In order to accelerate the transition of our electricity system to renewable sources, it is important that buildings participate effectively as distributed generators. However, traditional integration methods for solar energy often add complexity to our electricity system. With the rapidly declining costs of battery systems, building-level microgrids are becoming a viable alternative allowing buildings to generate and use renewable energy locally rather than exporting to the grid, enhancing the resilience of energy supply and improving demand profiles.
The inspection process has been an inseparable part of manufacturing to measure dimensions such as diameter, flatness, roundness and straightness of the parts. Besides, on some machined surfaces, it is required to measure roughness and identify surface defects. For defect detection, companies are still relying on visual inspection, which is very slow and labor-intensive. To overcome all challenges, interferometry instruments are used to acquire 3D images. Still, once a surface is acquired, the position and size of defects have to be found, and sometimes defect has to be classified.
Lithium-sulfur (Li-S) batteries have been considered as one of the most promising candidates to meet the energy storage demand for electric vehicles due to their high theoretical energy density of 2600 Wh kg-1, low cost, natural abundance, environmental friendliness. State-of-the-art Li-S batteries, using liquid electrolytes, still have significant challenges in their safety and lifespan.
In-water measurement and sample collection solutions for environmental marine monitoring will be studied. Fine-scale responsive measurements cannot be achieved cost-effectively with satellites or aircraft. For near-surface monitoring, an unmanned aerial system (UAS) could achieve the necessary spatial-temporal sampling.
The proposed solutions deploy a payload sensor and/or sample grabber from an UAS with a winchable tether. However, the winch and tether can impact the UAS dynamics.
A geospatial query is a question where the concept of location is necessary for formulating the answer. Furthermore, we are not simply interested in spatial relationships, but also with the ways in which people can possibly move through space given the goals that they want to achieve. We therefore want to predict the behaviour of people moving through urban environments based on observations about their purchases. In this project, we will explore how can models of commonsense knowledge can be used for automated reasoning to answer geospatial queries and to infer consumer behaviour.
Due to the potential for significant cost-savings, many companies are turning their attention to digital simulations which produce an enormous amount of data. For companies to realize the benefits of having access to this data they need tools that allow efficient and accurate extraction of information from the data set. The goal of this project is to conduct the research required to develop a framework for the implementation of machine learning algorithms to provide engineering predictions for industrial applications.
The objective of the research project that is the subject of this proposal is to simulate autonomous flight for the purpose of risk-based safety assessment using machine learning applications. Simulation methodologies are developed and implemented for the purpose of extracting data from real-time, publicly-accessible sources to create a virtual environment that represents actual airspaces including air traffic, weather, terrain obstacles and navigation aids.
Manufacturing of aerospace composite structures requires drilling of thousands of holes for rivet and bolt attachments. Traditionally, required holes have been drilled and inspected manually, which is extremely time-consuming, inconsistent due to human error, and potentially hazardous to workers. The proposed research will develop a high performance robotic drilling and inspection technology for aerospace composite manufacturing. In order to improve hole quality in robotic drilling, a novel two-axis actuator will be developed to actively measure and suppress robot vibrations during operation.
This project is to purpose to use computer vision to identify the various error types during the operation of 3D printers to boost their throughput and enhance their application in the manufacturing industry. Nevertheless, due to the lack of precision and controllability inside the printers, engineers cannot achieve a reliable printing process and acceptable quality of final 3D printing products. In this research, a monitoring system equipped with computer vision is proposed to address this challenge.
Over the air (OTA) test is the standard procedure for wireless devices to verify the transceiver and antennas performance together in specified conditions. Any wireless device such as tablets, phones and laptop must go under OTA testing. Furthermore, regulatory organizations require OTA testing before the wireless device being certified.
Due to the time-consuming procedure of OTA testing, currently manufacturers and vendors limit themselves to a small sample of devices for testing.