Neonatal Seizure Detection using Convolutional Neural Networks

This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state-of-the-art SVM-based neonatal seizure detector, which operates on a set of carefully designed hand-crafted features. The fully convolutional architecture allows for the localization of EEG waveforms and patterns that result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin

The effect of viscosity on the maximisation of electrical power from a wave energy converter under predictive control

In this paper, the non-linear effects of viscosity on the performance of a Wave Energy Converter (WEC) system are analysed. A standard linear Model Predictive Control (MPC) is used to show the negative effects that the unaccounted non-linear viscosity force in the hydrodynamic system has on the power absorption. A non-linear MPC (NLMPC) is then implemented, where the non-linear viscosity effects are included in the optimisation. A linear drag coefficient estimate of the non-linear viscosity is then included in the linear MPC; creating a Linear Viscous Model Predictive Control. When constraints are incorporated, it is shown that a single choice of the linear viscous drag coefficient for use within the linear MPC can provide comparable results to the non-linear MPC approach, over a wide range of sea states

The effect of model inaccuracy and move-blocking on the performance of a wave-to-wire wave energy converter , under economic predictive control

In this paper, an economic Model Predictive Control (MPC) is used to investigate the effects that arise from the model mismatch between the control and the system. It is shown that the average electrical power is affected by the modelling discrepancies, but that the performance is still acceptable. A move-blocking technique is incorporated into the structure of the control horizon of the MPC, where the move-blocking decreases the computational burden whilst maintaining system performance, hence drastically reducing the optimisation solving time. The MPC with the move-blocking incorporated is then tested on the most significant mismatch, where it is shown that the control horizon of the MPC can be drastically reduced while maintaining system performance

Wave to wire power maximisation from a wave energy converter

In this paper a back-to-back voltage source converter controlled linear permanent magnet generator (LPMG) is utilised as the power take off (PTO) for a point absorber wave energy converter system (WEC). It is shown that reactive control which seems promising when an ideal PTO is assumed, is actually infeasible with a real PTO as the electrical losses of the LPMG are excessive when the wave frequency is lower than the natural frequency. A Zero Order Hold (ZOH) and First Order Hold (FOH) Model Predictive Control (MPC) which maximises the mechanical power is first utilised. The two MPC systems show that more electrical power is extracted for a lower horizon when the MPC is optimised for mechanical power. The electrical losses from the LPMG and voltage source converter (VSC) are then incorporated in the cost function of the MPC systems and demonstrates significant improvements in the electrical power extracted when compared to the electrical power extracted via mechanical power optimisation. PTO force and heave displacement constraints are then incorporated into the optimisation, to further demonstrate the limitations of performance when a realistic PTO is utilised. It is shown here that the electrical power can be maximised, whilst the PTO force and heave displacement are shown to be within limits. The power quality from the ZOH MPC is then compared to the power quality from the FOH MPC

Distributed hierarchical droop control of boost converters in DC microgrids

Voltage stability and accurate current-sharing are primary features of an efficiently operating power distribution network, such as a dc islanded-microgrid. This paper presents the development of a distributed hierarchical droop control architecture for dc-dc boost converters within a dc islanded-microgrid. Decentralised controllers are conventionally designed for local voltage and current control without accounting for coupling to other converters. However, due to the non-minimum phase action of boost converters, global knowledge of coupling is required to inform stable local controller tuning over a range of load disturbances. Consensus-based distributed secondary controllers, utilising low-bandwidth communications, are designed to coordinate voltage levels and improve current-sharing accuracy. The control architecture is tested in response to communication faults, non-linear loads, and plug-and-play operations