Browsing by Department "University of Leeds"

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How can airborne transmission of COVID-19 indoors be minimised?
(2020-09) Morawska, Lidia; Tang, Julian W.; Bahnfleth, William; Bluyssen, Philomena M.; Boerstra, Atze; Buonanno, Giorgio; Cao, Junji; Dancer, Stephanie; Floto, Andres; Franchimon, Francesco; Haworth, Charles; Hogeling, Jaap; Isaxon, Christina; Jimenez, Jose L.; Kurnitski, Jarek; Li, Yuguo; Loomans, Marcel; Marks, Guy; Marr, Linsey C.; Mazzarella, Livio; Melikov, Arsen Krikor; Miller, Shelly; Milton, Donald K.; Nazaroff, William; Nielsen, Peter V.; Noakes, Catherine; Peccia, Jordan; Querol, Xavier; Sekhar, Chandra; Seppänen, Olli; Tanabe, Shin ichi; Tellier, Raymond; Tham, Kwok Wai; Wargocki, Pawel; Wierzbicka, Aneta; Yao, Maosheng
Letter
During the rapid rise in COVID-19 illnesses and deaths globally, and notwithstanding recommended precautions, questions are voiced about routes of transmission for this pandemic disease. Inhaling small airborne droplets is probable as a third route of infection, in addition to more widely recognized transmission via larger respiratory droplets and direct contact with infected people or contaminated surfaces. While uncertainties remain regarding the relative contributions of the different transmission pathways, we argue that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors. Appropriate building engineering controls include sufficient and effective ventilation, possibly enhanced by particle filtration and air disinfection, avoiding air recirculation and avoiding overcrowding. Often, such measures can be easily implemented and without much cost, but if only they are recognised as significant in contributing to infection control goals. We believe that the use of engineering controls in public buildings, including hospitals, shops, offices, schools, kindergartens, libraries, restaurants, cruise ships, elevators, conference rooms or public transport, in parallel with effective application of other controls (including isolation and quarantine, social distancing and hand hygiene), would be an additional important measure globally to reduce the likelihood of transmission and thereby protect healthcare workers, patients and the general public.
A probabilistic framework for learning geometry-based robot manipulation skills
(2021-07) Abu-Dakka, Fares J.; Huang, Yanlong; Silvério, João; Kyrki, Ville
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Programming robots to perform complex manipulation tasks is difficult because many tasks require sophisticated controllers that may rely on data such as manipulability ellipsoids, stiffness/damping and inertia matrices. Such data are naturally represented as Symmetric Positive Definite (SPD) matrices to capture specific geometric characteristics of the data, which increases the complexity of hard-coding them. To alleviate this difficulty, the Learning from Demonstration (LfD) paradigm can be used in order to learn robot manipulation skills with specific geometric constraints encapsulated in SPD matrices. Learned skills often need to be adapted when they are applied to new situations. While existing techniques can adapt Cartesian and joint space trajectories described by various desired points, the adaptation of motion skills encapsulated in SPD matrices remains an open problem. In this paper, we introduce a new LfD framework that can learn robot manipulation skills encapsulated in SPD matrices from expert demonstrations and adapt them to new situations defined by new start-, via- and end-matrices. The proposed approach leverages Kernelized Movement Primitives (KMPs) to generate SPD-based robot manipulation skills that smoothly adapt the demonstrations to conform to new constraints. We validate the proposed framework using a couple of simulations in addition to a real experiment scenario.