests; 1.8 Relative efficiency of tests; 2. Asymptotic theory; 2.1 Parametric tests; 2.2 Parametric likelihood ratio tests; 2.3 Likelihood ratio tests against local alternatives; 2.4 Nonparametric likelihood ratio tests; 2.5 Nonparametric tests for empirical functionals; 2.6 Tests of homogeneity2.7 Mixtures of exponential distributions2.8 Nonparametric bootstrap tests; 2.9 Exercises; 3. Nonparametric tests for one sample; 3.1 Introduction; 3.2 Kolmogorov-Smirnov tests for a distribution function; 3.3 Tests for symmetry of a density; 3.3.1 Kolmogorov-Smirnov tests for symmetry; 3.3.2 Semi-parametric tests, with an unknown center; 3.3.3 Rank test for symmetry; 3.4 Tests about the formof a density; 3.5 Goodness of fit test in biased length models; 3.6 Goodness of fit tests for a regression function; 3.7 Tests about the form of a regression function3.8 Tests based on observations by intervals3.8.1 Goodness of fit tests for a density; 3.8.2 Goodness of fit tests for a regression function; 3.8.3 Tests of symmetry for a density; 3.8.4 Tests of a monotone density; 3.9 Exercises; 4. Two-sample tests; 4.1 Introduction; 4.2 Tests of independence; 4.2.1 Kolmogorov-Smirnov and Cramer-von Mises tests; 4.2.2 Tests based on the dependence function; 4.2.3 Tests based on the conditional distribution; 4.3 Test of homogeneity; 4.4 Goodness of fit tests in R2; 4.5 Tests of symmetry for a bivariate density; 4.6 Tests about the form of densities4.7 Comparison of two regression curves4.8 Tests based on observations by intervals; 4.8.1 Test of independence; 4.8.2 Test of homogeneity; 4.8.3 Comparison of two regression curves; 4.9 Exercises; 5. Multi-dimensional tests; 5.1 Introduction; 5.2 Tests of independence; 5.3 Test of homogeneity of k sub-samples; 5.4 Test of homogeneity of k rescaled distributions; 5.5 Test of homogeneity of several variables of Rk; 5.6 Test of equality of marginal distributions; 5.7 Test of exchangeable components for a random variable; 5.8 Tests in single-indexmodels; 5.9 Comparison of k curves5.10 Tests in proportional odds models5.11 Tests for observations by intervals; 5.11.1 Test of independence; 5.11.2 Test of homogeneity; 5.11.3 Comparison of k regression curves; 5.12 Competing risks; 5.13 Tests for Markov renewal processes; 5.14 Tests in Rkn as kn tends to infinity; 5.15 Exercises; 6. Nonparametric tests for processes; 6.1 Introduction; 6.2 Goodness of fit tests for an ergodic process; 6.3 Poisson process; 6.4 Poisson processes with scarce jumps; 6.5 Point processes in R+; 6.6 Marked point processes; 6.7 Spatial Poisson processes6.8 Tests of stationarity for point processesAn overview of the asymptotic theory of optimal nonparametric tests is presented in this book. It covers a wide range of topics: Neyman-Pearson and LeCam's theories of optimal tests, the theories of empirical processes and kernel estimators with extensions of their applications to the asymptotic behavior of tests for distribution functions, densities and curves of the nonparametric models defining the distributions of point processes and diffusions. With many new test statistics developed for smooth curves, the reliance on kernel estimators with bias corrections and the weak convergence of theNonparametric statisticsAsymptotic theoryNonparametric statisticsAsymptotic theory.519.5/4Pons Odile1090182MiFhGGMiFhGGBOOK9910812656403321Statistical tests of nonparametric hypotheses4108211UNINA