2024
Devlin, L., Carter, M., Horridge, P., Green, P. L., & Maskell, S. (2024). The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters, 1-5. doi:10.1109/lsp.2024.3386494DOI: 10.1109/lsp.2024.3386494
Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach (Journal article)
Echeverria-Rios, D., & Green, P. L. (2024). Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach. Engineering Applications of Artificial Intelligence, 127, 107233. doi:10.1016/j.engappai.2023.107233DOI: 10.1016/j.engappai.2023.107233
2023
Jayasinghe, S., Paoletti, P., Jones, N., & Green, P. L. (n.d.). Predicting gas pores from photodiode measurements in laser powder bed fusion builds. Progress in Additive Manufacturing. doi:10.1007/s40964-023-00489-6DOI: 10.1007/s40964-023-00489-6
2022
Effective tree-based classification for automated flow cytometry data analysis on samples with suspected haematological malignancy (Preprint)
DOI: 10.1101/2022.12.07.22283209
Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference (Journal article)
Wu, J., Wen, L., Green, P. L., Li, J., & Maskell, S. (2022). Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. STATISTICS AND COMPUTING, 32(1). doi:10.1007/s11222-021-10075-xDOI: 10.1007/s11222-021-10075-x
2021
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels (Journal article)
Green, P., Devlin, L., Moore, R., Jackson, R., Li, J., & Maskell, S. (2021). Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels. Mechanical Systems and Signal Processing. doi:10.1016/j.ymssp.2021.108028DOI: 10.1016/j.ymssp.2021.108028
2020
Roberts, J. W., Sutcliffe, C. J., Green, P. L., & Black, K. (2020). Modelling of metallic particle binders for increased part density in binder jet printed components. ADDITIVE MANUFACTURING, 34. doi:10.1016/j.addma.2020.101244DOI: 10.1016/j.addma.2020.101244
Jackson, R. D., Jump, M., & Green, P. L. (2020). Predicting On-axis Rotorcraft Dynamic Responses Using Machine Learning Techniques. Journal of the American Helicopter Society, 65(3), 1-12. doi:10.4050/jahs.65.032004DOI: 10.4050/jahs.65.032004
Le Carrer, N., & Green, P. L. (2020). A possibilistic interpretation of ensemble forecasts: experiments on the imperfect Lorenz 96 system. Advances in Science and Research, 17, 39-45. doi:10.5194/asr-17-39-2020DOI: 10.5194/asr-17-39-2020
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels (Preprint)
DOI: 10.48550/arxiv.2004.12838
Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements (Journal article)
Jayasinghe, S., Paoletti, P., Sutcliffe, C., Dardis, J., Jones, N., & Green, P. L. (2022). Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. PROGRESS IN ADDITIVE MANUFACTURING, 7(2), 143-160. doi:10.1007/s40964-021-00219-wDOI: 10.1007/s40964-021-00219-w
Optimising cargo loading and ship scheduling in tidal areas (Journal article)
Le Carrer, N., Ferson, S., & Green, P. L. (2020). Optimising cargo loading and ship scheduling in tidal areas. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 280(3), 1082-1094. doi:10.1016/j.ejor.2019.08.002DOI: 10.1016/j.ejor.2019.08.002
2019
Predicting On-Axis Rotorcraft Dynamic Responses Using Machine Learning Techniques (Journal article)
Jackson, R., Jump, M., & Green, P. (2019). Predicting On-Axis Rotorcraft Dynamic Responses Using Machine Learning Techniques. doi:10.20944/preprints201907.0348.v1DOI: 10.20944/preprints201907.0348.v1
Automatic Fault Detection for Selective Laser Melting using Semi-Supervised Machine Learning (Journal article)
Okaro, I., Jayasinghe, S., Sutcliffe, C., Black, K., Paoletti, P., & Green, P. (2019). Automatic Fault Detection for Selective Laser Melting using Semi-Supervised Machine Learning. Additive Manufacturing, 27, 42-53. doi:10.1016/j.addma.2019.01.006DOI: 10.1016/j.addma.2019.01.006
2018
Mendoza-Puchades, M., Green, P. L., & Judge, R. (2018). Variability in masonry behaviour and modelling under blast and seismic actions. PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-STRUCTURES AND BUILDINGS, 171(10), 768-777. doi:10.1680/jstbu.17.00088DOI: 10.1680/jstbu.17.00088
Green, P., Chodora, E., Zhu, Z., & Atamturktur, S. (2018). Towards the Validation of Dynamical Models in Regions where there is no Data. doi:10.20944/preprints201809.0389.v1DOI: 10.20944/preprints201809.0389.v1
Jackson, R., Jump, M., & Green, P. (2018). Towards Gaussian Process Models of Complex Rotorcraft Dynamics. In HS International’s 74th Annual Forum and Technology Display; The Future of Vertical Flight. Phoenix, Arizona, USA.
