Evacuation slides are critical aircraft safety components governed by stringent regulatory standards set by agencies like the Federal Aviation Administration and European Union Aviation Safety Agency. To comply with these standards, the maintenance and repacking of slides, currently performed manually, require operators to follow hundreds of precise steps. This process is labor-intensive, error-prone, and can result in costly delays and safety risks due to human error. Real-time visual inspection systems can help address these challenges, however, a key obstacle for real-world deployment of such systems is the scarcity of benchmark data from aerospace factory operations needed to validate and verify their performance. To enable this, we introduce the first known dataset tailored for evacuation slide inspection, comprising over 14,500 images captured under real and controlled conditions. This data aims to capture slide folding procedures of Embraer AFT evacuation slides, such that developed real-time systems can: (1) estimate the occluded position of the Pressure Relief Valve (PRV), (2) detect context-sensitive foreign objects such as packing clamps, and (3) calculate slide fold dimensions to prevent tolerance stacking errors. From this dataset, five benchmarks were constructed to evaluate performance across the three requirements. Baseline models were developed, including a PRV localization network using LSTMs, a variational autoencoder and object detection pipeline for packing clamp FOD, and a depth and reference-based slide fold measurement calculation method. When tested on our benchmarks, the depth-based measurement estimator showed precision and accuracy, the clamp FOD methodology showed high precision for images taken from specific cameras, however, the PRV position estimation remains a challenge that requires further research. Overall, our results set a foundation for the automation of visual inspection in slide packing and offer benchmarks for future research in this safety-critical inspection task.

In this thesis, we investigate how representative periods can be used as a temporal reduction technique for stochastic programming formulations of large-scale energy models. We specifically apply this to generation expansion planning. The focus is on cost-efficient decisions as well as on robust decisions, especially since the latter is why uncertainty is incorporated in most energy models. For this, we build on recent work that uses instead of traditional clustering methods like $k$-means or $k$-medoids, a method to obtain a convex or bounded conical hull over the periods. We prove that when inter-period constraints are ignored and a perfect hull is found, we can ensure that a feasible solution for the original model exists, obtained from the optimal decision variables of the reduced model. The proof is suitable for both the traditional way of using representative periods in stochastic programming, as for an approach that shares representative periods across scenarios instead of making them scenario-dependent. We show that a greedy implementation of the hull approach can outperform standard clustering methods in terms of costs in small case studies when a valid hull is found. The near-optimal costs can be obtained with a low number of representatives, largely due to the absence of loss of load due to the hull methods. However, in more complex cases, such as a European-scale model with high renewable shares, finding such a hull with the greedy algorithm proves difficult. To address this, we propose adding extreme periods either beforehand or afterwards, and applying a blended weights approach to reduce conservativeness while maintaining feasibility. Our results on small case studies demonstrate that extreme representatives can significantly reduce loss of load, although not always at lower cost when applied to the large European case study. These findings suggest that targeted selection of extremes, a right weighting approach and improved hull approximations offer a promising direction for enabling scalable, robust planning under uncertainty. 

The combination of increasing global food demand with increased food security risks associated with climate change amid a decreasing number of skilled growers necessitates innovative solutions in green- house horticulture. Autonomous growing offers a solution based on greenhouse climate forecasting and (optimal) control. However, current theoretical models developed for greenhouse climate forecast- ing face limitations due to the in-depth physics knowledge required for their use and their dependence on intricate system parameters that are difficult to estimate. Conversely, machine learning models struggle with generalisation to unseen conditions and adherence to physical laws, leading to unrealis- tic predictions in greenhouse environments. This study addresses these challenges by exploring the use of Physics-Informed Machine Learning (PIML) techniques to enhance greenhouse climate fore- casting. A simple differentiable theoretical model for simulating the greenhouse climate is proposed to serve as prior physical knowledge of the system. Subsequently, a novel PIML model is introduced in the form of Controlled Aphynity (CA), which integrates insights from neural controlled differential equations and Aphynity, and is the first such model that allows for the augmentation of incomplete prior knowledge of a dynamical system with data-driven machine learning models while being adaptable to changing dynamics due to forces acting on the system. Experimental results show that CA is capable of successfully augmenting incorrect physics under changing dynamics on the task of humidity deficit prediction in the greenhouse. Furthermore, three ensemble methods combining CA with traditional ma- chine learning techniques are explored and demonstrate promising synergies. A detailed case study evaluates CA and the best-performing ensemble approach on the task of humidity deficit prediction under realistic greenhouse scenarios over a complete crop cycle. Both CA and the ensemble methods exhibit superior adherence to physical laws, lower data requirements, and improved performance on outliers compared to conventional machine learning methods. This research contributes to advancing greenhouse climate modelling, underscoring PIML’s potential in optimising the greenhouse climate.F

