Zobrazeno 1 - 10
of 38
pro vyhledávání: '"James S Welsh"'
Publikováno v:
IFAC-PapersOnLine. 54:339-344
In this paper, we propose an adaptive identification method for continuous-time systems with slowly time-varying parameters subject to infrequent abrupt changes using sampled data in the presence of measurement noise. The proposed estimator consists
Autor:
James W. Denham, John Simpson, Stephan K. Chalup, Neda Gholizadeh, Sabbir Siddique, Peter Lau, Peter B. Greer, James S. Welsh, Saadallah Ramadan, Jason Dowling
Publikováno v:
Journal of Applied Clinical Medical Physics
Purpose The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), compri
Publikováno v:
Automatica, 106, 161-167. Elsevier
Bayesian learning techniques have recently garnered significant attention in the system identification community. Originally introduced for low variance estimation of linear impulse response models, the concept has since been extended to the nonlinea
Autor:
Caesar E. Ordoñez, Christina Sarosiek, Ethan A. DeJongh, Nicholas T. Karonis, George Coutrakon, John R. Winans, V. Rykalin, Kirk L. Duffin, Mark Pankuch, Don F. DeJongh, James S. Welsh
Publikováno v:
Med Phys
Verification of patient specific proton stopping powers obtained in the patient treatment position can be used to reduce the distal margins needed in particle beam planning. Proton radiography can be used as a pre-treatment instrument to verify integ
For many years, the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC) has been widely used for identification. The intersample behaviour of the input plays an important role in this method, and it has been shown rece
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::034f427c08518f8782bbdf812ea24036
http://arxiv.org/abs/2005.02662
http://arxiv.org/abs/2005.02662
Publikováno v:
Applied Sciences
Volume 10
Issue 7
Applied Sciences, Vol 10, Iss 2601, p 2601 (2020)
Volume 10
Issue 7
Applied Sciences, Vol 10, Iss 2601, p 2601 (2020)
In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and co
Autor:
James S. Welsh, Jeremy G. Stoddard
Publikováno v:
IFAC-PapersOnLine. 51:1014-1019
There exists a number of nonparametric model structures specifically developed for frequency domain modelling of nonlinear systems. Here we consider the nonlinear output frequency response function (NOFRF) structure, which is a series of input-depend
Autor:
Jeremy G. Stoddard, James S. Welsh
Publikováno v:
IFAC-PapersOnLine. 51:413-418
In the nonlinear setting, nonparametric estimation methods are convenient because they do not require a detailed model structure selection and can be used with limited prior knowledge on the system of interest. In this paper, we consider the cascaded
Publikováno v:
IEEE Transactions on Control Systems Technology
IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, 2018, 26 (1), pp.329-335. ⟨10.1109/TCST.2016.2646320⟩
IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, 2018, 26 (1), pp.329-335. ⟨10.1109/TCST.2016.2646320⟩
International audience; An instrumental variable (IV) solution for wideband frequency-domain identification is developed in this paper. One of the most successful IV approaches for time-domain data is known as the simple refined IV method for continu
Autor:
James S. Welsh, Siqi Pan
Publikováno v:
IFAC-PapersOnLine. 51:790-795
Indirect inference has been widely used in the field of econometrics for time series data, and only introduced to the field of system identification in recent years. It is especially useful as a parameter estimation technique when the log-likelihood