Grant: Applied Research and Development Grants - Level 2

Mobile applications enable companies to place maintenance/repair service requests instantly from anywhere*and track their progress with real-time updates. However, automatic identification of equipment with an issue*in their facilities is a much more valuable piece of information for service efficiency. Such automatic*identification would allow the service provider to make the appropriate decisions for fixing these issues. It*would also save the facility managers' time because they would not need to search for the equipment*information.*The proposed research will focus on creating complex, innovative machine-learning models that can*automatically identify a specific piece of equipment in a variety of photographic settings with multiple*variables. This will enable a facility manager to seamlessly send and track service requests. The proposed app*and models will do the leg work so the facility manager can focus on what matters most - running their*restaurant and serving their customers. Furthermore, the proposed research builds on the strong relationship*established between GetResQ Ltd. and Sheridan College to continue the research and development work from*a successful NSERC Engage grant that was completed in May, 2018.*The benefits of this project to the partner include competitive advancement, as well as gaining insight and*knowledge on best practices in developing leading-edge machine-learning models. The collaborative research*team aims to be among the first to provide these kinds of rich experiences by exploring combining image*processing, mobile computing and machine learning to provide automatic equipment identification to*GetResQ's clients. The benefit to Sheridan is increased student and faculty engagement in applied research to*solve challenging real-world problems. The benefits to the research discipline include new knowledge and*expertise that will be disseminated and transferred to Canadian companies locally, provincially, and nationally.