Integration Einer Data Analysis Toolbox In Die Iot Suite

Integration einer Data Analysis Toolbox in die IOT Suite PDF
Author: Daniel Lomosits
Publisher:
Category :
Languages : de
Pages : 150
View: 2927

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Book Description:
Im Sinne der Firmenausrichtung zum Schlagwort Industrie 4.0 ist mit der SIEMENS SIMATIC WinCC OA IOT Suite [1] ein neues Produkt in diesem Segment entstanden, das auf der SCADA Software SIEMENS SIMATIC WinCC Open Architecture [2] basiert. Diese Masterarbeit verfolgt das Ziel, eine Data Analysis Toolbox basierend auf einer fundamentalen Recherche zu realisieren und in die IOT Suite [1] zu integrieren. Das Ergebnis dieser Recherche wurde anhand eines qualitativen Variantenvergleichs bewertet und evaluiert, sodass die technisch sinnvollste Lösung ermittelt wurde, ehe jene mit der höchsten technischen Wertigkeit implementiert wurde. Abschließend wurde die Toolbox mit einer grafischen Oberfläche versehen und in Form einer eigenständigen App in die IOT Suite [1] integriert, ehe anhand verschiedener Modelle technische Versuche und Simulationen zur Verifikation herangezogen wurden.*****Regarding to the company alignment converging to industry 4.0 a new product called SIEMENS SIMATIC WinCC OA IOT Suite [1] was released which is based on the SCADA software SIEMENS SIMATIC WinCC Open Architecture [2]. The aim of this master thesis is to achieve the integration of a data analysis toolbox based on a fundamental research into IOT Suite [1]. The result of this research has to be compared and evaluated. The concept with the highest technical valence has to be implemented afterwards. The developed toolbox has to be extended with a graphical user interface and integrated into IOT Suite [1] as a standalone app before technical attempts and simulations verifies the results.


Random Data

Random Data PDF
Author: Julius S. Bendat
Publisher: John Wiley & Sons
Category : Technology & Engineering
Languages : en
Pages : 640
View: 2134

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Book Description:
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-undergraduate 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.


Asteroseismic Data Analysis

Asteroseismic Data Analysis PDF
Author: Sarbani Basu
Publisher: Princeton University Press
Category : Science
Languages : en
Pages : 352
View: 3957

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Book Description:
Studies of stars and stellar populations, and the discovery and characterization of exoplanets, are being revolutionized by new satellite and telescope observations of unprecedented quality and scope. Some of the most significant advances have been in the field of asteroseismology, the study of stars by observation of their oscillations. Asteroseismic Data Analysis gives a comprehensive technical introduction to this discipline. This book not only helps students and researchers learn about asteroseismology; it also serves as an essential instruction manual for those entering the field. The book presents readers with the foundational techniques used in the analysis and interpretation of asteroseismic data on cool stars that show solar-like oscillations. The techniques have been refined, and in some cases developed, to analyze asteroseismic data collected by the NASA Kepler mission. Topics range from the analysis of time-series observations to extract seismic data for stars to the use of those data to determine global and internal properties of the stars. Reading lists and problem sets are provided, and data necessary for the problem sets are available online. The first book to describe in detail the different techniques used to analyze the data on stellar oscillations, Asteroseismic Data Analysis offers an invaluable window into the hearts of stars. Introduces the asteroseismic study of stars and the theory of stellar oscillations Describes the analysis of observational (time-domain) data Examines how seismic parameters are extracted from observations Explores how stellar properties are determined from seismic data Looks at the “inverse problem,” where frequencies are used to infer internal structures of stars


Data Analysis Machine Learning And Applications

Data Analysis  Machine Learning and Applications PDF
Author: Christine Preisach
Publisher: Springer Science & Business Media
Category : Computers
Languages : en
Pages : 719
View: 6955

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Book Description:
Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.


Functional Data Analysis

Functional Data Analysis PDF
Author: James Ramsay
Publisher: Springer Science & Business Media
Category : Mathematics
Languages : en
Pages : 311
View: 1041

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Book Description:
Included here are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meteorology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, applied data analysts, and to experienced researchers; and as such is of value both within statistics and across a broad spectrum of other fields. Much of the material appears here for the first time.


Data Analysis

Data Analysis PDF
Author: Devinderjit Sivia
Publisher: Oxford University Press
Category : Mathematics
Languages : en
Pages : 246
View: 560

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Book Description:
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design. The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.


Bayesian Data Analysis Third Edition

Bayesian Data Analysis  Third Edition PDF
Author: Andrew Gelman
Publisher: CRC Press
Category : Mathematics
Languages : en
Pages : 675
View: 7044

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Book Description:
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.