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Institution

Springer Science+Business Media

CompanyBerlin, Germany
About: Springer Science+Business Media is a company organization based out in Berlin, Germany. It is known for research contribution in the topics: Medicine & Tolerability. The organization has 1411 authors who have published 2397 publications receiving 41477 citations.


Papers
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Journal ArticleDOI
TL;DR: The four Purposes of Multidimensional Scaling, Special Solutions, Degeneracies, and Local Minima, and Avoiding Trivial Solutions in Unfolding are explained.
Abstract: Fundamentals of MDS.- The Four Purposes of Multidimensional Scaling.- Constructing MDS Representations.- MDS Models and Measures of Fit.- Three Applications of MDS.- MDS and Facet Theory.- How to Obtain Proximities.- MDS Models and Solving MDS Problems.- Matrix Algebra for MDS.- A Majorization Algorithm for Solving MDS.- Metric and Nonmetric MDS.- Confirmatory MDS.- MDS Fit Measures, Their Relations, and Some Algorithms.- Classical Scaling.- Special Solutions, Degeneracies, and Local Minima.- Unfolding.- Unfolding.- Avoiding Trivial Solutions in Unfolding.- Special Unfolding Models.- MDS Geometry as a Substantive Model.- MDS as a Psychological Model.- Scalar Products and Euclidean Distances.- Euclidean Embeddings.- MDS and Related Methods.- Procrustes Procedures.- Three-Way Procrustean Models.- Three-Way MDS Models.- Modeling Asymmetric Data.- Methods Related to MDS.

3,096 citations

Book
20 Feb 1997
TL;DR: In this article, the Cox Proportional Hazards model and its characteristics are evaluated and the Stratified Cox Procedure for Time-Dependent Variables is extended for time-dependent variables.
Abstract: Introduction to Survival Analysis.- Kaplan-Meier Survival Curves and the Log-Rank Test.- The Cox Proportional Hazards Model and Its Characteristics.- Evaluating the Proportional Hazards Assumption.- The Stratified Cox Procedure.- Extension of the Cox Proportional Hazards Model for Time-Dependent Variables.- Parametric Survival Models.- Recurrent Events Survival Analysis.- Competing Risks Survival Analysis.

2,450 citations

01 Jan 1997
TL;DR: In this paper, a majorization algorithm for solving multidimensional scaling problems is proposed. But the algorithm is not suitable for solving MDS problems in general, and it requires a large number of dimensions.
Abstract: Fundamentals of MDS.- The Four Purposes of Multidimensional Scaling.- Constructing MDS Representations.- MDS Models and Measures of Fit.- Three Applications of MDS.- MDS and Facet Theory.- How to Obtain Proximities.- MDS Models and Solving MDS Problems.- Matrix Algebra for MDS.- A Majorization Algorithm for Solving MDS.- Metric and Nonmetric MDS.- Confirmatory MDS.- MDS Fit Measures, Their Relations, and Some Algorithms.- Classical Scaling.- Special Solutions, Degeneracies, and Local Minima.- Unfolding.- Unfolding.- Avoiding Trivial Solutions in Unfolding.- Special Unfolding Models.- MDS Geometry as a Substantive Model.- MDS as a Psychological Model.- Scalar Products and Euclidean Distances.- Euclidean Embeddings.- MDS and Related Methods.- Procrustes Procedures.- Three-Way Procrustean Models.- Three-Way MDS Models.- Modeling Asymmetric Data.- Methods Related to MDS.

