Stereo Pointclouds for Safety Monitoring of Port Environments

Abstract—The MOSES project develops an autonomous vessel equipped with an autonomous crane to optimise the supply chain of shortsea shipping. This study focusses on monitoring the safety of the port environment based on stereo camera data generated by sensors attached to the crane at 15m altitude, oriented 45° downward. The objective is to...

A study of how outlier detectors can accurately authenticate multiple persons using the heart rate from consumer-grade wearables

The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research...

A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...

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

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...

An Analysis of the Computational Efficiency Gains from the Incorporation of Importance Sampling in Determining Optimal Sound Source Location

A novel approach for determining the optimal location of a sound source within an acoustic environment is proposed. This approach involves the application of Importance Sampling to improve the efficiency of the existing method of acoustic ray-tracing for finding the frequency response at various listening locations. The results of this study do...

Linear simulation of large scale regional electricity distribution networks and its applications: Towards a controllable electricity network

The volatility of renewable energy sources pose a significant challenge for Distribution Network Operators (DNOs) as it makes planning and maintaining a reliable electricity grid more complex. An essential tool in dealing with the uncertain behavior of renewable energy resources is the load flow simulation, i.e., the standard electricity network...

An incremental clustering method for anomaly detection in flight data

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot...

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles from Smart Meter Data

The development of thorough probability models for highly volatile load profiles based on smart meter data is crucial to obtain accurate results when developing grid planning and operational frameworks. This paper proposes a new top-down modeling approach for residential load profiles (RLPs) based on multivariate elliptical copulas that can...

Utilizing volunteered geographical information for the benefit of city planners and urban science: Case of Rotterdam

One of the main challenges of the recently popular data science field is establishing a common ground of understanding between technical methods and domain knowledge. Making smart and effective use of data is just as important for public organizations as it is for private organizations as our cities and their problems get more complex with...

Real-time reference trajectory adaptation for haptic shared control during teleoperation

Haptic Shared Control (HSC) can improve operator performance during teleoperation. HSC uses assistive forces on the master device to guide the operator's control input towards a reference trajectory. However, the reference trajectory might be incorrect due to inaccuracies in the sensed or modeled environment and thus not match the intended...

Model-based rare category detection for temporal data

Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which instances are assumed to be independent. In this thesis we propose an algorithm that is designed for temporal data. Specifically, we are interested in data with temporal...

Reservoir Lithology Determination by Hidden Markov Random Fields Based on a Gaussian Mixture Model

In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the...

Partial discreteness: A novel prior for magnetic resonance image reconstruction

An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness (PD), where a small number of regions in the image...

Learning Control Policies for Tooth Extraction: An Application of Programming by Demonstration

: Tooth extraction is one of the most common procedures in oral surgery. However, it is complicated as well as not fully understood. Different teeth have different structures and strengths. Even teeth of the same type differ depending on ages, genders, races, and other reasons affecting its integrity. During the procedure, the properties of the...

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of...

Implementation and evaluation of a binary mixture model and Fe-analysis on water saturated soil

A binary mixture model for soils has been formulated and analyzed. In the model one of the constituents is soil and the other constituent an incompressible liquid. The effects of water are included by formulating a mixture model is formulated as an extension of an existing non-associated elasto-plastic material model for granular materials. This...