Ronald M. Lesperance

The Gaussian derivative model for spatial-temporal vision: I. Cortical model

Receptive fields of simple cells in the primate visual cortex were well fit in the space and time domains by the Gaussian Derivative (GD) model for spatio-temporal vision. All 23 fields in the data sample could be fit by one equation. varying only a single shape number and nine geometric transformation parameters. A difference-of-offset-Gaussians (DOOG) mechanism for the GD model also fit the data well. Other models tested did not fit the data as well as or as succinctly, or failed to converge on a unique solution, indicating over-parameterization. An efficient computational algorithm was found for the GD model which produced robust estimates of the direction and speed of moving objects in real scenes.

The General-Motors Variation-Reduction Adviser

TheGeneral Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in eighteen General Motors asssembly centers. This article reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domainspecific ontologies.

Digital Panel Assembly for Automotive Body-in-White

This paper presents the methodology of Digital Panel Assembly (DPA), a computer aided approach in automotive body panel assembly. Core to the methodology is special-purpose finite element software, EAVS, that can simulate the panel assembly processes and predict assembly dimensions by taking into considerations of the compliant nature of panels and sub-assemblies. To validate the methodology, a non-contact EOIS optical scanning procedure for panel measurement is established. The validation study shows that the reported methodology can accurately predict the 1st level panel sub-assemblies. Finally, the resource requirements, functional capabilities, and scalability of the digital panel assembly methodology towards a complete Body-in-White implementation are discussed.Copyright

Partial discharge detection in medium voltage stators using an antenna

Partial Discharges, if initiated give rise to progressive deterioration of insulation material and lowers its life expectancy. If allowed to persist further, PD can even lead to the electric breakdown of insulation. However, in spite of its damaging effects on machine insulation, its application during the production phase of machines, to avoid their permanent failure has gained importance in this research field. This paper discusses a Partial Discharge detection technique developed for medium voltage stator insulation utilizing an antenna to sense the discharge currents and record PD events produced inside a stator insulation. To reduce the effect of noise on detections process, some grounding techniques incorporated and later Undecimated Discrete Wavelet Transform was used to extract the discharge events from antenna output. To extract the best out of the technique, outputs were analyzed on a repetitive basis to make a confident decision on the presence of PD. This technique of detecting PD shows high sensitivity, as the detections are independent of the impedance of test setup used for experiments.