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1000 tulosta hakusanalla John O'Reilly

The Life of O'Reilly

The Life of O'Reilly

John O'Reilly; Ivan Morris

Clock Tower Press
2004
nidottu
The Life of O'Reilly is a chronicle of the career of one of the European PGA Tour's most famous caddies-John O'Reilly-and it's full of funny stories as only this Irishman can tell them. Like the one about the time he and some fellow caddies were arrested and jailed in East Germany on their way to the German Open in Berlin. Or the one about the Tour pro who, in a fit of temper after a bad shot, put his foot through the bottom of his golf bag and could not get it out again! The Life of O'Reilly is a rollicking ride around the world-and the world of professional golf-by one of the game's most interesting characters. No golf library would be complete without it. "I hope that you'll find this book as entertaining as I found Johnny to be throughout our three successful years on tour. There was never a dull moment!" --Padraig Harrington
New Directions in Dynamical Systems, Automatic Control and Singular Perturbations
With this short book, Professor O'Reilly brings his considerable engineering experience to bear upon three subjects close to his heart: dynamical systems, automatic control and singular perturbations. New results of a fundamental and unifying nature are presented in all three areas. New directions are thereby established. Due care is taken of historical context and motivations. This highly readable book with its compelling physical narrative is divided into two parts, Part 1 and Part 2, for the reader’s convenience. Aimed primarily at engineers, this unusually affordable book should be read by every postgraduate. Part 1 sets out the fundamental conditions that small-signal physically realisable dynamical system models must satisfy. These fundamental conditions are causality and non-singularity. They apply to all small-signal dynamical system models, as for example arise in electrical networks. Another important example is automatic control. Part 1 of this book also re-interprets the classic works of Nyquist and Bode to establish that the uncontrolled system must also not be singular; nor must the controlled system encounter singularity. But Part 1 goes much further. It shows that these fundamental properties, in particular non-singularity, must obtain for all small-signal system models regardless of how many inputs and outputs the system happens to have. So, small-signal automatic control for instance is all of a piece. It is that simple. As for singular perturbations in Part 2, these little fellows simply pop up all over the place, sometimes where you least expect them. New associated low-frequency and high-frequency system transfer-function models are presented with almost insolent ease. Part 2 achieves for the frequency domain what standard singular perturbation theory does for the time domain. Moreover, even the standard nonlinear singularly perturbed system model does not escape scrutiny. Which model to choose? It could be important. Part 2 is an indispensable aid to modellers across the engineering spectrum seeking generic low-frequency and high-frequency models for what they do.
A New Approach to Forecasting

A New Approach to Forecasting

John O’Reilly

TROUBADOR PUBLISHING
2025
nidottu
The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetime’s engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used. No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system. In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use.