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Your search keyword '"Fitzpatrick, Michael E."' showing total 322 results
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322 results on '"Fitzpatrick, Michael E."'

1. Single-cell integration reveals metaplasia in inflammatory gut diseases

Author

Oliver, Amanda J., Huang, Ni, Bartolome-Casado, Raquel, Li, Ruoyan, Koplev, Simon, Nilsen, Hogne R., Moy, Madelyn, Cakir, Batuhan, Polanski, Krzysztof, Gudiño, Victoria, Melón-Ardanaz, Elisa, Sumanaweera, Dinithi, Dimitrov, Daniel, Milchsack, Lisa Marie, FitzPatrick, Michael E. B., Provine, Nicholas M., Boccacino, Jacqueline M., Dann, Emma, Predeus, Alexander V., To, Ken, Prete, Martin, Chapman, Jonathan A., Masi, Andrea C., Stephenson, Emily, Engelbert, Justin, Lobentanzer, Sebastian, Perera, Shani, Richardson, Laura, Kapuge, Rakeshlal, Wilbrey-Clark, Anna, Semprich, Claudia I., Ellams, Sophie, Tudor, Catherine, Joseph, Philomeena, Garrido-Trigo, Alba, Corraliza, Ana M., Oliver, Thomas R. W., Hook, C. Elizabeth, James, Kylie R., Mahbubani, Krishnaa T., Saeb-Parsy, Kourosh, Zilbauer, Matthias, Saez-Rodriguez, Julio, Høivik, Marte Lie, Bækkevold, Espen S., Stewart, Christopher J., Berrington, Janet E., Meyer, Kerstin B., Klenerman, Paul, Salas, Azucena, Haniffa, Muzlifah, Jahnsen, Frode L., Elmentaite, Rasa, and Teichmann, Sarah A.

Published
2024
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3. R-WhONet: Recalibrated Wheel Odometry Neural Network for Vehicular Positioning using Transfer Learning

Author

Onyekpe, Uche, Szkolnik, Alicja, Palade, Vasile, Kanarachos, Stratis, and Fitzpatrick, Michael E.

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper proposes a transfer learning approach to recalibrate our previously developed Wheel Odometry Neural Network (WhONet) for vehicle positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. The WhONet has been shown to possess the capability to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning of vehicles. These uncertainties may be manifested as tyre pressure changes from driving on muddy and uneven terrains or wheel slips. However, a common cause for concern for data-driven approaches, such as the WhONet model, is usually the inability to generalise the models to a new vehicle. In scenarios where machine learning models are trained in a specific domain but deployed in another domain, the model's performance degrades. In real-life scenarios, several factors are influential to this degradation, from changes to the dynamics of the vehicle to new pattern distributions of the sensor's noise, and bias will make the test sensor data vary from training data. Therefore, the challenge is to explore techniques that allow the trained machine learning models to spontaneously adjust to new vehicle domains. As such, we propose the Recalibrated-Wheel Odometry neural Network (R-WhONet), that adapts the WhONet model from its source domain (a vehicle and environment on which the model is initially trained) to the target domain (a new vehicle on which the trained model is to be deployed). Through a performance evaluation on several GNSS outage scenarios - short-term complex driving scenarios, and on longer-term GNSS outage scenarios. We demonstrate that a model trained in the source domain does not generalise well to a new vehicle in the target domain. However, we show that our new proposed framework improves the generalisation of the WhONet model to new vehicles in the target domains by up to 32%., Comment: arXiv admin note: text overlap with arXiv:2104.02581

Published
2022

6. WhONet: Wheel Odometry Neural Network for Vehicular Localisation in GNSS-Deprived Environments

Author

Onyekpe, Uche, Palade, Vasile, Herath, Anuradha, Kanarachos, Stratis, and Fitzpatrick, Michael E.

