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Ryerson University

Country: Canada

Ryerson University

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11 Projects, page 1 of 3
  • Funder: UKRI Project Code: NE/X006557/1
    Funder Contribution: 9,075 GBP

    Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle. The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis. The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.

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  • Funder: UKRI Project Code: NE/X006433/1
    Funder Contribution: 11,289 GBP

    EPSRC : Charles Gillott : EP/R513313/1 In an attempt to limit global temperature increase to 1.5 degrees C, the UK is committed to reducing greenhouse gas emissions to net-zero by 2050. The built environment is currently responsible for over 40% of the UK's greenhouse gas emissions, and is its largest contributor of waste. An increasing proportion of built emissions come from embodied carbon, referring to emissions from the extraction and manufacture of materials; construction, maintenance and demolition of buildings; and the processing and disposal of waste. A circular economy (CE) attempts to reduce resource consumption and waste generation - and thus embodied carbon - by retaining materials at their highest level of usefulness for as long as possible. Increasingly, policy promoting a CE is being seen across the globe, with strategies to achieving this including the retention of existing buildings, reuse of components, and recycling of materials. This project will identify policies that are promotive of CE in the Canadian and UK built environments, including those that apply at the national (e.g. building regulations in the UK and national construction codes in Canada) and sub-national level (e.g. local authority or provincial planning requirements). How policies promote different CE strategies will be assessed, allowing the homogeneity and characteristics of the policy landscape to be compared within and across the two countries. Building upon this, the success of different policy instruments in influencing construction practice will be considered. This will result in cross contextual learnings, and the formation of recommendations to increase adoption of a CE in the Canadian and UK built environments.

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  • Funder: UKRI Project Code: EP/H010262/1
    Funder Contribution: 297,055 GBP

    This project studies development of high power DC transmission networks. There is currently significant interest in developing technologies that will enable interconnection of distributed DC sources to DC networks in multi MW power sizes. The application fields include offshore renewable power parks, North Sea Supergrid, subsea power supplies in oil industry and many more. A medium power DC network test rig will be developed at Aberdeen University which will include DC transformers and fault isolation components. The project will investigate efficient, light-weight DC transformer topologies that will enable cost-effective power exchange between DC systems at wide varying voltage levels. The DC test rig will enable practical testing of DC circuit breaker which will be one of the crucial enabling technologies for DC networks. The project further investigates the operational and control principles of future large DC power networks. This project strengthens collaborative links between University of Aberdeen and Ryerson University LEDAR laboratory.

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  • Funder: UKRI Project Code: NE/V020803/1
    Funder Contribution: 8,491 GBP

    AHRC : Katherine Hall : AH/R01275X/1 This project will make use of unique research knowledge and expertise (Gammel) and specialist archive collections within the Modern Literature and Culture Research Centre at Ryerson University, including life writing materials such as autobiographical accounts, diaries and letters of figures, in particular Gertrude Stein and Florine Stettheimer. It aims to develop a queer/feminist methodology, through a lens of defamiliarization (Palmer) at the convergence of creative and critical scholarship (Braidotti), to analyse relevant primary texts, from the MLC archive, for moments of knowing about friendships between women within the confessional telling. The method(s) will then be extended to published confessional material of modernist figures including Virginia Woolf, Vita Sackville West, Violet Trefusis, Alice B. Toklas and Colette. The anticipated co-authored outputs are: a conference paper; a research paper (suitable for submission to a leading feminist publication); an account of the methodology; an open source reading list; a creative response to the material and a workshop.

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  • Funder: UKRI Project Code: NE/V019368/1
    Funder Contribution: 7,504 GBP

    ESRC : John Shayegh : ES/P000762/1 Prejudice against Muslims has risen in the U.K. and Canada, threatening harmonious intergroup relations and the wellbeing of Muslims. Some politicians endorse and encourage interactions among people from diverse backgrounds, whereas others discourage this. It is currently unclear what effects this has on the desires of non-Muslims to interact with Muslims. Using Canadian politicians' twitter statements from the 2019 election period, themes related to politicians' statements about Muslims will be explored. Then positive and negative messaging about Muslims from this data will inform experimental manipulations to understand their impact on people's desires for intergroup contact. We will also examine emotion variables such as intergroup anxiety and trust. The findings will inform how politician messaging can impact intergroup relations, and the particular psychological mechanisms that account for these effects.

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