2017
Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers (Journal article)
Green, P. L., & Maskell, S. (2017). Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing, 93, 379-396. doi:10.1016/j.ymssp.2016.12.023DOI: 10.1016/j.ymssp.2016.12.023
Ahmed, T. M., Green, P. L., & Khalid, H. A. (2017). Predicting fatigue performance of hot mix asphalt using artificial neural networks. Road Materials and Pavement Design, 18(sup2), 141-154. doi:10.1080/14680629.2017.1306928DOI: 10.1080/14680629.2017.1306928
A machine learning approach to nonlinear modal analysis. (Journal article)
Worden, K., & Green, P. (2017). A machine learning approach to nonlinear modal analysis.. Mechanical Systems and Signal Processing, 84(Part B), 34-53. doi:10.1016/j.ymssp.2016.04.029DOI: 10.1016/j.ymssp.2016.04.029
2016
Scott, M., Green, P. L., O’Driscoll, D., Worden, K., & Sims, N. (2016). Sensitivity analysis of an Advanced Gas-cooled Reactor control rodmodel. Nuclear Engineering and Design.
Probabilistic modelling of a rotational energy harvester (Journal article)
Green, P. L., Hendijanizadeh, M., Simeone, L., & Elliott, S. J. (2016). Probabilistic modelling of a rotational energy harvester. JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 27(4), 528-536. doi:10.1177/1045389X15573343DOI: 10.1177/1045389X15573343
Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves (Journal article)
Hill, T. L., Green, P. L., Cammarano, A., & Neild, S. A. (2016). Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves. JOURNAL OF SOUND AND VIBRATION, 360, 156-170. doi:10.1016/j.jsv.2015.09.007DOI: 10.1016/j.jsv.2015.09.007
Nonlinear System Identification Through Backbone Curves and Bayesian Inference (Chapter)
Cammarano, A., Green, P. L., Hill, T. L., & Neild, S. A. (2016). Nonlinear System Identification Through Backbone Curves and Bayesian Inference. In Nonlinear Dynamics, Volume 1 (pp. 255-262). Springer International Publishing. doi:10.1007/978-3-319-15221-9_23DOI: 10.1007/978-3-319-15221-9_23
2015
Using Particle Filters to Analyse the Credibility in Model Predictions (Conference Paper)
Green, P. L. (n.d.). Using Particle Filters to Analyse the Credibility in Model Predictions. In Applied Mechanics and Materials Vol. 807 (pp. 218-225). Trans Tech Publications, Ltd.. doi:10.4028/www.scientific.net/amm.807.218DOI: 10.4028/www.scientific.net/amm.807.218
Green, P. L., & Worden, K. (2015). Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 373(2051). doi:10.1098/rsta.2014.0405DOI: 10.1098/rsta.2014.0405
Green, P. L. (2015). Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution. PROBABILISTIC ENGINEERING MECHANICS, 42, 54-63. doi:10.1016/j.probengmech.2015.09.010DOI: 10.1016/j.probengmech.2015.09.010
Friction estimation in wind turbine blade bearings (Journal article)
Stevanović, N., Green, P. L., Worden, K., & Kirkegaard, P. H. (2016). Friction estimation in wind turbine blade bearings. Structural Control and Health Monitoring, 23(1), 103-122. doi:10.1002/stc.1752DOI: 10.1002/stc.1752
Green, P. L., Cross, E. J., & Worden, K. (2015). Bayesian system identification of dynamical systems using highly informative training data. Mechanical Systems and Signal Processing, 56-57, 109-122. doi:10.1016/j.ymssp.2014.10.003DOI: 10.1016/j.ymssp.2014.10.003
Green, P. L. (2015). Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing. Mechanical Systems and Signal Processing, 52-53(February 2019), 133-146. doi:10.1016/j.ymssp.2014.07.010DOI: 10.1016/j.ymssp.2014.07.010
2014
A Machine Learning Approach to Nonlinear Modal Analysis (Chapter)
Worden, K., & Green, P. L. (2014). A Machine Learning Approach to Nonlinear Modal Analysis. In Dynamics of Civil Structures, Volume 4 (pp. 521-528). Springer International Publishing. doi:10.1007/978-3-319-04546-7_56DOI: 10.1007/978-3-319-04546-7_56
Bayesian System Identification of Dynamical Systems Using Reversible Jump Markov Chain Monte Carlo (Chapter)
