Data-Driven Optimal Control: An Inverse Optimization Model and Algorithm

In Inverse Optimization (IO), it is hypothesized that experts, when making decisions, implicitly engage in solving an optimization problem. If we can reconstruct this optimization problem using the decision data of the expert, then the behavior of the expert can be emulated. In this thesis, a novel inverse optimization model, Kernel Inverse...

Image Reconstruction for Dynamic Multi-Pinhole SPECT-imaging using Kernelised Expectation Maximisation

<i><b>Background </b></i>Dynamic SPECT scanning provides a non-invasive way to image the time-dependent distribution of radio-labelled tracers inside living tissue. Beside human medicine, dynamic SPECT also finds its applications in pre-clinical research on small animals. In pre-clinical research, multi-pinhole collimators are used to enable...

Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging

Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the...

Spatial effect removal from field data by virtual replanting

In agricultural studies it is often important to predict the performance of genetically different plants. To make sure predictions are done well, it is necessary to make sure they are not influenced by effects of the field on which they are planted. These field effects or spatial effects are in practice often quite complicated and can be due to...

Diagnostics of the Theoretical Underpinning of the Socio-hydrological Model in "Water Effciency in Sustainable Cotton-based Production Systems" Project in Maharashtra, India: Evaluation of model performance and the quantication of errors using Monte Carlo sampling, GLUE, linear regressions, linear PCA, and kernel PCA

This research is part of the project "Water Efficiency in Sustainable Cotton-based Production Systems” between Solidaridad Asia and TU Delft. The project aims to increase the livelihood of smallholder farmers in the Maharashtra, India through. A socio-hydrological (SH) model is used extensively in this research as an evaluation tool. However,...

Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train accurate (supervised) predictive models. The main idea behind active learning is that models can perform better with less labeled data, if the model may choose the data from which it learns. Active...