Innovative Projects Realized

A Unified Framework for Remote Monitoring the State and Performance of Photovoltaic Power Plants

Energy produced using the solar radiation as the source is one of the most prominent parts of the clean energy mix. More than 300 GW of photovoltaic systems (PV systems) of different size supply the daily needs of millions of families and industries around the world. Photovoltaic panels are installed on the roof of houses and buildings, or they constitute large-scale photovoltaic power stations. Monitoring the energy production of the PV systems has a crucial role in both predictability and maintenance. To effectively monitoring the systems, the measures of the energy produced need to be remotely read and compared with the estimate production values that are calculated considering the solar radiation. With these data, many views and indexes related to the operating status of the plants can be given. Also, suggestions on how better use the self-produced energy can be supplied. TO BE CONT’D

Effects of Water Saturation and Foamy Oil Stability Agents on Foamy Oil Flow and the Cyclic Solvent Injection Process

This project is to perform systematic studies to better understand effects of water saturation and foamy oil stability agents on foamy oil flow and the cyclic solvent injection process and provide fundamental parameters for field-scaled prediction. Equilibrium and non-equilibrium PVT tests will be conducted to monitor and analyze the foamy oil performance under effects of the water saturation through a live-oil brine system with water emulsification. Also, pressure depletion tests will be performed to investigate the foamy oil performance under effects of the water saturation in porous medium. Well designed constant concentration expansion (CCE) tests will be carried out to examine effects of the foamy oil stabilizer and select an optimal one. Numerical simulation models will also be built to perform history matching and predicting study. Then scaling criteria from laboratory tests to field applications will be established based on experimental results, numerical simulation models and field data.

Accelerate Transaction Latency of Pool Mining in Cryptocurrency Networks

In this project, using such mainstream cryptocurrencies as BitCoin and Ethereum as representatives, the intern will analyze the transaction collection strategies of their mining pools, and then collect transactions and the corresponding blocks data to build a large dataset, from which the computing power of different mining pools and their proportions will be analyzed, together with the transaction latencies of pool mining. We will also identify potential enhancement through the analysis and measurement, particularly on energy and delay optimization. Coinchain is a BC-based startup company focusing on advanced cryptocurrency and blockchain technologies, and their application in industrial and commercial scenarios. It delivers global enterprise-level blockchain solutions to leading companies worldwide, and provides one-stop customized services such as product and information platforms, as well as smart contracts and trading platforms. TO BE CONT’D

Learning PDF Document Structures using Recursive Neural Networks

Portable Document Format or PDF is the de facto standard for presenting textual-visual content. In this project, we aim to develop a machine learning framework for PDF document understanding. Despite the recent proliferation of deep learning-based methods for the analysis and processing of natural images, there have been considerably less efforts on designing similar approaches for highly structured data such as documents. Our project will explore two novel ideas. First, we will develop a structured and organizational representation of PDF documents which is built on labeled content blocks (e.g., heading, figure, list, caption, etc.). Second, we will investigate how recursive neural networks (RvNN), one type of deep neural networks that have been utilized to language parsing, can be adopted and formulated for learning PDF document structures.

Energy Simulation and Lifecycle Costing of Advanced Glazing Systems

Highly efficient glazing, such as those offered by ECO-Insulating Glass, provide a significant

opportunity to reduce the energy consumption of buildings. This project quantifies the energy

savings, cost savings and reduction in heating and cooling demand resulting from using ECO

glazing in five major Canadian cities for a typical medium sized office building, a home and a

school. Four window options are evaluated and compared to windows, which just meet the

building code requirements. The effect of using higher insulation levels in conjunction with

ECO glazing will also be evaluated. The optimal scenarios will be selected from a total of 225

possible scenarios, using an advance building simulation software program from the U.S.

Department of Energy. The resulting values and trends will be useful for ECO glazing sales,

market analysis and for the shift towards higher performance buildings in Canada.

Structural Inequality, Resistance, & the Future of a Continent: Examining Colonialism’s Toxic Legacies

Last year, the world’s leading health researchers found toxic pollution to be the greatest cause of disease and premature death in the world today. However, while the findings acknowledge that deaths caused by toxins to be most prevalent among marginalized groups, there is no mention of socio-historical structures in producing these toxic patterns. Through this research I examine the relationship between structural violence and toxic geographies in relation to legacies of colonialism across North America and will conduct a comparative case study that looks at the Mount Polley Mine Disaster (MPMD) and the Flint Water Crisis (FWC) to think about solutions for addressing toxicity, anew. TO BE CONT’D

Quantitative micro-computed tomography for cartilage and joint mechanobiological measurement

