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Processing Random Data

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Processing Random Data

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Author : Robert V. Edwards
language : en
Publisher: World Scientific
Release Date : 2006

Processing Random Data written by Robert V. Edwards and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Technology & Engineering categories.

Two features of Processing Random Data differentiate it from other similar books: the focus on computing the reproducibility error for statistical measurements, and its comprehensive coverage of Maximum Likelihood parameter estimation techniques. The book is useful for dealing with situations where there is a model relating to the input and output of a process, but with a random component, which could be noise in the system or the process itself could be random, like turbulence. Parameter estimation techniques are shown for many different types of statistical models, including joint Gaussian. The Cramer-Rao bounds are described as useful estimates of reproducibility errors.Finally, using an example with a random sampling of turbulent flows that can occur when using laser anemometry the book also explains the use of conditional probabilities.

Processing Random Data Statistics For Engineers And Scientists

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Author : Robert V Edwards
language : en
Publisher: World Scientific Publishing Company
Release Date : 2006-07-03

Processing Random Data Statistics For Engineers And Scientists written by Robert V Edwards and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-07-03 with Mathematics categories.

Processing Random Data

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Author : Robert Valentino Edwards
language : en
Publisher:
Release Date : 2006

Processing Random Data written by Robert Valentino Edwards and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

Random Data

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Author : Julius S. Bendat
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20

Random Data written by Julius S. Bendat and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Technology & Engineering categories.

RANDOM DATA A TIMELY UPDATE OF THE CLASSIC BOOK ON THE THEORY AND APPLICATION OF RANDOM DATA ANALYSIS First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-under-graduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data.

Random Field Modelling And Its Application In Stochastic Data Processing

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Author : George Christakos
language : en
Publisher:
Release Date : 1990

Random Field Modelling And Its Application In Stochastic Data Processing written by George Christakos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Random fields categories.

Granular Computing Based Machine Learning

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Author : Han Liu
language : en
Publisher: Springer
Release Date : 2017-11-04

Granular Computing Based Machine Learning written by Han Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-04 with Technology & Engineering categories.

This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.

Digital Processing Of Random Oscillations

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Author : Viacheslav Karmalita
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-06-17

Digital Processing Of Random Oscillations written by Viacheslav Karmalita and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-17 with Technology & Engineering categories.

