Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

gramEvol: Grammatical Evolution in R

We describe an R package which implements grammatical evolution (GE) for automatic program generation. By performing an unconstrained optimization over a population of R expressions generated via a user-defined grammar, programs which achieve a desired goal can be discovered. The package facilitates the coding and execution of GE programs, and supports parallel execution. In addition, three applications of GE in statistics and machine learning, including hyper-parameter optimization, classification and feature generation are studied

A pneumatic bionic voice prosthesis : pre-clinical trials of controlling the voice onset and offset

Despite emergent progress in many fields of bionics, a functional Bionic Voice prosthesis for laryngectomy patients (larynx amputees) has not yet been achieved, leading to a lifetime of vocal disability for these patients. This study introduces a novel framework of Pneumatic Bionic Voice Prostheses as an electronic adaptation of the Pneumatic Artificial Larynx (PAL) device. The PAL is a non-invasive mechanical voice source, driven exclusively by respiration with an exceptionally high voice quality, comparable to the existing gold standard of Tracheoesophageal (TE) voice prosthesis. Following PAL design closely as the reference, Pneumatic Bionic Voice Prostheses seem to have a strong potential to substitute the existing gold standard by generating a similar voice quality while remaining non-invasive and non-surgical. This paper designs the first Pneumatic Bionic Voice prosthesis and evaluates its onset and offset control against the PAL device through pre-clinical trials on one laryngectomy patient. The evaluation on a database of more than five hours of continuous/isolated speech recordings shows a close match between the onset/offset control of the Pneumatic Bionic Voice and the PAL with an accuracy of 98.45 ±0.54%. When implemented in real-time, the Pneumatic Bionic Voice prosthesis controller has an average onset/offset delay of 10 milliseconds compared to the PAL. Hence it addresses a major disadvantage of previous electronic voice prostheses, including myoelectric Bionic Voice, in meeting the short time-frames of controlling the onset/offset of the voice in continuous speech

Auto-label detection for two samples of recording of in continuous speech.

<p>a, b) The patient is reading the rainbow passage. The time axis shows the speed of onset and offset occurrence in speech. The auto-labeller keeps up with the accuracy with conversational speech.</p

Components of natural control of phonation.

<p>The involuntary control system benefits from feedback from the hearing, respiratory, laryngeal and articulation systems. P: Pressure sensing feedback, J: articulator positions sensing feedback, M: Muscle movement sensing feedback (adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192257#pone.0192257.ref016" target="_blank">16</a>]).</p

The logarithmic error, cost function <i>f</i>_<i>t</i>(<i>θ</i>_<i>H</i>, <i>θ</i>_<i>L</i>), calculated over the search domain for <i>θ</i>_<i>H</i>, <i>θ</i>_<i>L</i> for the dataset.

<p>The logarithmic error, cost function <i>f</i>_<i>t</i>(<i>θ</i>_<i>H</i>, <i>θ</i>_<i>L</i>), calculated over the search domain for <i>θ</i>_<i>H</i>, <i>θ</i>_<i>L</i> for the dataset.</p

The cost function (error) vs. <i>θ</i>_<i>H</i> and <i>θ</i>_<i>L</i>, when the other threshold value is set to optimal.

<p>The median values of the pressure amplitude <i>P</i>_<i>g</i>(<i>t</i>) at onset and offsets are plotted as dashed lines as a rough estimate of <i>θ</i>_<i>H</i>, <i>θ</i>_<i>L</i> respectively.</p

The average accuracy of the hysteresis model in matching manual labels when the hysteresis thresholds are concurrently adapted through three different optimization methods.

<p>The bars show the variance of the accuracy for 24 isolated and continuous recordings of 45<i>s</i> each. The results are compared against the offline MSE thresholds driven for the full length of 45<i>s</i> of each recording.</p

The three components of a Pneumatic Bionic Voice system as a conceptualized device.

<p>A) The hybrid voice source, B) the respiratory pressure sensing by S: stoma and M: mouth pressure sensors, C) the Control unit.</p

Threshold optimisation on the offline recorded data.

<p>The laryngectomy patient used the PAL to generate continuous speech. a, b) Pre-processing of raw stoma and mouth pressures result <i>P</i>_<i>s</i>(<i>t</i>), <i>P</i>_<i>m</i>(<i>t</i>), c) The low-pass filtered pressure difference (<i>P</i>_<i>g</i> = <i>P</i>_<i>s</i>(<i>t</i>)—<i>P</i>_<i>m</i>(<i>t</i>)) as the input to the hysteresis model (solid line), threshold values (dashed lines) and the onset/offset instances (circles), d) comparison of the MSE labels of the offline PAL model with the manual.</p