Innovative Projects Realized

Chain Certs: Development of a platform for organizational authenticity certificates creation

Blockchain is a decentralized and immutable data structure. The information stored on blockchain is tamper-resistant, immutable and transparent. Blockchain is an interesting platform for managing digital certificates without a central authority. Because paper certificates can be easily faked or tampered with modern computer skills. Additionally, using a central authority for issuing distributing certificates is inefficient.

In this project, we will analyze the security and scalability of different approaches to certificate management solutions using blockchain. This analysis will provide guidelines for certificate management in permissioned blockchains. The guidelines will be developed through the construction of a reusable and configurable testbed for blockchain performance testing and analysis of the results for the new configurations that exist within the certificate management space.

Détermination des facteurs limitants de la performance du triathlète

L’objectif de ce projet sera de mettre en place un protocole d’évaluation précis pour les triathlètes. Cette évaluation déterminera les points forts et les points à améliorer dans chacune des trois disciplines, c’est-à-dire en natation, en cyclisme et en course à pied. De cette façon, nous pourrons fournir les données nécessaires aux entraîneurs et aux athlètes afin que ceux-ci puissent obtenir un portrait spécifique des capacités de chacun de ces athlètes. De cette façon, il sera possible de quantifier les charges d’entraînement pour chaque discipline de sorte que l’aspect à travailler par l’athlète soit bien déterminé. Donc, cette évaluation sera utile pour orienter la planification d’entraînement des athlètes, effectuer un suivi précis ainsi qu’optimiser la progression de ceux-ci tout en améliorant leurs performances.

Determining position, speed and stride length using machine learning with sensor fusion based on ultrawideband local positioning system technology

Sensors that track human movement are becoming more and more popular in all kinds of applications including healthcare, sport and general human movement. However, traditional sensors generally have problems tracking individuals indoors and they are not very accurate when measuring subtle movements. Using innovative technology, new wearable sensors have been developed to track human movement that have solved the problems associated with previous sensors. Further development of these new sensors is still required and that is the overall aim of this project.

The goal of this project is to develop software that accurately calculates the speed and stride rate of athletes who wear a small sensor when they walk or run. To do this, we will compare the information we receive from the sensors with data that we collect in a laboratory using a video-based motion capture system, which is highly accurate. We will also use some advanced Artificial Intelligence techniques to process the information to help us develop the software. The newly developed software will allow the partner company to market and sell a new system that is very accurate and can be used indoors, giving them a major advantage in the marketplace

Plant level implementation of a model for real time tracking of composition changes to steel, slag and inclusions during ladle processing

The Ladle Metallurgy Furnace is used for adjustment of chemical composition and temperature, and control of tiny particles called “inclusions”. Controlling inclusions is carried out by adding calcium to modify the solid alumina or magnesium aluminate inclusions to less harmful liquid inclusions.
During ladle process, reaction of top slag, steel and inclusions occur simultaneously. Therefore, establishing a model to describe ladle process is indeed a challenge. The author developed a model to predict the chemical composition changes in molten steel, slag, and evolution of inclusions in the ladle during Ca treatment. The result of calculations was found to agree well with industrial heat data. However, the model is not in a form that can be used as a real-time tool. The overall objective of the current project is to develop a version of the model that will be sufficiently fast to use for real-time processing. Secondary objective is to calibrate the model for the ladle used in the KOBM stream at ArcelorMittal Dofasco

Modification éco-responsable de mousse à base de cellulose

es dernières années, les objectifs imposés par la gouvernance environnementale internationale ont conduit au développement de matériaux plus durables. Au Canada, cela a permis l’essor de nouveaux produits technologiques issus de l’industrie agroforestière et en particulier la valorisation de la cellulose pour le développement de nouveaux matériaux ‘vert’ à faible impact environnemental. Parmi les différents exemples on peut citer l’utilisation de la nano-cellulose cristalline dans le renforcement mécanique des matériaux composites. Récemment, l’utilisation des suspensions de cellulose cristalline ont conduit à la synthèse de mousses nanoporeuses. Ces nouveaux substrats biodégradables possèdent une porosité nanométrique contrôlée ce qui permettrai leur utilisation dans la filtration sélective d’agent polluants. Aujourd’hui, leur emploi reste limité à cause de leur forte hygroscopie (forte sensibilité d’un substrat à l’eau et à l’humidité). TO BE CONT’D

Optimization of group equivariant convolutional networks

The explosion of popularity of deep learning owes a lot to the success of convolutional neural networks, widely used in diverse fields including computer vision and natural language processing. Recently, the group equivariant convolutional neural network (G-CNN) was introduced, where equivariance of symmetries inherent in the data set is built in the architecture of the networks. While the G-CNNs has proven to exploit inherent symmetries more effectively than traditional CNNs, their architectural design and implementation require a deeper understanding of the mathematical concept of symmetries. We propose to develop better mathematical tools suitable for deep learning on G-CNNs, with two main goals: (1) improving the optimization methods of G-CNNs by exploiting the geometry underlying the inherent symmetry of the data set; (2) generalizing the architecture of G-CNNs to adapt to other practical learning problems, such as speech recognition and image processing.