Weather forecasting has always been a critical issue across various fields, and traditionally, weather predictions have relied on solving complex atmospheric equations using supercomputers. However, the rise of machine learning has introduced a more efficient method that utilizes historical data to forecast weather patterns. Nonetheless, there is a problem with auto-correlation, where values at a specific time point correlate with those at a previous time. Machine learning models assume independence between data points, which can lead to overfitting if the data is not split properly. This happens because training data might not be independent of test data and models can learn the noise rather than the underlying pattern in data. Also, auto-correlation can apply when having multiple locations with data (named spatial data), because nearby locations share similar characteristics.

This thesis investigates temporal and spatial data splitting strategies to address this challenge to determine which methods provide the most reliable performance estimates. Temporal splitting strategies involve dividing the data using time intervals, while spatial splitting strategies involve dividing based on the geographical location of weather stations. The goal is to identify strategies that minimize bias and variance in the error estimates of weather forecasting models. This will ensure less overfitting and robust and generalisable predictions across different climatic conditions and data regimes.

By systematically evaluating various data splitting strategies, this thesis aims to provide insights into the best approaches for preparing meteorological data for Machine Learning-based weather prediction models, thereby contributing to the advancement of more accurate and efficient weather forecasting techniques. Four different strategies for temporal data splitting were evaluated: random splitting, testing at the end of the dataset, testing in the middle of the dataset, and splitting the test year into multiple parts. Three different strategies for splitting spatial data were evaluated: random splitting, the use of one cluster of cities as the test data with the other clusters as training data, and lastly, choosing only the non-neighbouring clusters as training data. Some of these strategies, such as the random strategy, do not take into account auto-correlation when splitting the data.

Results indicate that the random strategy in both scenarios yields a relatively modest error rate. However, it is advisable to employ a strategy that organizes the test data into a cohesive block and positions it in the centre of the dataset time. Hence, this thesis contributes to the development of more refined weather prediction by shedding light on how data splitting strategies influence outcomes.

This thesis investigates the application of alternating maximisation for active wake control in wind farms, focusing on both numerical static yaw optimisation and multi-agent deep reinforcement learning for dynamic yaw control. As the size and number of offshore wind farms continue to grow, effectively mitigating wake effects ---~where the airflow behind a turbine is disturbed, reducing the efficiency of downstream turbines~--- becomes increasingly important to maximise power output and ensure economic profitability.

Current numerical strategies are either sub-optimal, as they do not leverage the shape of the optimisation surface well, or expensive to optimise due to the computational demands of high-fidelity simulations. Similarly, current reinforcement learning approaches are too sample inefficient on contemporary wind farms of 40+ turbines to learn high-quality policies and likely suffer from credit assignment problems, other agents performing exploration, and relative over-generalisation. Alternating maximisation emerges as a promising candidate to address these challenges by providing an efficient and potentially more effective optimisation method.

In static yaw optimisation, the research analysed the effects of wind direction, wind speed, turbine layout, and yaw misalignment on the optimisation surface. Key findings indicate that Nash equilibria are transient and significantly influenced by these factors.

The sampling-based approach to alternating maximisation outperformed other methods, proving robust against variations in initial yaw configurations and optimisation orders. This approach required fewer samples and appears suitable for practical applications.

For dynamic yaw control, alternating maximisation with policy gradients demonstrated greater sample efficiency compared to distributed policy gradient methods. However, convergence to sub-optimal policies was common due to limited exploration capabilities. An oscillating exploration strategy enabled effective exploration of yaw angles, leading to high-quality policies.

