About Keith

Keith Brawner currently works in the simulation industry for the DoD, before, during, and after getting a Masters in Intelligent Systems. Sadly, he is not yet a Doctor.

Saturday, November 6, 2010

Neural Network Applications in Ship Research with Emphasis on the Identification of Roll Damping Coefficient of a Ship

Neural Network Applications in Ship Research with Emphasis on the Identification of Roll Damping Coefficient of a Ship was written out in Glasgow, Scotland's Department of Naval Architecture and Marine Engineering as a postdoctoral research paper by Mukhtiar Ali Unar.  It can be found for free online here.

Still having a good time.  Still trying to get a job with the Army.  Also, my team sucks at trivia (we came in dead last, but regularly take 2nd or 3rd in the Thursday night games.
This paper is mainly a survey paper, but in brief, the main problem was to predict hydrodynamic coefficients for ship vessels.  The hydrodynamic coefficients are used to determine exactly how to execute the appropriate maneuver.  Due to a large number of factors, these cannot be directly measured and have thus far yielded to the 'best guess' of the people who are driving the boat.  In theory, if we were able to model the parameters better, we would be able to build safer and more reliable ships. 

Artificial Neural Networks (ANNs or NNs) are a computational construction of a function that has many inputs and outputs.  It is a way to represent a problem as a 'black box', where the internals are unknown, but the inputs and outputs known.  The output of a neuron is the sum of the inputs to it, multiplied by their corresponding weights.  These weights can be 'trained' (adjusted in small increments) until they correctly model a given function.  It is represented to the right.

With that said, it is proven that any linearly separable function can be modeled by a Multi-Layer Perceptron (MLP) Neural Network (below).  It is represented below.


NNs have been in use (in research) for naval application since 1998 or so, with over 100 papers published using NNs in 2005.  They are a reasonably standard way of representing an unknown problem.

Naval Uses
  • Ship design (length, breadth, speed, draft, depth, displacement) - Clausen (2001)
  • Stability parameters (shipping vessels) - Alkan (2004)
  • Hull weight estimation - Wu (1999)
  • Estimation of wave induced ship hull bending - Xu/Haddarra (2001)
  • Automatic hull form generation - Islam (2001)
  • Control (steering, rudder roll, fin stabilization, collision avoidance, path following) - Various
  • Classification (filtering radar picture, marine acoustic signals, wake detection, ship trail clouds) - Various
  • Prediction of waves, tidal levels, storm surges, coastal water levels, and ocean currents - Various (1994-2007)

So the goal here was to estimate ship parameters.  For this, a ship controller was constructed and fed data from a centralized database.  It was the job of the NN to stabilize the ship.  The NN error was charted, but not in a meaningful way (sorry Dr. Unar), as it seems like this paper was written for funding, rather than results.

Why do you care? 
Let's be honest: you probably don't.  The important takeaway here is that NNs are being used well outside of the traditional domain of artificial intelligence.  Although developed as an application of Machine Learning methods, they have now made their way into fairly mainstream applications.  Just remember that when you are looking at what a predicted storm surge for an area is, or how your semi-automated ship is turning, you are seeing the fallout of AI.