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Machine Learning

Cognitive computation for Deviation detection in Fleet of City Buses

on Wed, 12/09/2015 - 16:21

With Prof. Thorsteinn Rögnvaldsson, from Halmstad University, Sweden, we are looking at how Reservoir Computing can help in deviation detection in a fleet of Swedish city buses using a signal from the air tank pressure from the buses in order to predict when a bus is going to break well in advance.

Video from the project at Halmstad University:




Proxy dynamical models of offshore oil production platforms via recurrent neural networks

on Wed, 12/09/2015 - 14:52

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement.

Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principled model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESNs) as an efficient technique for training Recurrent Neural Networks.

We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that for some periods the latter architecture performs better. 




Delayed Response Tasks in Robot Control

on Wed, 12/09/2015 - 14:38

In this work we tackle the road sign problem with Reservoir Computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which the delay period between the sign received and the required response (e.g., turn right or left) is a crucial factor. Delayed response tasks like this one form a temporal problem that can be handled very well by RC networks. Reservoir Computing is a biologically plausible technique which overcomes the problems of previous algorithms such as Backpropagation Through Time - which exhibits slow (or non-) convergence on training. RC is a new concept that includes a fast and efficient training algorithm. We show that this simple approach can solve the T-maze task efficiently.


Video showing trained RC network controlling the robot:



  1. Eric AntoneloBenjamin Schrauwen and Dirk Stroobandt Mobile Robot Control in the Road Sign Problem using Reservoir Computing Networks Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 911-916 (2008)    
  2. Eric AntoneloBenjamin Schrauwen and Jan Van Campenhout Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing Neural Processing Letters, Vol. 26(3), pp. 233-249 (2007)   

Technologies used: Matlab and toolboxes. 
Developed in 2005.

I worked with Prof. Thorsteinn Rognvaldsson from Halmstad University on a consulting project for Eka Chemicals from Sweden.

It involved the multi-variate data mining with several variables representing measurements from the chemical process. The main goal was to find out about the important variables influencing negatively the process.

For that, machine learning tools such as logistic regression, multi-layer perceptrons, and support vector machines were used (in Matlab).