This research aims to measure complex cartilage and joint mechanobiology, the dynamic interaction between cells and the physical factors, such as force, in their environment. This will be done by combining quantitative micro-computed tomography (microCT), image guided mechanical evaluation, and quantitative morphometric analysis. The aim is to combine these approaches into a single platform to provide longitudinal, quantitative, and in vivo measurement. Combining bone and cartilage imaging into a single modality is important since the two are tightly inter-regulated and communicate and respond to their loading environment among each other. To accomplish this, initial steps are to establish longitudinal contrast-enhanced CT protocols for use in time-lapse microCT imaging and integrate image-guided mechanical evaluation. This work will provide comprehensive all-in-one measurement of joint and cartilage mechanobiology. TO BE CONT’D

A Deep Learning Approach to Soft Sensor Design and Process Optimization for an Industrial Nickel Extraction Process

The objective of this project is to use artificial intelligence (AI) approaches to solve complex industrial problems. The two biggest advantages of AI-based approaches are the ability to continuously learn and also learn adequately from historical data. Traditionally, many process information are unmeasurable during live operations because of instrumentation limitations. Also, plants are not sufficiently optimized to maximize production quality, while minimizing waste. Using AI-based approaches, we can develop complex non-linear models from historical to predict the unmeasurable process information. The models are also continuously learning from the new data coming into the plant. To optimize the process operations, another family of AI algorithms called reinforcement learning will be used. These algorithms will learn the whole process, including what happens when each process variable is changed. With this knowledge, reinforcement learning can then provide the optimal sets of inputs to maximize the plant productivity, while minimizing its waste.

The control tower of the future

In our vision, each human operator participating to an emergency response mission should equipped with a portable mobile command center that collects, elaborates and displays the meaningful information generated within the area of operations. In such scenario, the availability of a reliable network able to offer the required performance and reliability to an heterogeneous set of devices (from smartphones to drones, from smart bracelets or any other sensor to any kind of vehicle, etc) represents the key to run a distributed decision-making architecture. The objective of the project is to build over the ad-hoc networking technology developed by Humanitas Solutions – the Heterogeneous Embedded Ad-hoc Virtual Emergency Network (HEAVEN) – to develop a new network infrastructure capable of supporting the novel multi-command center architecture for distributed control and improved situational awareness in emergency response operations

Pavement Distress Detection Using Conventional Unmanned Autonomous Vehicle LiDAR

In Montreal, pavement distresses are causing serious problem to the road network with more than half of the road considered in a bad and a very bad shape. Many pavement inspection methods are developed in order to inspect, detect, locate, and classify pavement distresses; however, these methods are not efficient in term of time, cost, and accuracy. In our project, we aim to develop a new approach in detecting, classifying, and locating pavement distresses using conventional unmanned autonomous vehicle LiDAR. This approach will create a new platform involving large number of vehicles equipped with LiDAR in detecting pavement distresses with no extra cost, less time, and more detection accuracy than the traditional methods.

Effects of Chemotherapy and Immune Cells on Ribosomal RNA Degradation (RNA disruption) in Tumour Cells

The business partner in this application (Rna Diagnostics, Inc.) has developed a diagnostic tool to determine whether the tumour(s) of a cancer patient undergoing chemotherapy before surgery is responding (dying) in response to treatment. This tool, called the Rna Disruption Assay (RDA), can help tailor chemotherapy, such that chemotherapy treatment is discontinued (along with its negative side effects) in patients with non-responding tumours. These patients can then move more quickly to other potentially more beneficial treatment. A new class of anti-cancer drugs can stimulate the body’s immune cells to kill tumours. They are called immunomodulators. The partner would like to determine if RDA can detect and quantify tumour cell death by the body’s immune cells, with or without chemotherapy agents. If so, then RDA may also be useful for patients prescribed immunomodulators and/or chemotherapy agents. This would significantly increase the value of RDA services provided by Rna Diagnostics, Inc.

Développement d’outils pour améliorer la fabrication de nanocomposites à base de thermoplastiques et de filaments de cellulose

Les filaments de cellulose peuvent facilement être extraits de produits naturels. Leur addition à des polymères peut entrainer une amélioration des propriétés mécaniques du matériau. Cependant, ces propriétés mécaniques intéressantes dépendent de l’état de dispersion des CF dans la matrice polymère et d’une adhésion interfaciale suffisante entre les CF et le polymère. Pour la plupart des thermoplastiques, obtenir une bonne dispersion reste un challenge et une combinaison précise d’additifs doit être choisie. En 2013, FP Innovation et Kruger ont formé une alliance pour créer une usine de production de CF et pour développer des nouvelles applications pour ces CF. Le succès de leur technologie leur a permis d’atteindre une production de 6000 T/an en 2015. Ils ont récemment conduit une étude de marché qui a montré que les nanocomposites de polypropylène (PP) et polyamide (PA) renforcés par les CF serait intéressant pour des applications automobiles. TO BE CONT’D