Machine learning for real-time prediction of intradialytic hypotension

Introduction<br/>In the Netherlands, approximately five thousand people are dependent on maintenance hemodialysis (HD) treatment. The one-year mortality of this patient population in 2021 was as high as 18%. A significant contributor to this high mortality and burden on HD patients is the high incidence of intradialytic hypotension (IDH), which...

DMQL: Deep Maximum Q-Learning: Combatting Relative Overgeneralisation in Deep Independent Learners using Optimism and Similarity

Various pathologies can occur when independent learners are used in cooperative Multi-Agent Reinforcement Learning. One such pathology is Relative Overgeneralisation, which manifests when a suboptimal Nash Equilibrium in the joint action space of a problem is preferred over an optimal Equilibrium. Approaches exist to combat relative...

Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light

Touching physical buttons to interact with public electronic devices has raised some concerns regrading disease transmission following the COVID-19 pandemic. The use of hand gestures as a touchless replacement sounds appealing, but comes with the challenge of recognizing which gesture is being performed by the user, with only the processing...

WMR prediction using recurrent neural networks on FX limit order book data

This thesis investigates the application of machine learning models on foreign exchange data around the WM/R 4pm Closing Spot Rate (colloquially known as the WMR Fix). Due to the nature of the market dynamics around the WMR Fix, inefficiencies can occur and therefore some predictability might be expected. We aim to find these inefficiencies....

Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM

Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is...

Quadcopter Loss-of-Control Prediction Using Recurrent Neural Networks

Loss-of-control (LOC) is the main cause of crashes for drones. On-board prevention systems should be designed that require low computing power and memory. Data-driven techniques serve as a solution. This study proposes the use of recurrent neural networks (RNN) for LOC prediction. The aim is to identify which RNN model is most suitable and if...

Semantic Segmentation using Deep Neural Networks for MAVs

Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we...

Automatic Psychological Text Analysis using Recurrent Neural Networks

Schema therapy is a type of psychological treatment for people suffering from personality disorders. A schema is a core psychological state of mind that influences external behaviour through the development of coping styles. Current schema therapy is time inefficient and human-processed. Enabling automatic schema classification helps the overall...

Short-Term Traffic Forecasting on Highways Using Neural Networks: A Congestion Pattern-Based Assessment

As traffic demands are ever increasing and building new infrastructure poses challenges in densely populated areas, it is important to optimally utilise existing infrastructure. Short-term traffic forecasting can help with this task, as its predictions can help to prevent congestion by rerouting vehicles. Recently, neural networks developed for...

Towards orientation estimation using a loosely attached IMU with a recurrent neural network

Inertial measurement units (IMUs) are getting more and more incorporated into our lives due to their improving accuracy, lower cost and smaller sizes. Applications for inertial-based orientation estimation can already be found in the field of computer vision, aerospace engineering, robotics, navigation, biomechanics, health monitoring and sports...

Self-Supervised Continual Learning for Interaction-Aware Pedestrian Prediction Models

Learning human motion prediction models online is key for autonomous navigation in unknown dynamic scenarios. Previous works focus solely on improving prediction network architectures, whilst training them offline. This paper introduces a self-supervised continual learning framework that continuously improves data-driven pedestrian trajectory...

Predicting pedestrian path using optical flow as context cue

A human driver can gauge the intention and signals given by other road users indicative of their future behaviour. The intentions and signals are identified by looking at the cues originating from vulnerable road users or their surroundings (hand signals, head orientation, posture, traffic signals, distance to curb, etc.). Taking all these cues...

Word recognition in a model of visually grounded speech: An analysis using techniques inspired by human speech processing research

A Visually Grounded Speech model is a neural model which is trained to embed image caption pairs closely together in a common embedding space. As a result, such a model can retrieve semantically related images given a speech caption and vice versa. The purpose of this research is to investigate whether and how a Visually Grounded Speech model...

Application of Deep Learning for Spacecraft Fault Detection and Isolation

Spacecraft require high availability, autonomous operation, and a high degree of mission success. Spacecraft use sensors, such as star trackers and GPS, and actuators, such as reaction wheels, to reach and maintain a correct attitude and position. Failures in these components will have a significant negative impact on the success of the mission,...

Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report

Very complex flows can be expensive to compute using current CFD techniques. In this thesis, models based on deep learning were used to replace certain parts of the flow domain, with the objective of replacing well-known regions with simplified models to increase efficiency. To keep the error produced by the deep learning model bounded, a...

DeepSleep: A sensor-agnostic approach towards modelling a sleep classification system

Sleep is a natural state of our mind and body during which our muscles heal and our memories are consolidated. It is such a habitual phenomenon that we have been viewing it as another ordinary task in our day-to-day life. However, owing to the current fast-paced, technology-driven generation, we are letting ourselves be sleep-deprived, giving...

Vehicle Trajectory Prediction Using Road Structure

An autonomous vehicle should be able to operate amidst numerous other human-driven vehicles, each driving on its own trajectory. To safely navigate in such a dynamic environment, the autonomous vehicle should be able to predict trajectories of the vehicles operating in its vicinity and use these to plan its own path. Most related work uses a...

Generating Robotic Manipulation Trajectories with Neural Networks

In order to interact with environments and appliances made for humans, robots should be able to manipulate a large variety of objects and appliances in human environments. When having experience with manipulating a certain object or appliance, a robot should be able to generalize this behaviour to novel, but similar objects and appliances. When...