Reliability and Statistical Computing: Modeling, Methods and Applications - Couverture souple
Livre 67 sur 90: Springer Series in Reliability Engineering
Synopsis
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.
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