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Prediction of Aircraft Take-off Weight using Machine Learning

Gheorghe, Andrada (author)

Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML...

master thesis 2024

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Deep Learning for Aerodynamic Dataset Prediction of Combat Aircraft

Maes, Vincent (author)

The aerodynamic model of a combat aircraft is essential for its success and competitiveness compared to other combat aircraft. This thesis aims to research the most optimal machine learning model to create an aerodynamic model of a combat aircraft. The very large but still sparse, highly nonlinear dataset forms a challenge for using specific...

master thesis 2023

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Predicting Future Aircraft Spare Part Purchases by Using Previous Sales Records and Technical Maintenance Documentation: Master Thesis Aerospace Engineering

Wittenberg, Mels (author)

Efficient maintenance scheduling is a critical objective for aircraft carriers and Maintenance Repair and Overhaul Organizations (MROs) to minimize aircraft downtime. While predictive maintenance models have improved, accurately identifying materials, especially spare parts, for specific maintenance events remains challenging. This paper...

master thesis 2023

document

Multi Target XGBoost Cash Flow Prediction: An Efficient Machine Learning Algorithm For Future Liability Projections

van Schagen, Sebastiaan (author)

Insurers are required to have buffers to be able to meet financial obligations that result from their portfolios, which are determined using a cash flow model. The input of such a cash flow model consists among of things, of two mortality tables and the portfolio of an insurer. Mortality rates are simulated using the Lee-Carter model. These...

master thesis 2023

document

(Near) Real-time Data Analysis for Smart Sensor Shorts in Football

Liu, Qingxin (author)

Most injuries in football occur in the lower extremities due to high muscle stress. To prevent such injuries, the Dutch Football Association (KNVB) and the Delft University of Technology developed the Smart Sensor Shorts, an inertial sensor-based tracking system measuring the athlete’s lower body kinematics, to improve physical load estimates...

master thesis 2023

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Enhancing Baggage Handling Operations at Hub Airports through Sequential Forecasting and Resource Allocation: A Gradient Boosting Approach with MILP Optimisation

de Keijzer, Mitchell (author)

Baggage handling operations in airports have become substantially more challenging due to the surge in air transportation demand witnessed in the last three decades combined with an even higher pressure on the operations following the COVID-19 pandemic. The intricate nature of checked baggage requirements, impacting resource allocation and...

master thesis 2023

document

The road to intelligent asphalt concrete mixture design: A Data driven analysis of common asphalt concrete property prediction methods and a solution to the inverse problem

Hopman, Luuk (author)

Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the...

master thesis 2023

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PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

Zhu, Hangyu (author), Wang, R. (author), Jin, Yaochu (author), Liang, K. (author)

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However,...

journal article 2023

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Gradient boosting for extreme quantile regression

Velthoen, J.J. (author), Dombry, Clément (author), Cai, Juan Juan (author), Engelke, Sebastian (author)

Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the tail region are too scarce. Extreme value theory is used for extrapolation beyond the range of observed values and estimation of conditional extreme quantiles....

journal article 2023

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Machine learning study on performance prediction of asphalt mixtures under Dutch conditions

Mota Lontra, Bernardo (author)

The Netherlands is a northwestern European country. The location makes the Netherlands an important entry for trade to the European continent resulting in high traffic intensities [1]. The Netherlands has one of the densest road networks in the world, having over 139.000km of roadways [2, 3]. The understanding of the link between the functional...

master thesis 2022

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Statistical post processing of extreme weather forecasts

Velthoen, J.J. (author)

In this thesis we develop several statistical methods to estimate high conditional quantiles to use for statistical post-processing of weather forecasts. We propose methodologies that combine theory from extreme value statistics and machine learning algorithms in order to estimate high conditional quantiles in large covariate spaces. In...

doctoral thesis 2022

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Inferring the number of floors of building footprints in the Netherlands

Roy, Ellie (author)

Data on the number of floors is required for a variety of applications, ranging from energy demand estimation to flood response plans. Despite this, open data on the number of floors is currently not available at a nationwide level in the Netherlands. This means that it must be inferred from other available data. Automatic methods usually...

master thesis 2022

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Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles

Gan, Naifeng (author), Sun, S.Z. (author), Zhang, Zhaosheng (author), Xu, Shiqi (author), Liu, Peng (author), Qin, Z. (author)

The overdischarge can significantly degrade a lithium-ion (Li-ion) battery's lifetime. Therefore, it is important to detect the overdischarge and prevent severe damage of the Li-ion battery. Depending on the battery technology, there is a minimum voltage (cutoff voltage) that the battery is allowed to be discharged in common practice. Once the...

journal article 2022

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Automatic change detection of digital maps using aerial images and point clouds

Dahle, F. (author), Arroyo Ohori, G.A.K. (author), Agugiaro, G. (author), Briels, Sven (author)

In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change...

journal article 2021

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Wave overtopping predictions using an advanced machine learning technique

den Bieman, Joost P. (author), van Gent, M.R.A. (author), van den Boogaard, Henk F.P. (author)

Coastal structures are often designed to a maximum allowable wave overtopping discharge, hence accurate prediction of the amount of wave overtopping is an important issue. Both empirical formulae and neural networks are among the commonly used prediction tools. In this work, a new model for the prediction of mean wave overtopping discharge is...

journal article 2021

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Predictive maintenance for utility scale solar parks: A machine learning approach towards early fault detection for PV inverters

Manmohan Sane, Omkar (author)

The growing demand and improvements in manufacturing capabilities, supported by government subsidies, has allowed the increase in the installed capacity of utility scale solar parks. Due to the remoteness in their location, the costs associated with dispatching personnel for maintenance is extremely high. A major contribution towards unscheduled...

master thesis 2020

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Proxying Bond Credit Spreads with Machine Learning

Bacci di Capaci, G. (author)

The bond market is affected by the shortage of liquidity problem, which means that many bonds are not frequently traded. This implies that market data for these bonds are missing. This lack of data represent a problem for financial risk measures such as Value at Risk (VaR). This research provides the framework for the construction of a proxy...

master thesis 2020

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Predicting 4D trajectories of aircraft using neural networks and gradient boosting machines: A data-driven aircraft trajectory prediction study

Dek, Casper (author)

Data-driven trajectory prediction is one of the key pillars of the future ATM system. Recent research focuses on using novel data sources and machine learning algorithms to improve the performance of 4D trajectory prediction, enabling safer and more efficient routing of aircraft. In this paper a framework for data sourcing and preparation for...

master thesis 2020

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To order or not to order: Predicting customer grocery shopping behaviour using multi-label classification techniques

Verbruggen, Roderik (author)

Research and Objective: In the recent years the online grocery sector experienced an enormous uplift and evolved to a highly competitive business sector. Within this demanding environment, the need for strategic information has become extremely important, as it greatly enhances decision-making processes and the optimisation of the supply chain....

master thesis 2020

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Forecasting river discharge using machine learning methods: with application to the Geul and Rur river

van den Munckhof, Gijs (author)

The objective of this study is find out whether maximum daily discharge of the Geul and Rur catchments can be forecast using machine learning (ML) methods, and if so, to what extent. In addition, these ML models are compared to a conceptual model to see which performs better. A second objective is to test whether soil moisture content (SMC) and...

master thesis 2020