SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications

  title={SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications},
  author={Ashwini R. More and Kenny C. Gross},
  journal={2016 International Conference on Computational Science and Computational Intelligence (CSCI)},
Advanced statistical machine learning (ML) algorithms are being developed, trained, tuned, optimized, and validated for real-time prognostics for internet-of-things (IoT) applications in the fields of manufacturing, transportation, and utilities. For such applications, we have achieved greatest prognostic success with ML algorithms from a class of pattern recognition known as nonlinear, nonparametric regression. To intercompare candidate ML algorithmics to identify the "best" algorithms for IoT… CONTINUE READING


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Fault - Tolerance Improvement for a MSET Model of the Crystal River - 3 Feedwater Flow System

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New artificial intelligence technique detects instrument faults early

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Application of a Model-based Fault Detection System to Nuclear Plant Signals

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