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Reliability and Statistical Computing Modeling, Methods and Applications
Title
Reliability and Statistical Computing [electronic resource] : Modeling, Methods and Applications / edited by Hoang Pham.
ISBN
9783030434120
Edition
1st ed. 2020.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2020.
Physical Description
1 online resource (XIII, 317 p.) 104 illus., 57 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This 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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 09, 2020
Series
Springer Series in Reliability Engineering,
Contents
Reliability 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.
Subjects
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