Subjects
Computer Science - Robotics
Abstract

In this paper, a deep learning approach is proposed to accurately position wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived environments. In the absence of GNSS signals, information on the speed of the wheels of a vehicle (or other robots alike), recorded from the wheel encoder, can be used to provide continuous positioning information for the vehicle, through the integration of the vehicle's linear velocity to displacement. However, the displacement estimation from the wheel speed measurements are characterised by uncertainties, which could be manifested as wheel slips or/and changes to the tyre size or pressure, from wet and muddy road drives or tyres wearing out. As such, we exploit recent advances in deep learning to propose the Wheel Odometry neural Network (WhONet) to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning. The performance of the proposed WhONet is first evaluated on several challenging driving scenarios, such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts). WhONet's performance is then further and extensively evaluated on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration, respectively over a total distance of 493 km. The experimental results obtained show that the proposed method is able to accurately position the vehicle with up to 93% reduction in the positioning error of its original counterpart after any 180s of travel. WhONet's implementation can be found at https://github.com/onyekpeu/WhONet.

Published
2021
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11. Spontaneous Fruit Fly Optimisation for truss weight minimisation: Performance evaluation based on the no free lunch theorem

Author

Onyekpe, Uche, Kanarachos, Stratis, and Fitzpatrick, Michael E.

Subjects
Mathematics - Optimization and Control, Computer Science - Computational Engineering, Finance, and Science
Abstract

Over the past decade, several researchers have presented various optimisation algorithms for use in truss design. The no free lunch theorem implies that no optimisation algorithm fits all problems; therefore, the interest is not only in the accuracy and convergence rate of the algorithm but also the tuning effort and population size required for achieving the optimal result. The latter is particularly crucial for computationally intensive or high-dimensional problems. Contrast-based Fruit-fly Optimisation Algorithm (c-FOA) proposed by Kanarachos et al. in 2017 is based on the efficiency of fruit flies in food foraging by olfaction and visual contrast. The proposed Spontaneous Fruit Fly Optimisation (s-FOA) enhances c-FOA and addresses the difficulty in solving nonlinear optimisation algorithms by presenting standard parameters and lean population size for use on all optimisation problems. Six benchmark problems were studied to assess the performance of s-FOA. A comparison of the results obtained from documented literature and other investigated techniques demonstrates the competence and robustness of the algorithm in truss optimisation., Comment: Presented at the International conference for sustainable materials, energy and technologies, 2019

Published
2019

15. A conserved population of MHC II-restricted, innate-like, commensal-reactive T cells in the gut of humans and mice

Author

Hackstein, Carl-Philipp, Costigan, Dana, Drexhage, Linnea, Pearson, Claire, Bullers, Samuel, Ilott, Nicholas, Akther, Hossain Delowar, Gu, Yisu, FitzPatrick, Michael E. B., Harrison, Oliver J., Garner, Lucy C., Mann, Elizabeth H., Pandey, Sumeet, Friedrich, Matthias, Provine, Nicholas M., Uhlig, Holm H., Marchi, Emanuele, Powrie, Fiona, Klenerman, Paul, and Thornton, Emily E.

Published
2022
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18. Vehicle Dynamics Virtual Sensing Using Unscented Kalman Filter: Simulations and Experiments in a Driver-in-the-Loop Setup

Author

Acosta, Manuel, Kanarachos, Stratis, Fitzpatrick, Michael E., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Gusikhin, Oleg, editor, and Madani, Kurosh, editor

Published
2020
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40. Fatigue crack growth behaviour and life prediction following a lifetime extension strategy in the low-cycle fatigue regime for FV566 turbine blade steel

Author

Cunningham, Benjamin M.D., Leering, Mitchell, Fan, Yuhui, You, Chao, Morris, Andrew, Reed, Philippa A.S., Hamilton, Andrew R., and Fitzpatrick, Michael E.

Abstract

Bend bars made from FV566 martensitic stainless steel were extracted from the root of ex-service power plant turbine blades and representative notches introduced. Constant amplitude cyclic loading, and with overload cycles of OLR of 1.56 applied every 150 baseload cycles, were applied to the samples until a crack of 1 mm was observed. A ‘lifetime extension strategy’ was applied, involving grinding away existing short cracks, followed by T0 shot peening on the top surface of the sample. Fatigue cyclic loading was recommenced with initial strain range loading conditions until failure. A lifetime prediction method combining the Coffin-Manson and Paris Law relationships were used to investigate the efficacy of the ‘lifetime extension strategy’ for industrial use. The ‘lifetime extension strategy’ increased the life of U-notched samples tested and a minimum increase in strain range following the application of the ‘lifetime extension strategy’ was estimated. It is expected that the application of a’ lifetime extension strategy’ such as the one tested may provide some additional service life to existing in-service turbine blades.

Published
2023

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