Passive Fault-Tolerant Augmented Neural Lyapunov Control: A method to synthesise control functions for marine vehicles affected by actuators faults

Closed-loop stability of control systems can be undermined by actuator faults. Redundant actuator sets and Fault-Tolerant Control (FTC) strategies can be exploited to enhance system resiliency to loss of actuator efficiency, complete failures or jamming. Passive FTC methods entail designing a fixed-gain control law that can preserve the...

Wearable airbag technology and machine learned models to mitigate falls after stroke

Background: Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed...

Long-Short Term Memory Model for chromosomal aberration detection in Non-Invasive Prenatal Testing

In 1997 it was discovered that fragments of DNA circulate freely in the blood plasma and, in the case of pregnancy, this DNA consists of DNA belonging to both the mother and the fetus. This circulating free DNA has made it possible to test for chromosomal aberration in the fetus through non-invasive methods, thereby avoiding the 1 in 100 chance...

Obey validity limits of data-driven models through topological data analysis and one-class classification

Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus, it is critical to ensure that data-driven models are not evaluated outside their validity...

Machine Learning of Synthetic Lethality: Data Integration, Generalisation, and Selection Bias

Synthetic lethality (SL) arises between two genes when loss of function of both genes would lead cells to become inviable. This can be exploited for therapy, where a drug is used to selectively kill diseased cells by perturbing one gene of an SL pair where the other gene is inactive (e.g. through naturally occurring mutation). Computational...

Data-driven modelling of the Reynolds stress tensor using random forests with invariance

A novel machine learning algorithm is presented, serving as a data-driven turbulence modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine learning algorithm, called the Tensor Basis Random Forest (TBRF), is used to predict the Reynolds-stress anisotropy tensor, while guaranteeing Galilean invariance by making use...

Machine-learnt design guidelines for axial vanes in Supersonic turbines operating with non-ideal compressible flows

The scientific community is consistently focused on identifying new sources of energy, which can reduce the consequences of climate change and depleting natural resources. Organic Rankine Cycle (ORC) based power systems have been touted as one of the promising technologies of extracting thermal energy from waste-heat and renewable sources such...