1,733 citations

Journal ArticleDOI
TL;DR: This is a comprehensive book discussing several methods for the identification of nonlinear systems, ranging from linear optimization techniques to fuzzy logic and nonlinear adaptive control, and Nelles has certainly described an extensive number of results.
Abstract: This is a comprehensive book discussing several methods for the identification of nonlinear systems. Identification is extremely relevant in applications and only recently has much ongoing research addressed the pressing problem of identifying systems with nonlinearities. In this respect, the book is timely as it is a collection of results from many different areas in applied science, ranging from linear optimization techniques to fuzzy logic and nonlinear adaptive control. The declared aim is `to provide engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification'. At the same time, the author wishes to enable users to apply the methods illustrated in the book. The book is well structured and divided into four distinct parts. The first part is entirely devoted to an overview of the main optimization techniques for nonlinear problems. Least squares methods and other classical strategies such as general gradient-based algorithms are discussed. While the presentation is clear, it is too wordy at times, making it difficult to appreciate the key issues involved. A set of diagrams and summarizing tables is included, though, to improve the overall clarity and highlight similarities and differences. The second part is mostly devoted to static models such as linear, polynomial and look-up table models. The main emphasis is on neural networks and fuzzy logic. The results are clearly expounded but the aim of giving a general overview of too many different approaches in some cases hampers the clarity of the exposition. Neuro-fuzzy models are presented in chapter 12 and further detailed in chapters 13 and 14 where local linear Neuro-Fuzzy models are discussed. In particular, chapter 13 focuses on methods proposed by the author. Despite their usefulness, I found that the choice of dedicating two entire chapters to such methods causes a slight imbalance in the presentation. Up to chapter 13, the discussion is quite well balanced and different methods are given the space needed to expound the main results. Unlike the other strategies, in my view, local neuro-fuzzy approaches are treated in far too much detail. This is beyond the scope of the book, which is that of giving a general balanced overview of all possible results. A summary of the second part is reported in chapter 15 where the author reinforces the view that local neuro-fuzzy methods should be more widely applied for static modelling problems. Dynamic Models are the subject of the third main section of the book. Linear dynamic system identification is discussed in chapter 16, where time series models are presented together with multivariable methods and other linear approaches. Nonlinear dynamic systems are considered in chapter 17 and are followed by classical polynomial approaches in chapter 18. Neural and fuzzy dynamic models are treated together with local neuro-fuzzy dynamic systems in the remaining chapters of this third part. Again particular emphasis is given to local neuro-fuzzy systems which have been the subject of research and development by the author. Unfortunately, this part does not include a chapter dedicated to summarizing the main results expounded. It must be noted though that many diagrams and schematics do help in highlighting the main results. Nevertheless, an extensive summary such as the one included at the end of the second part would have been useful. As I have indicated, Nelles has certainly described an extensive number of results in the book. On the other hand, more recent methods based on novel developments of Nonlinear Dynamics such as nonlinear time series analysis, which have been successfully used to identify nonlinear systems, have not been included in the book. I hope they will be incorporated in later editions, as they have the potential to play an important role in the identification of complex models. Applications are discussed in the fourth and last part of the book. The problems presented are interesting but again it becomes apparent that local linear neuro-fuzzy methods are somehow the author's preferred method. This bias, which might well be motivated by the author's experience, should in my view be counterbalanced by applications showing the use of other methods. Some are indeed included in the final chapters of the book but I would have liked to see a few more problems. Two appendices recall some useful results from linear algebra, vector calculus and statistics and are well suited to a general readership. An impressive reference list of more than 400 items completes the book, representing an invaluable starting point for further research and details. As mentioned in the Preface, throughout the book Nelles tries to keep the mathematical description to a basic level. This indeed makes the textbook accessible to a wider audience. Unfortunately, it also results at times in lengthy, wordy descriptions of the most intricate approaches. As a consequence, users who wish to apply some of the methods discussed to problems that interest them will often find that they need to look up further details from other sources. In this respect, the extensive reference list at the end of the book will certainly be helpful. Despite this disadvantage, the book is certainly an invaluable archive of available strategies for nonlinear system identification, which will undoubtedly help readers with the choice of the particular method to use. In conclusion, as I have indicated, I found the book a well-packaged overview of the main results concerned with nonlinear system identification. But I believe that the description is wordy at times and not rigorous enough. Contrary to what is stated in the Preface, I believe that rather than being a self-contained book, readers will undoubtedly need to look up further references to be able to make use of the methods illustrated. On the other hand, the book should be a useful reference for students. It certainly deserves to be included in the reading list of any course on nonlinear system identification and optimization. Mario di Bernado