This book deals with the autoregressive method for digital processing of random oscillations. The method is based on a one-to-one transformation of the numeric factors of the Yule series model to linear elastic system characteristics. This parametric approach allowed to develop a formal processing procedure from the experimental data to obtain estimates of logarithmic decrement and natural frequency of random oscillations. A straightforward mathematical description of the procedure makes it possible to optimize a discretization of oscillation realizations providing efficient estimates. The derived analytical expressions for confidence intervals of estimates enable a priori evaluation of their accuracy. Experimental validation of the method is also provided. Statistical applications for the analysis of mechanical systems arise from the fact that the loads experienced by machineries and various structures often cannot be described by deterministic vibration theory. Therefore, a sufficient description of real oscillatory processes (vibrations) calls for the use of random functions. In engineering practice, the linear vibration theory (modeling phenomena by common linear differential equations) is generally used. This theory’s fundamental concepts such as natural frequency, oscillation decrement, resonance, etc. are credited for its wide use in different technical tasks. In technical applications two types of research tasks exist: direct and inverse. The former allows to determine stochastic characteristics of the system output X(t) resulting from a random process E(t) when the object model is considered known. The direct task enables to evaluate the effect of an operational environment on the designed object and to predict its operation under various loads. The inverse task is aimed at evaluating the object model on known processes E(t) and X(t), i.e. finding model (equations) factors. This task is usually met at the tests of prototypes to identify (or verify) its model experimentally. To characterize random processes a notion of "shaping dynamic system" is commonly used. This concept allows to consider the observing process as the output of a hypothetical system with the input being stationary Gauss-distributed ("white") noise. Therefore, the process may be exhaustively described in terms of parameters of that system. In the case of random oscillations, the "shaping system" is an elastic system described by the common differential equation of the second order: X ̈(t)+2hX ̇(t)+ ω_0^2 X(t)=E(t), where ω0 = 2π/Т0 is the natural frequency, T0 is the oscillation period, and h is a damping factor. As a result, the process X(t) can be characterized in terms of the system parameters – natural frequency and logarithmic oscillations decrement δ = hT0 as well as the process variance. Evaluation of these parameters is subjected to experimental data processing based on frequency or time-domain representations of oscillations. It must be noted that a concept of these parameters evaluation did not change much during the last century. For instance, in case of the spectral density utilization, evaluation of the decrement values is linked with bandwidth measurements at the points of half-power of the observed oscillations. For a time-domain presentation, evaluation of the decrement requires measuring covariance values delayed by a time interval divisible by T0. Both estimation procedures are derived from a continuous description of research phenomena, so the accuracy of estimates is linked directly to the adequacy of discrete representation of random oscillations. This approach is similar a concept of transforming differential equations to difference ones with derivative approximation by corresponding finite differences. The resulting discrete model, being an approximation, features a methodical error which can be decreased but never eliminated. To render such a presentation more accurate it is imperative to decrease the discretization interval and to increase realization size growing requirements for computing power. The spectral density and covariance function estimates comprise a non-parametric (non-formal) approach. In principle, any non-formal approach is a kind of art i.e. the results depend on the performer’s skills. Due to interference of subjective factors in spectral or covariance estimates of random signals, accuracy of results cannot be properly determined or justified. To avoid the abovementioned difficulties, the application of linear time-series models with well-developed procedures for parameter estimates is more advantageous. A method for the analysis of random oscillations using a parametric model corresponding discretely (no approximation error) with a linear elastic system is developed and presented in this book. As a result, a one-to-one transformation of the model’s numerical factors to logarithmic decrement and natural frequency of random oscillations is established. It allowed to develop a formal processing procedure from experimental data to obtain the estimates of δ and ω0. The proposed approach allows researchers to replace traditional subjective techniques by a formal processing procedure providing efficient estimates with analytically defined statistical uncertainties.

Aerospace Instrumentation

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Author : M. A. Perry
language : en
Publisher: Elsevier
Release Date : 2015-05-18

Aerospace Instrumentation written by M. A. Perry and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-18 with Technology & Engineering categories.

Aerospace Instrumentation, Volume 4 is a collection of papers presented at the Fourth International Aerospace Instrumentation Symposium, held at the College of Aeronautics, Cranfield. Co-sponsored by the Instrument Society of America, the symposium covers most aspects of aerospace instrumentation. This book is composed of 14 chapters and begins with a description of strain gauge transducers, an introduction to noise, filtering, and random function, as well as the data analysis facility designed to satisfy the needs in the fields of fundamental research and major power plant design and commissioning. A chapter examines equipment for the analysis of random processes for low frequence purposes. Other chapters explore the measurement and analysis of rotor blade airloads, the application of digital computer to instrumentation systems, the features of an altitude test facility, and the trade-offs existing between analogue and digital filtering techniques. The last chapters are devoted to test methods for aircraft performance, stability, and control characteristics determination in non-steady flight. These chapters also treat the operational experience of the B-70 flight test data system. This book will prove useful to aerospace scientists, engineers and research workers.

Machine Learning For Signal Processing

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Author : Max A. Little
language : en
Publisher: Oxford University Press, USA
Release Date : 2019

Machine Learning For Signal Processing written by Max A. Little and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.

Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.

Introduction To Computer Data Processing

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Author : Outlet
language : en
Publisher:
Release Date : 1988-12-01

Introduction To Computer Data Processing written by Outlet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-12-01 with categories.

Processing Random Data

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