2021

Realtime simulators in virtual reality are a leading-edge technology to provide standardized training and evaluation that contribute to heavy-equipment operator safety. They aim to reduce accidents due to operator inexperience by training an operator in scenarios before they are expected to perform these same tasks in reality. To satisfy the high frame-rate requirements of real-time simulation, algorithms to solve these models must be extremely efficient and yield predictable and reproducible results.

The simulation of heavy equipment involves simulating both physical bodies in motion and hydraulic pressures. The goal of this project is to develop an approach to the simulation of fluid pressure volumes that can be interfaced with a position-based dynamics (PBD) simulation, improving performance and providing a more realistic experience.

McMillan-McGee have developed induction heating technology for in-situ soil remediation. As such, we have developed heater casings, work coils, and inverters which generate high frequency alternating current. We have also developed a great amount of the analytical work for engineering this equipment.

An important aspect of our inverter design (or for that matter any high frequency inverter in general) is to have a good understanding of the electrical properties (resistance and inductive reactance) of the DC bus bar that supplies current to the high speed switching devices (IGBTs and SiC Mosfets). Knowing this would allow us to design a suitable bus bar system that can absorb energy caused by switching transients from these semiconductor devices as a result of commuting current through the work coil. The problems in this project centre on solving certain boundary value problems involving Maxwell’s equations and linking these to the laws of thermodynamics.

Insect dispersal is often divided into two classes: local and long distance. Local dispersal is the most common dispersal mode and because many individuals disperse this way, it is well described by dispersal kernels. Long distance dispersal is more stochastic and difficult to model using dispersal kernels because only a small proportion of insects are thought to disperse long distances. For the mountain pine beetle for example, most individuals disperse between five and fifty meters from where they were born but about 0.2 percent of individuals end up above tree canopies where they can be pulled upwards by updrafts and then transported laterally by higher wind speeds in the lower atmosphere.

Long-distance dispersal is likely the dominant determinant of the speed of mountain pine beetle invasions, but estimation of invasion speeds using standard mathematical approaches are impeded by the strong Allee effect. In this project, we will explore theoretical dispersion distributions and models to better estimate mountain pine beetle invasion speed.

Existing techniques for insect mark and recapture studies can result in unnatural dispersal patterns. We have developed a new technique for marking insects as the emerge from which offers a much more natural dispersal pattern.

The technique involves coating trees with a special paper which glows under ultra-violet light. The insects are marked with paper dust as they chew through the paper to emerge. Following recapture, they can be examined for traces of the paper under ultra-violet light.

In this project we will work with images of insects marked using this technique and control images of unmarked insects. The problem is to conduct batch analysis of the images and classify them as marked and unmarked to validate the new technique.

Roads are a vital component of today’s society. They not only connect us to different cities, towns, provinces, and countries, but they also provide of a means of transporting goods and services. Typically, roads are constructed using materials such as concrete, or asphalt. Between the cold winter temperatures and hot summer temperatures, as well as settling earth, cracks and potholes are bound to occur on roads.

Alberta spent over 1.6 billion dollars in transportation related needs in 2020-21. Over 26% of this money went towards road construction and maintenance related expenditures. Roads undergo regular inspection; a detailed Surface Condition Rating occurs every two years in Alberta. This helps determine the priority in which maintenance should occur on roads in the province. One method of conducting these inspections could be done via aerial imagery. While an individual could go through these images to inspect the roads for cracks and potholes, is there a way that machine learning and computer vision could not only detect cracks but also classify their severity?