The study primarily focused on farm power output, neglecting structural loading and leading-edge erosion. Future research should integrate these factors and explore dynamic yaw control under time-varying wind conditions.

Overall, alternating maximisation shows potential in enhancing wind farm performance through efficient optimisation strategies. Future work should refine these methods and address identified limitations to maximise practical applicability.

The increasing demand for electricity has lead to demand for more efficient energy production. One promising option is wind power, which currently provides an estimated 7.8% of the world’s energy production. One of the problems with wind energy is that a small percentage of the energy is lost due to the wake effect. The wake of a wind turbine is an area of low wind speed and high turbulence which is caused by the spinning of the turbine. This wake effect can mitigated by active wake control, which is a process by which the wake from a turbine is redirected away from downwind turbines, by changing the yaw of the turbine head. Calculating a policy for doing this is computationally expensive to do using numerical optimisation. Therefore, multi agent reinforcement learning is proposed to learn a policy which performs active wake control.
The proposed approach makes use of the popular reinforcement learning algorithm REINFORCE, and extends it using a variety of methods. First, a simplified version of the problem is treated, wherein the wind direction is fixed. Then the problem is made more realistic by introducing changing wind directions. The first extension of REINFORCE that is treated is difference rewards, a reward shaping strategy which seeks to solve the credit assignment problem, thereby improving cooperation between turbines. The second method uses training regimes, which train different agents at different times to stabilise the environment as much as possible. Next, role-based reinforcement learning is used to conteract the complexity of the problem by allowing each agent to specialise for a certain role. Finally, since roles cannot be manually determined for larger farms, influence-based abstraction is used to enable agents to learn the roles themselves, by abstracting spacial information and presenting it to the agent as an observation.
The results demonstrate that multi agent reinforcement learning can be used to perform active wake control in wind farms. Furthermore, the extensions proposed are shown to improve learning, and lead to greater energy output. While multi agent reinforcement learning is shown to be a promising way to tackle active wake control in wind farms, research is needed to improve the stability of the learned policies.

Offline model-based reinforcement learning uses a model of the environment, learned from a static dataset of interactions, to guide policy generation. Sub-optimal planning decisions can be made when the agent explores states that are out-of-distribution, as the world model will have more uncertainty. This paper explores the use of pessimism, the tendency to avoid uncertain states, in the planning procedure. We evaluate Lower Confidence Bound, ensembles, and Monte Carlo dropout in the MinAtar breakout environment. Results indicate that ensemble methods yield the highest performance, with a significant performance gain over the baseline, while LCB also shows varying degrees of improvement. MC dropout is generally shown to not yield a performance improvement.

While model-free reinforcement learning (MFRL) approaches have been shown effective at solving a diverse range of environments, recent developments in model-based reinforcement learning (MBRL) have shown that it is possible to leverage its increased sample efficiency and generalisation abilities to solve highly complex tasks with fewer resources and environment interactions. The introduction of discrete latent states through categorical distributions allowed DreamerV2, a MBRL approach, to surpass the state-of-the-art MFRL Rainbow algorithm on the Arcade Learning Environment. Despite the successes of this approach, it is not yet understood why discretization improves performance. This paper investigates how the discretization of the latent space through categorical distribution affects planning performance in a deterministic environment. Further investigations are conducted on the model's generalization abilities and the impact of the latent space's shape on performance. By using a dataset of experiences instead of directly interacting with the environment, the models are trained in an offline setting. Results show that the discrete world model underperforms compared to a continuous latent space model while being significantly harder to train. Further investigations concluded that the number of categorical distributions has a high influence on performance and that in the considered setting the discrete world model can generalize better than the continuous baseline but it does so by sacrificing small gains in important metrics.

Traditionally, Recurrent Neural Networks (RNNs) are used to predict the sequential dynamics of the environment. With the advancement and breakthroughs of Transformer models, there has been demonstrated improvement in the performance & sample efficiency of Transformers as world models. The focus has been on partially-observable environments where their capabilities can be maximally utilised. In this paper, we sought to investigate the conditions under which transformers outperform RNNs given a fully observable environment where states obey the Markov property. This provides insight into transformers' generalisation and predictive capabilities. Specifically, our experiments explored the impact of model complexity and the size of the dataset. We observed that transformers did not outperform our baseline implementation when given up to 7000 episodes of trajectory data. It was also observed that having shorter sequence lengths had a negligible impact on the performance of the model, leading to our recommendation of avoiding using transformers in these fully observable environments.