Soutenir l’adoption de comportements favorisant l’efficacité énergétique des familles québécoises par l’approche ludique

Au Québec, le gouvernement s’est donné pour mandat d’agir sur l’efficacité énergétique et l’économie d’énergie dans les ménages afin de réduire l’impact environnemental lié la production d’électricité. Pour concrétiser cette orientation politique, un changement de comportements des consommateurs d’énergie est nécessaire. Dans le cas de l’électricité, ce changement représente toutefois un défi important puisqu’elle est invisible et donc difficile à conceptualiser. Au cours des dernières années, des techniques de rétroaction visuelle ont été développées afin d’augmenter la visibilité de la consommation électrique, mais ces techniques ont un effet éphémère. Il semble alors nécessaire de développer et de tester de nouveaux dispositifs de rétroaction innovants conçus dans un souci d’engager l’utilisateur à long terme. Des études récentes démontrent que les approches ludiques ou la « gamification » constituent une avenue prometteuse suscitant la motivation et l’engagement à changer les comportements énergétiques. TO BE CONT’D

New Methods for Automated Assessment GPR in Potash Mining

This project attempts to improve on a Ground Penetrating Radar (GPR) based assessment system that is used to evaluate the thickness of the salt layer in the roof of Potash mining rooms to enhance mine safety. The goal is to improve the operation of the algorithm by studying the GPR signatures of known geological structures which can affect the operation of the algorithm and hence the evaluation of the roof thickness in order to adjust for these structures. This work will involved signal processing and analysis and will examine actual GPR data from the mines as well as simulated data from a GPR simulation software tool.

Machinery Health Monitoring using Multiple Sensor Fusion and Deep Learning

Manufacturing system reliability is significant in modern industry that requires high production speed, low maintenance cost, and enhanced operation safety. The proposed work is aiming to establish a systematic and applicable approach for condition monitoring of key machinery (e.g. bearings, gearboxes, motors) through multiple sensor fusion and deep learning. With the capability of working under high industry electromagnetic noise environment, optical-fiber-based sensors are incorporated to collect various signals of the machinery. Sensory information from various types of sensors will then be fused by sensor fusion algorithms. Deep learning, as an emerging technique successfully applied in many areas will be utilized to automatically extract the knowledge inside the massive sensory data. An intelligent machine fault diagnosis and prognosis system will be developed to achieve increased accuracy and reliability.

Nickel-catalyzed [3+2+1] synthesis of 2-pyridones

Catalysts speed up chemical reactions by lowering the energy required for a process to occur. In our daily lives, this phenomenon occurs very similarly to how catalytic converters in car exhausts work. By employing a catalyst, we can minimize waste and save energy, time, and money. However, to increase the performance of a catalyst, and to discover the most efficient process, one must know how the catalyst works. Building upon previous research, we envision a novel methodology in which minuscule quantities of nickel can be used to discover new ways of making useful nitrogen-containing molecules. We will investigate the efficiency of this process whilst trying to improve this new method. We hope that this new technology can be used to synthesize versatile building blocks as valuable pharmaceutical intermediates.

Efficacité du débit réservé à préserver le saumon atlantique juvénile et ses habitats

L’exploitation de la centrale hydroélectrique Romaine-1 cause sur la rivière Romaine des fluctuations du débit susceptible d’entraîner le déplacement des saumons juvéniles en dehors de leurs habitats préférentiels. Il est donc essentiel de déterminer si le mode de gestion actuel des débits permet aux saumons juvéniles de se maintenir dans leurs habitats.
Pour étudier la question, un système de télémétrie a été développé afin d’effectuer un suivi des déplacements des saumons juvéniles en fonction du régime de débit et des conditions environnementales. Ce système unique au monde est constitué d’un réseau de 305 antennes stationnaires enfouies dans le lit de la rivière et reliées à un module de contrôle en rive. La portée de détection des antennes favorise une excellente efficacité du suivi des déplacements des poissons marqués sur l’ensemble des 3 500 m2 d’habitat aménagé par Hydro-Québec pour documenter l’utilisation des habitats par les saumons juvéniles.

Efficient Motion in Circularly Arranged Sextuple Triptycene Molecular Gears

Molecular machines are compounds which can perform tasks or work given an external stimulus. One of the simplest tools available to scientists interested in nanoscale machines are molecular gears. Like their macroscale counterparts, molecular gears can turn and cause rotation in another part of the molecule in a controlled manner. While several molecular gears have been prepared to date, most are simple and only involve two or three gears parts in a system. Recently, the Shionoya group has prepared intricate molecular gears systems, involving four or six gears moving together. While these systems are promising for future development of more complicated molecular machines, they suffer due to inefficient rotation, causing slow rotation around the gears. We propose the preparation of a new molecular gear, based on the previous six-gear system developed by Shionoya, however with more sterically bulky groups to help cause more efficient rotation.