Industrial processes are continuously evolving to reduce environmental pollution, save energy, and remain economically competitive. In order to achieve these goals and optimize industrial processes such as combustion in cars and airplanes, or drilling in downhole oil extraction wells, the processes parameters need to be accurately monitored and effectively analyzed. Sensors are able to transduce physical signals into meaningful electronic signals for processing and decision-making purposes.
This research project will develop a state-of-the-art web-based Decision Support System (DSS) for operational forecasting and visualization of flood extents (via interactive maps and plots) in watersheds in Ontario and across Canada. The proposed DSS, called ISWMS-Smart, is an extension of the ISWMSTM system, developed by the GREENLAND Group (Industry Partner). ISWMS-Smart will efficiently generate accurate short-term flood forecasts by i) using Environment Canada’s open weather forecast data as input, and ii) feeding this weather data into sophisticated hydrologic and hydraulic models.
Knowledge graphs store facts using relations between pairs of entities. In this work, we address the question of link prediction in knowledge graphs. Our general approach broadly follows neighborhood aggregation schemes such as that of Graph Convolutional Networks (GCN), which in turn was motivated by spectral graph convolutions. Our proposed model will aggregate information from neighbour entities and relations. Contrary to most existing knowledge graph completion methods, our model is expected to work in the inductive setting: Predicting relations for entities not seen during training.
Quality of drinking water is important to our health and well-being. Water quality monitoring outside the laboratory aims to obtain qualitative information on the physical, chemical, and biological characteristics of a water body. The traditional method of taking samples periodically is not only a cost intensive process, but one that takes snap shots only, with big unobserved periods in between.
Transmission pipelines are the most popular and widely used medium to transport hydrocarbons (e.g., oil and gas) over long distances. Pipelines might pass through various geological and topographic conditions and therefore, pipeline routing is a critical component for successful design and regulatory approval. Due to the environmental and safety concerns or constraints imposed by the land use, pipeline routing often requires designers to allow for crossing adverse ground, e.g. steep slopes, valleys and faults.
Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), two main phytocannabinoids from cannabis, have demonstrated biological effects and potential therapeutic uses. However, their functionality when consumed orally could be limited due to poor water solubility and low utilization rate in human body. To make the cannabinoids more soluble, liquid formulations are being developed using food-grade oils and/or phytoglycogen, a natural compound from non-GMO sweet corn.
The main purpose of this project is to develop an enhanced portable ground Control Station (GCS) equipped with an Advanced Stand-Alone Virtual Reality Head Mounted Display (ASVR-HMD). In the beginning, a Commercial-off-the-shelf (COTS) stand-alone Virtual Reality (VR) headset would be connected to the flight simulation tool. The VR HMD will be used to visualize basic flight simulation. Then, an onboard camera would be integrated to the aircraft provided by the partner organization.
In the last decade optimization is expanded in many applications from food production to sophisticated applications such as engine fuel efficiency. In the proposed package, it is tried to apply optimization techniques along with physics based analytical and semi-analytical methodologies to create a compelling framework which can help thermal-process based oil industry to reduce their GHG and also better evaluate their CAPEX. Many SAGD projects are overspent on their facilities due to under prediction or overprediction of their oil production expectations.
Algae-C is a company specialized in algal biomass production. It enables on-site production of algae for a wide variety of sectors (aquaculture, nutraceutical, cosmetic and biofuel). To date, many fuels, pharmaceuticals and cosmetic products are extracted from plants. These valuable plant natural products (PNPs) are often produced in low quantities in plants and extraction methods can be long and expensive. Thus, there is much interest in metabolic engineering for developing microbial platforms to produce specific PNPs.
Autonomous cars are one example of a compelling next-generation artificial intelligence technology. In order to safely navigate through the world, cars must plan long-range routes and short-range paths, perceive the world around them, and act according to a safety-first policy that takes into account the intent of agents in their surrounding world. While not strictly AI-complete, the challenge of autonomous driving in urban and unstructured environments is substantial, as-yet unsolved, and of paramount economic importance.