Tiboaca, D., Green, P. L., Barthorpe, R. J., & Worden, K. (2014). Bayesian System Identification of Dynamical Systems Using Reversible Jump Markov Chain Monte Carlo. In Topics in Modal Analysis II, Volume 8 (pp. 277-284). Springer International Publishing. doi:10.1007/978-3-319-04774-4_27DOI: 10.1007/978-3-319-04774-4_27
Bayesian System Identification of MDOF Nonlinear Systems Using Highly Informative Training Data (Chapter)
Green, P. L. (2014). Bayesian System Identification of MDOF Nonlinear Systems Using Highly Informative Training Data. In Topics in Modal Analysis II, Volume 8 (pp. 257-265). Springer International Publishing. doi:10.1007/978-3-319-04774-4_25DOI: 10.1007/978-3-319-04774-4_25
Identification of Time-Varying Nonlinear Systems Using Differential Evolution Algorithm (Chapter)
Perisic, N., Green, P. L., Worden, K., & Kirkegaard, P. H. (2014). Identification of Time-Varying Nonlinear Systems Using Differential Evolution Algorithm. In Topics in Modal Analysis, Volume 7 (pp. 575-583). Springer New York. doi:10.1007/978-1-4614-6585-0_56DOI: 10.1007/978-1-4614-6585-0_56
2013
Green, P. L., Worden, K., & Sims, N. D. (2013). On the identification and modelling of friction in a randomly excited energy harvester. Journal of Sound and Vibration, 332(19), 4696-4708. doi:10.1016/j.jsv.2013.04.024DOI: 10.1016/j.jsv.2013.04.024
Green, P. L., Papatheou, E., & Sims, N. D. (2013). Energy harvesting from human motion and bridge vibrations: An evaluation of current nonlinear energy harvesting solutions. Journal of Intelligent Material Systems and Structures, 24(12), 1494-1505. doi:10.1177/1045389x12473379DOI: 10.1177/1045389x12473379
Modelling Friction in a Nonlinear Dynamic System via Bayesian Inference (Chapter)
Green, P. L., & Worden, K. (2013). Modelling Friction in a Nonlinear Dynamic System via Bayesian Inference. In Special Topics in Structural Dynamics, Volume 6 (pp. 543-553). Springer New York. doi:10.1007/978-1-4614-6546-1_57DOI: 10.1007/978-1-4614-6546-1_57
2012
Green, P. L., Worden, K., Atallah, K., & Sims, N. D. (2012). The effect of Duffing-type non-linearities and Coulomb damping on the response of an energy harvester to random excitations. Journal of Intelligent Material Systems and Structures, 23(18), 2039-2054. doi:10.1177/1045389x12446520DOI: 10.1177/1045389x12446520
Green, P. L., Worden, K., Atallah, K., & Sims, N. D. (2012). The benefits of Duffing-type nonlinearities and electrical optimisation of a mono-stable energy harvester under white Gaussian excitations. Journal of Sound and Vibration, 331(20), 4504-4517. doi:10.1016/j.jsv.2012.04.035DOI: 10.1016/j.jsv.2012.04.035
Energy harvesting from human motion: an evaluation of current nonlinear energy harvesting solutions (Journal article)
Green, P. L., Papatheou, E., & Sims, N. D. (2012). Energy harvesting from human motion: an evaluation of current nonlinear energy harvesting solutions. Journal of Physics: Conference Series, 382, 012023. doi:10.1088/1742-6596/382/1/012023DOI: 10.1088/1742-6596/382/1/012023
A short investigation of the effect of an energy harvesting backpack on the human gait (Conference Paper)
Papatheou, E., Green, P., Racic, V., Brownjohn, J. M. W., & Sims, N. D. (2012). A short investigation of the effect of an energy harvesting backpack on the human gait. In H. A. Sodano (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.915524DOI: 10.1117/12.915524
The Benefits of Duffing-type Nonlinearities and Electrical Optimisation of a Randomly Excited Energy Harvester (Chapter)
Green, P. L., Worden, K., Atallah, K., & Sims, N. D. (2012). The Benefits of Duffing-type Nonlinearities and Electrical Optimisation of a Randomly Excited Energy Harvester. In Topics in Modal Analysis II, Volume 6 (pp. 657-667). Springer New York. doi:10.1007/978-1-4614-2419-2_65DOI: 10.1007/978-1-4614-2419-2_65
