Anomaly detection for construction vibration signals using unsupervised deep learning and cloud computing

This publication has 38 references indexed in Scilit:

• Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data
  Advanced Engineering Informatics, 2015
• Blind denoising of structural vibration responses with outliers via principal component pursuit
  Structural Control and Health Monitoring, 2013
• Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
  Journal of Experimental Social Psychology, 2013
• False-Positive Psychology
  Psychological Science, 2011
• Anomaly detection in streaming environmental sensor data: A data-driven modeling approach
  Environmental Modelling & Software, 2010
• Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications
  Mechanical Systems and Signal Processing, 2009
• Anomaly detection
  ACM Computing Surveys, 2009
• Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
  Neural Networks, 2008
• Sensor validation using principal component analysis
  Smart Materials and Structures, 2004
• A new distribution-free quantile estimator
  Biometrika, 1982