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008			200328s2020 gw | s |||| 0|eng d
020			_a9783030434120
024	7		_a10.1007/978-3-030-43412-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76 _b2020 EB
245	0	0	_aReliability and Statistical Computing : _bModeling, Methods and Applications _cedited by Hoang Pham.
250			_aFirst edition 2020.
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XIII, 317 páginas) _b104 ilustraciones, 57 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aSpringer Series in Reliability Engineering _x1614-7839
490	0		_aEngineering (Springer-11647)
505	0		_aReliability Computing -- Modeling and Methods -- Predicted Reliability Modeling -- Mechanical Reliability Analysis -- Fatigue Distribution Functions -- Optimal Maintenance Models -- Maintenance Policies -- System Reliability with Simultaneous Failure on Consecutive Components -- Statistical Computing -- Modeling and Methods -- Wearable Sensor Data Based Human Activity Recognition Using Machine Learning -- Bootstrap Confidence Interval for Regression Coefficients -- Run Rules Control Charts for Coefficient of Variation with Measurement Errors -- Goodness-of-Fit Tests for the Component Lifetimes Distribution Based on the System Failure Data with Known Signature -- Methodology of Using Empirical Distributions to Solve Business Optimization Problems -- Deep Learning-based Scene Understanding Model for Assistive System Related to Alzheimer's Patients -- Applications and Case Studies -- Modelling the Performance of Capital Constrained Firms -- Integrating Sentiment Analysis in Recommender Systems -- Feature Matching Technique Using Similarity Features Filtering for Image Alignment -- Extended Sentence Similarity Based on Word Relations for Document Summarization -- Developing Alert Level for Aircraft Components -- Application of Machine Learning for Failure Prediction in Manufacturing Process.
520	3		_aThis book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in the field, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems. Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.
988			_aSpringer_Engineering_31032020
650		7	_2embne _aSistemas informáticos _9161392
650		7	_2embne _aEstadística matemática _9138936
700	1		_aPham, Hoang _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030434113
776	0	8	_iPrinted edition: _z9783030434137
776	0	8	_iPrinted edition: _z9783030434144
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-43412-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI