Microgrid’s Performance Modeling & Optimization Method Based on Data Mining & Artificial Intelligence

With the maturity of renewable energy technology in recent years, micro-network has become an ideal power supply solution to the remote villages and islands. Recently, researchers have tried to reduce the cost of the system based on ideal assumptions. However, the factors that actually affect the system life cycle cost are varied. Including the control of the system, the maintenance mode of the system, the geographical factors of the power station and the configuration of the system will greatly affect the cycle cost of the whole system.

Development and validation of analysis tools and interfaces for automated rehabilitation systems

Automated physiotherapy motion tracking system may improve clinical outcomes by providing subjective measures and continuous monitoring. A study into different metrics that PTs may find useful for diagnosis and a user interface study assessing the current usability of the Automated Rehabilitation System, a system being developed by Cardon rehabilitation, will be conducted. A method to model the central nervous system using controls will be investigated to see if fatigue can be detected, which is a useful metric to provide both patients and physiotherapists.

Applied Machine Learning for Malware and Network Intrusion Detection

Wedge Networks is a leading cybersecurity solution provider in Canada. In this project, we aim to investigate the application of statistical machine learning and deep learning to cyber threat detection, aiming to detect both network intrusions and malware binaries transmitted in the network.

Enhancing Lateness Management in Cross-docking

Today's marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively. This has led to the rise of cross-docking in the global supply chain to help keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materials from an incoming vehicle (e.g., train car, truck, vessel container) and then loading them directly onto outbound vehicles with no storage in between.

Enhancing Lateness Management in Cross-docking

Today's marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively. This has led to the rise of cross-docking in the global supply chain to help keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materials from an incoming vehicle (e.g., train car, truck, vessel container) and then loading them directly onto outbound vehicles with no storage in between.

Prototype Behavior Based Integrity Verification (BBIV)

Web computing, in which the world-wide web is itself employed as a distributed computing platform, is entering a stage of rapid expansion with the advent of Open Web Platform so that programs that once worked only a native environment on desktop, tablets or phones can now work from within a browser itself. There is therefore a need for a new form of protection for apps.

Automatic Casting from Videos Using Deep Convolutional Neural Networks

In automatic casting applications, the aim is to accurately recognise facial regions that correspond to a same actor appearing in a movie to produce described video. In particular, this project will focus on challenging tasks of capturing and modeling the facial trajectory for each person appearing in a movie in order to predict when/where the principal actors appear. This is a challenging task because recent movies are typically high quality and faces are often occluded and their appearance varies significantly according to pose, illumination, blur, etc.

3D Heat-Map Development based on Fault Diagnosis Data

This work focuses on generation a framework to employ a set of 3D coordinates, as the input dataset to the model, and generate the 3D heat map based on the 3D shape. The generated 3D heatmap aims to define the most probable areas for fault categories on the 3D surface. To develop such a system, the 3D shape is printed and the 3D coordinates of simulated faults are recorded using a tool tracker. Then, a machine learning platform is employed to use the 3D fault datasets as the input and produce the probabilities of different fault categories on the given location.

High power all-fiber Raman laser at 1.65 ?m

Fiber lasers have become the fastest-growing laser with a projected worldwide revenue up to $1.41 billion in 2017. In particular, fiber lasers at 1.65 ?m have drawn increasing attention with potential applications in chemical sensing, LIDAR and spectroscopy. All-fiber Raman lasing technology is a promising and efficient technology to achieve high power lasing at 1.65 ?m. However, there are limited all-fiber high power sources at 1.65 ?m that are commercially available.

Simulation and Development of a new Drive system and its Control Method for Permanent Magnet Synchronous Motor (PMSM)

The proposed research project is to develop and implement a motor drive based on new semiconductor technologies such SiCMOS and GaN. This motor drive will be used in eVox bikes developed and commercialized by Procycle. The objective is to increasing the operating voltage in the system from 100V to 250V. This high voltage device will improve substantially the efficiency of the system by decreasing the losses in the electrical machine used in the bike. This improved performance will help the company to stay in head of this very competitive market.

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