document

Improving model-based control of a soft robot via gaussian process regression

Tavio Y Cabrera, Emilio (author)

Soft robots have the potential to accelerate robotiza- tion in areas that are complex and impractical for hard robots. The use of soft materials results in a safe and flexible design that is unattainable for hard robots. However, this attribute results in the need for new control approaches and strategies. Hybrid controllers are a relative...

master thesis 2023

document

Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data

Marinescu, M. (author), Olivares, Alberto (author), Staffetti, Ernesto (author), Sun, Junzi (author)

This paper addresses the problem of spatiotemporal wind velocity field estimation for air traffic management applications. Using data obtained from aircraft, the eastward and northward components of the wind velocity field inside a specific air space are calculated as functions of time. Both short-term wind velocity field forecasting and wind...

journal article 2023

document

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

Veen, Thijs (author)

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...

master thesis 2022

document

Predicting noise attenuation level in the earplugs using Gaussian Process Regression

Rakesh Arya, Stephy Annie Curie (author)

The earplug development followed by ALPINE, a hearing protection company is a trial and error method. This method leads to high material wastage. It is also a time-consuming and expensive development process. So, the objective of this thesis is to ease the error earplug development process by implementing a prediction model. The research is...

master thesis 2022

document

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

Martinez Colan, Andres (author)

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...

master thesis 2022

document

Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks

Heng, J. (author), Zheng, Kaifeng (author), Feng, Xiaoyang (author), Veljkovic, M. (author), Zhou, Zhixiang (author)

This study integrates the fatigue test and numerical prediction to derive a comprehensive probability-stress-life (P-S-N) curve for rib-to-deck (RD) welded joints in orthotropic steel decks. Fatigue tests of RD joints are conducted to measure fatigue strength and crack growth data. Based on the test, a probabilistic fatigue crack growth (PFCG...

journal article 2022

document

GPR-assisted evaluation of probabilistic fatigue crack growth in rib-to-deck joints in orthotropic steel decks considering mixed failure models

Heng, J. (author), Zhou, Zhixiang (author), Zou, Yang (author), Kaewunruen, Sakdirat (author)

Rib-to-deck (RD) welded joints in orthotropic steel decks (OSDs) of bridges demonstrates two major fatigue failure models, including the toe-to-deck (TTD) cracking and root-to-deck (RTD) cracking. Generally, the sole failure model is employed in the fatigue assessment of RD joints, causing a hot dispute on the dominant failure model. In this...

journal article 2022

document

On the Estimation of Vector Wind Profiles Using Aircraft-Derived Data and Gaussian Process Regression

Marinescu, Marius (author), Olivares, Alberto (author), Staffetti, Ernesto (author), Sun, Junzi (author)

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on the Adolfo Suarez Madrid-Barajas airport. A continuous nowcast of the wind profile is...

journal article 2022

document

Contour Method with Uncertainty Quantification: A Robust and Optimised Framework via Gaussian Process Regression

Tognan, A. (author), Laurenti, L. (author), Salvati, E. (author)

Background: Over the past 20 years, the Contour Method (CM) has been extensively implemented to evaluate residual stress at the macro scale, especially in products where material processing is involved. Despite this, insufficient attention has been devoted to addressing the problems of input data filtering and residual stress uncertainties...

journal article 2022

document

Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks: With Application to a Printer with Friction

Van Meer, Max (author), Poot, Maurice (author), Portegies, Jim (author), Oomen, T.A.E. (author)

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unknown structure using input-output data, which can be used to generate feedforward signals for a-priori unknown tasks. To this...

journal article 2022

document

Forecasting of electric vehicle load profiles: Predicting the uncontrolled load stemming from a large group of electric vehicles as input for trading on energy markets

van der Leest, Max (author)

Exploiting electric vehicles for maintaining a stable electricity grid is a promising opportunity, which is recognized by both the academic and industrial world. Although the concept is relatively simple, the operating space containing many different stakeholders, rules and legislation is not. There are various practical issues that complicate...

master thesis 2021

document

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

Molenaar, Tobias (author)

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...

master thesis 2021

document

Uncertainty Modelling in Aircraft Trajectory Predictions

Graas, Rik (author)

Several initiatives are being developed to shift the current paradigm in Air Traffic Management (ATM) from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models that predict the 4D-trajectory of an aircraft. Previous studies primarily...

master thesis 2021

document

Predicting movable bridge deck expansion: A machine learning and asset management perspective

Khedekar, Tejas (author)

Movable bridge decks experience critical expansion in summer, leading to uncertainty and unpredictability in its availability doe to improper docking and safety hazard. If the bridges are not cooled soon, the inertia of expansion stays, causing prolongation of availability problems. Structural health monitoring of such bridges with a predictive...

master thesis 2021

document

Wind Profile Estimation from Aircraft Derived Data Using Kalman Filters and Gaussian Process Regression

Sun, Junzi (author), Marinescu, M. (author), Olivares, Alberto (author), Staffetti, Ernesto (author)

Accurate wind information is crucial in air traffic management, for instance, to improve trajectory predictability and precision in controlled time of arrival. Nowadays, air traffic management relies on Numerical Weather Prediction, which usually has a low resolution and low update rate. A potential approach for improving the resolution and...

conference paper 2021

document

Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression

Graas, R. (author), Sun, Junzi (author), Hoekstra, J.M. (author)

Several initiatives are developed to shift the current paradigm in Air Traffic Management from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models for the 4D flight trajectories. A variety of performance-based and data-driven approaches...

conference paper 2021

document

Fast and robust identification of railway track stiffness from simple field measurement

Shen, C. (author), Dollevoet, R.P.B.J. (author), Li, Z. (author)

We propose to combine a physics-based finite element (FE) track model and a data-driven Gaussian process regression (GPR) model to directly infer railpad and ballast stiffness from measured frequency response functions (FRF) by field hammer tests. Conventionally, only the rail resonance and full track resonance are used as the FRF features to...

journal article 2021

document

A Gravity Assist Mapping Based on Gaussian Process Regression

Liu, Y. (author), Noomen, R. (author), Visser, P.N.A.M. (author)

We develop a Gravity Assist Mapping to quantify the effects of a flyby in a two-dimensional circular restricted three-body situation based on Gaussian Process Regression (GPR). This work is inspired by the Keplerian Map and Flyby Map. The flyby is allowed to occur anywhere above 300 km altitude at the Earth in the system of Sun-(Earth+Moon)...

journal article 2021

document

Strategy synthesis for partially-known switched stochastic systems

Jackson, John (author), Laurenti, L. (author), Frew, Eric (author), Lahijanian, Morteza (author)

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via...

conference paper 2021

document

A gravity assist mapping for the circular restricted three-body problem using Gaussian processes

Liu, Y. (author), Noomen, R. (author), Visser, P.N.A.M. (author)

Inspired by the Keplerian Map and the Flyby Map, a Gravity Assist Mapping using Gaussian Process Regression for the fully spatial Circular Restricted Three-Body Problem is developed. A mapping function for quantifying the flyby effects over one orbital period is defined. The Gaussian Process Regression model is established by proper mean and...

journal article 2021