Publications

Reeves, Paul C.

Abstract not provided.

Lorber, Alfred L.; Aragon, Kathryn M.; Prescott, Judy A.; Brooks, Jeffrey A.; Stout, Colt W.; Hughes, Bryan H.; Hughes, Bryan H.; Vigil, Dena V.; Walters, Tristan W.; King, Leisa K.; Reeves, Paul C.; Barnes, Scott M.; Arpin, Bettina K.

Abstract not provided.

Reeves, Paul C.; Ballard, Sanford B.; Hipp, James R.; Young, Christopher J.; Myers, Daniel S.; Ammons, Breanna A.

Abstract not provided.

Reeves, Paul C.; Farkas, Benjamin D.; Jones, John J.

This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

Mckenna, Sean A.; Reeves, Paul C.; Mckenna, Sean A.

This report describes the methodology and results of a project to develop a neural network for the prediction of the measured hydraulic conductivity or transmissivity in a series of boreholes at the Tono, Japan study site. Geophysical measurements were used as the input to EL feed-forward neural network. A simple genetic algorithm was used to evolve the architecture and parameters of the neural network in conjunction with an optimal subset of geophysical measurements for the prediction of hydraulic conductivity. The first attempt was focused on the estimation of the class of the hydraulic conductivity, high, medium or low, from the geophysical logs. This estimation was done while using the genetic algorithm to simultaneously determine which geophysical logs were the most important and optimizing the architecture of the neural network. Initial results showed that certain geophysical logs provided more information than others- most notably the 'short-normal', micro-resistivity, porosity and sonic logs provided the most information on hydraulic conductivity. The neural network produced excellent training results with accuracy of 90 percent or greater, but was unable to produce accurate predictions of the hydraulic conductivity class. The second attempt at prediction was done using a new methodology and a modified data set. The new methodology builds on the results of the first attempts at prediction by limiting the choices of geophysical logs to only those that provide significant information. Additionally, this second attempt uses a modified data set and predicts transmissivity instead of hydraulic conductivity. Results of these simulations indicate that the most informative geophysical measurements for the prediction of transmissivity are depth and sonic log. The long normal resistivity and self potential borehole logs are moderately informative. In addition, it was found that porosity and crack counts (clear, open, or hairline) do not inform predictions of hydraulic transmissivity.

Martinez, Mario J.; Hopkins, Polly L.; Reeves, Paul C.

This report describes the use of PorSalsa, a parallel-processing, finite-element-based, unstructured-grid code for the simulation of subsurface nonisothermal two-phase, two component flow through heterogeneous porous materials. PorSalsa can also model the advective-dispersive transport of any number of species. General source term and transport coefficient implementation greatly expands possible applications. Spatially heterogeneous flow and transport data are accommodated via a flexible interface. Discretization methods include both Galerkin and control volume finite element methods, with various options for weighting of nonlinear coefficients. Time integration includes both first and second-order predictor/corrector methods with automatic time step selection. Parallel processing is accomplished by domain decomposition and message passing, using MPI, enabling seamless execution on single computers, networked clusters, and massively parallel computers.