Speaker: Javier Tejedor Noguerales.
Abstract: This talk presents an advanced system for the continuous monitoring of potential threats in a long gas pipeline. For signal acquisition, phase-sensitive optical time domain reflectometry technology is employed. Then, pattern recognition strategies are incorporated, which are aimed at identifying threats. To do so, the system integrates a random forest-based approach on top of a multiple-layer perceptron (MLP)-based discriminative approach for feature extraction within a parallel Gaussian Mixture Model (GMM)-Hidden Markov Model (HMM) for pattern classification in a hybrid approach. Subsequently, a system combination strategy, which makes use of the decisions carried out by this hybrid approach, is also presented. This strategy is based on the so-called majority voting technique, which makes use of the output of the classification step from the different feature extraction strategies and the different number of states in the GMM-HMM-based classification. The system is tested on two tasks: (1) Identification of machine and activity, and (2) detection of threats for the pipeline. Results show 62% of accuracy in the identification of machine and activity, and 90% of threat detection rate with 33% false alarm rate in the threat detection task.