This thesis project explores how V2X communication between electric vehicles (EV) and charging stations can be used to reduce en-route charging times. This is done using standardized V2X messages for EV charging, as well as proposing an extension to these messages to include data on the intentions of other vehicles. As individual vehicles can have significant effects on the total waiting time at EV charging stations, knowing the intentions of other vehicles will allow drivers to better avoid congested charging stations and achieve a lower total journey time. The performance of the system is evaluated using state-of-the-art traffic and communications simulators showing that using V2X can reduce journey times by 70%. Aside from demonstrating the performance using simulation the system has also been implemented on a real life test vehicle using functional V2X hardware to show that the system is viable for implementation.

The λ-calculus is a versatile tool both in mathematical logic and computer science. This thesis studies and expands upon Martin Hyland’s paper ‘Classical lambda calculus in modern dress’. It gives examples for the definitions and provides more detailed proofs, as well as one new proof for Hyland’s fundamental theorem of the λ-calculus. It complements these definitions and proofs with material of previous authors by which Hyland has been inspired. The thesis translates Hyland’s paper from set theory with classical logic to univalent foundations, and showcases where subtleties arise in such a translation. In particular, it discusses the different implementations of the Karoubi envelope in univalent foundations. Lastly, it discusses the accompanying formalization of parts of Hyland’s paper, with in particular a tactic that was developed for applying β-reduction and substitution to λ-terms.

Writing software that follows its specification is important for many applications. One approach to guarantee this is formal verification in a dependently-typed programming language. Formal verification in these dependently-typed languages is based on proof writing. Sadly, while proofs are easy to check for computers, writing proofs can be tedious for developers. One particular proof component that currently requires developers attention in many systems, is associative and commutative (AC) reasoning.
We contribute an extension of dependent type-systems to fully automate AC reasoning. This alleviates developers from this task and allows them to concentrate on other proof components. Our approach works by modifying the conversion checker and doesn't compromise soundness or completeness. Furthermore, our approach reuses existing type-checking components, making it easier to implement. We also implemented our theory as an extension of the Agda type-checker. This allowed us to use this implementation to experiment with some example programs.
This thesis can help language designers decide if they want automatic AC reasoning in their language. For language users it can serve as inspiration on how to use such a type-system and finally for researchers we have ideas for future work.

Deep convolutional neural networks (CNNs) have achieved current state-of-the-art in image denoising, but require large datasets for training. Their performance remains limited on smaller real-noise datasets. In this paper, we investigate robust deep learning denoising using transfer learning. We explore the impact of dataset sizes, CNN parameter updates, and noise distribution similarity. Our findings demonstrate that finetuning the decoder while fixing the encoder of an encoder-decoder network architecture avoids overfitting during transfer learning. Moreover, we introduce the concept of noise similarity in transfer learning, showing that reducing the similarity distance between pre-training and finetuning noise significantly enhances CNN performance in denoising. Our results are demonstrated on multiple datasets and various noise camera models. Finally, we validate the robustness and applicability of our approach on real-noise images. All our results and analyses hold and generalize across different datasets, highlighting our insights into the potential of transfer learning for image denoising.

When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current turbine's power output but increases the turbine's total power output. As the number of wind turbines increases, how complex it is to calculate the optimal steering increases exponentially. Reinforcement learning techniques have been a promising candidate to solve this problem. However, single-agent techniques are still very computationally expensive when the number of turbines is the same as an average wind farm. Therefore, this paper aims to see how the QMIX algorithm, a multi-agent reinforcement learning technique, can be efficiently applied to the problem of active wake control. QMIX will be compared to the FLORIS model and a single agent deep reinforcement learning technique TD3 to see if it achieves a higher average reward and converges faster. Finally, QMIX is tested on a larger wind farm to see if it achieves any results in a reasonable amount of time, showing that multi-agent reinforcement learning techniques are more suitable to the problem. This paper shows QMIX has the potential to outperform TD3; although FLORIS is better for smaller wind farms but more research has to be done in applying QMIX to the problem.