1,451 citations

Journal ArticleDOI
TL;DR: Large Eddy Simulation (LES) is an approach to compute turbulent flows based on resolving the unsteady large-scale motion of the fluid while the impact of the small-scale turbulence on the large scales is accounted for by a sub-grid scale model as mentioned in this paper.
Abstract: Large Eddy Simulation (LES) is an approach to compute turbulent flows based on resolving the unsteady large-scale motion of the fluid while the impact of the small-scale turbulence on the large scales is accounted for by a sub-grid scale model. This model distinguishes LES from any other method and reduces the computational demands compared with a Direct Numerical Simulation. On the other hand, the cost typically is still at least an order of magnitude larger than for steady Reynolds-averaged computations. The LES approach is attractive when statistical turbulence models fail, when insight into the vortical dynamics or unsteady forces on a body is desired, or when additional features are involved such as large-scale mixing, particle transport, sound generation etc. In recent years the rapid increase of computer power has made LES accessible to a broader scientific community, and this is reflected in an abundance of papers on the method and its applications. Still, however, some fundamental aspects of LES are not conclusively settled, a fact residing in the intricate coupling between mathematical, physical, numerical and algorithmic issues. In this situation it is of great importance to gain an overview of the available approaches and techniques. Pierre Sagaut, in the style of a French encyclopedist, gives a very complete and exhaustive treatment of the different kinds of sub-grid scale models which have been developed so far. After discussing the separation into resolved and unresolved scales and its application to the Navier-Stokes equations, more than 140 pages are directly devoted to the description of sub-grid scale models. They are classified according to different criteria, which helps the reader to find his or her way through the arsenal of reasonings. The theoretical framework for which these models have mostly been developed is isotropic turbulence. The required notions from classical turbulence theory are summarized together with notions from EDQNM theory in two concise and helpful appendices. Further sections deal with numerical and implementational issues, boundary conditions and validation practice. A final section assembles a few key applications, cumulating in a condensed list of some general experiences gained so far. The book very wisely concentrates on issues particular to LES, which to a large extent is sub-grid scale modelling. Classical issues of CFD, such as numerical discretization schemes, solution procedures etc, or post-processing are not addressed. Limiting himself to incompressible, non-reactive flows, the author succeeds in describing the fundamental issues in great detail, thus laying the foundations for the understanding of more complex situations. The presentation is essentially theoretical and the reader should have some prior knowledge of turbulence theory and Fourier transforms. The text itself is well written and generally very clear. A pedagogical effort is made in several places, e.g. when an overview over a group of models is given before these are described in detail. A few typing errors and technical details should be amended in a second edition, though, such as the statement that a filter which is not a projector is invertible (p 12), but this is not detrimental to the quality of the text. Overall the book is a very relevant contribution to the field of LES and I read it with pleasure and benefit. It constitutes a worthy reference book for scientists and engineers interested in or practising LES and may serve as a textbook for a postgraduate course on the subject. Jochen Frohlich

771 citations


Authors

Showing all 1486 results

NameH-indexPapersCitations
Per Bech7848225241
Maria João Saraiva6830214775
Abdullah H Baqui6649315056
Aldo Roda6451616520
David C. Muddiman6332712861
Yehuda Bock6217113236
Andreas Maercker6246120463
Elvar Theodorsson6142513695
Gillian M. Keating6037811969
Greg L. Plosker6027611297
Pierre Sagaut5729213070
Rolf Isermann5564820474
Gary Koop5529114115
Gérard Hopfgartner551889623
Martin Voracek5238117273
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202318
2022280
2021226
2020209
2019209
2018227