Malleable Kernel Interpolation for Scalable Structured Gaussian Process

Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor...

Magnetic field SLAM: using an inertial human motion suit and reduced rank Gaussian process regression

Indoor localisation is a growing field of interest in recent studies. While GPS (global positioning system) is a standard for outdoor localisation, no such solution exists for indoor applications. The literature provides several methods to obtain the location of indoor systems, often using optical sensors. A small number of recent studies use...

Bayesian system identification of civil engineering structures using high resolution optic fibre measurements and surrogate modelling

Bayesian system identification has been extensively adopted in Structural Health Monitoring as a way to probabilistically infer unobservable parameters of the physical model of a structure using measurement data. Combining the Bayesian approach with distributed optic fibre sensors can potentially improve the accuracy and reduce the uncertainty...

Remote Sensing: Deriving water quality indicators from high-resolution satellite data using spatio-temporal statistics

The derivation of water quality indicators is of importance, especially in coastal areas, as most of the economic activities are located here. However, the availability of high-spatial-resolution water quality information in coastal zones is limited. Nowadays, high-resolution satellite data is becoming available and can fill in this knowledge...

Data­-driven techniques for finding governing equations of noisy nonlinear dynamical systems

The advent of machine learning and the availability of big data brought a novel approach for researchers to discover fundamental laws of motion. Computers allow to quickly find underlying physical laws from experimental data, without having in-depth knowledge of the system. Applications are widespread among numerous fields such as physics,...

Magnetic interpolation using Gaussian processes

In this thesis a model for interpolation of magnetic fields is constructed using Gaussian processes. This model takes the curl- and divergence-free properties of magnetic fields into account. The Gaussian process regression, or kriging, is tested on both simulated and real data. It is also attempted to reconstruct the magnetization of objects...

Sparse Gaussian Processes in the Longstaff-Schwartz algorithm

In financial applications it is often necessary to determine conditional expectations in Monte Carlo type of simulations. The industry standard at the moment relies on linear regression, which is characterized by the inconvenient problem of having to choose the type and number of basis functions used to build the model, task which is made harder...

Data-Driven Techniques for Printer Prognosis and Performance Improvement: Design and Critical Comparison

It is of key importance for modern printing systems to maintain high standards of efficiency, reliability and print quality. In this regard, the scope of this work is to investigate the applicability of data analysis and machine learning techniques to improve the performances of industrial printers manufactured at Océ Technologies. Two critical...

Gaussian Process Models for Preliminary Low-Thrust Trajectory Optimization

Low-thrust trajectories can benefit the search for propellant-optimal trajectories, but increases in modeling complexity and computational load remain a challenge for efficient mission design and optimization. An approach for developing models utilizing Gaussian Process (GP) regression and classification is proposed to perform computationally...

Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression

Industrial robots can be found in automotive, food, chemical, and electronics industries. These robots are often caged and are secluded from human beings. A recent trend in a subclass of industrial robots named collaborative robots allows the humans to interact with the robots safely. The word “safety” mentioned above is of supreme importance....