The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at utilizing a partial hierarchy, in which one of the appliances in a household is modelled separately from the rest of the house, to help improve household energy consumption forecasting accuracy. This is done in conjunction with Auto Regressive Moving Average (ARMA) based models. Three variants of ARMA based models will be looked at: Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), and Auto Regressive Integrated Moving Average with Exogenous variables (ARIMAX). These methods will then be compared to more baseline approaches such as a persistence method and a seasonal moving average. Our analysis has led us to conclude that the partial hierarchy model offers little to no benefit when applied in the field of household energy consumption forecasting when built upon ARMA based models. ARMA based models in general appeared to be poor performers when it came to household energy consumption forecasting.

Bottom up load forecasting, is a technique where energy consumption forecasts are made on lower spatial levels, after which the resulting forecasts are aggregated to form forecasts of higher spatial levels. With the current move to renewable energy sources and the importance of reducing the strain on an already congested electricity grid, accurately forecasting both the location and time of future energy consumption has become more important than ever. To this end, this paper analyses the impact of applying bottom up load forecasting to different spatial levels on the electricity grid, including appliance, household, community and city spatial levels. Energy consumption data for these spatial levels were gathered from the 15-minute Texas and California datasets from Pecan Street Dataport. The results obtained in this study, suggest that energy consumption volatility at lower spacial levels and forecast difficulty at higher spacial levels, play an important role in the performance of applying the bottom up load forecasting technique to energy consumption forecasting problems.

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy concerns associated with real-time household data collection through smart meters. The proposed approach leverages sequence-to-point learning, a deep learning method, to perform the disaggregation. A method that showed promising results in the domain of NILM. The results show good performance on the evaluation metrics, but they also show that the model does a poor job of capturing the load signature.

Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal information for predictions, ignoring the spatial information of neighbouring houses. However, as neighbouring houses are often under similar circumstances, such as weather and holiday conditions, predictions
could benefit from this information. Moreover, aggregating electric loads could further improve the accuracy of predictions, as the loads become less
stochastic when aggregated. This paper looks at Graph WaveNet, which can capture the hidden spatial dependencies of different houses without needing geographic information about the houses. The framework is compared against a baseline on different aggregation levels. The results show that the framework can benefit from aggregating the residential electrical loads and improves over the baseline on all aggregation levels.

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one-shot transfer learning. The research investigates the effectiveness of one-shot transfer learning in fine-tuning a CNN model to accurately detect the ON/OFF state of appliances in households not seen during the training phase of the CNN. The study utilizes the Pecan Street dataset for training and evaluation, which includes detailed energy consumption records from various locations in the United States. The results suggest that one-shot transfer learning could enhance the performance of the NILM algorithm, particularly when multiple data samples are used for fine-tuning. However, the effectiveness of one-shot transfer learning varies strongly depending on the number of samples and the characteristics of the target household.

TreeContainment is a well-known problem within phylogenetics, which asks whether a binary phylogenetic tree is embedded in a binary phylogenetic network. For this problem, Jones, Weller and van Iersel (2022) have created an algorithm that uses dynamic programming on tree-decompositions to achieve a running time that is exponential in the tree-width parameter instead of in the number of reticulations. However, due to the implicit formulations of two crucial steps in this algorithm, this running time cannot be achieved in practice without finding ways to generate the required structures explicitly. This paper provides two new sub-algorithms that can do that. To further improve the performance of the algorithm, I introduce a number of criteria and other methods that can be used to reduce the number of structures generated. Additionally, I describe a way to manipulate the nice tree-decompositions to create a more favorable order of bags for the dynamic programming. These sub-algorithms and improvements are used by two new algorithms TWITCH and PITCH, whose implementations are compared to a brute force algorithm and a new branching cherry-picking algorithm named BOTCH. The latter has an FPT running time that is exponential in the number of vertices that have only reticulations as children. The comparisons show that the implementations of the dynamic programming algorithms TWITCH and PITCH are slower in practice than the brute force algorithm, despite their numerous improvements. Of the four new implementations, BOTCH has the best test results and it is shown to be fast in practice.