document

Improving detection of river surface flow using p yOpenRiverCam and AI augmentation

Helmich, Max (author)

This thesis investigates the efficacy of artificial intelligence (AI) models, particularly convolutional neural networks (CNNs) and U Net architectures, in reconstructing datasets with missing velocity data in river flow analysis. Optical flow and Particle Image Velocimetry (PIV) techniques have emerged as valuable tools for analyzing river flow...

master thesis 2024

document

Phase Recognition for Pulmonary Orientation Detection: Towards Automated Intraoperative Imaging Guidance

Doornbos, Marie-Claire (author)

Objective: This study introduces a novel deep-learning-based orientation recognition approach for detecting intraoperative lung orientation during robot-assisted anatomical resections, including lobectomy and segmentectomy. This method can potentially aid in anatomical structure identification, facilitate training and education, improve...

master thesis 2024

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The OPS-SAT case: A data-centric competition for onboard satellite image classification

Meoni, G. (author), Märtens, Marcus (author), Derksen, Dawa (author), See, Kenneth (author), Lightheart, Toby (author), Sécher, Anthony (author), Martin, Arnaud (author), Rijlaarsdam, David (author), Fanizza, Vincenzo (author), Izzo, Dario (author)

While novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly lagging. A major hindrance in this regard is the need for high-quality annotated data for training such systems, which makes the...

journal article 2024

document

Georeferencing Historic Map Series: An Automated Approach

Tsipa, Eirini (author), Iliopoulos, Giorgos (author), Post, Oliver (author), Aalders, Rianne (author)

The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the...

student report 2023

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Data Model for Computer Vision Explainability, Fairness, and Robustness

Karnani, Simran (author)

In recent years, there has been a growing interest among researchers in the explainability, fairness, and robustness of Computer Vision models. While studies have explored the usability of these models for end users, limited research has delved into the challenges and requirements faced by researchers investigating these requirements. This study...

master thesis 2023

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Towards Coherent and Effective Self-Management during the Post-Diagnosis Stage: Vomo: An application empowering individuals with Pulmonary Fibrosis

Zhang, Ruixuan (author)

Pulmonary Fibrosis (PF) is a rare and incurable interstitial lung disease, which causes lung scarring, leading to an irreversible decline in patients’ lung function and breathing difficulties. In Europe, Pulmonary fibrosis (PF) affects over 400,000 people, and it claims the lives of about 100,000 people each year (EU-IPFF, 2023). In light of the...

master thesis 2023

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Designing for explanation-driven trust in Chatbots

Zhang, Zhengquan (author)

Trust plays an important role in the implementation of chatbot technology. This study was also focusing on user trust in chatbots, particularly focusing on the role of response delay and explanation-driven driven subjective transparency. This research includes a pretest and a main test. In the pretest, we selected one explanation that was...

master thesis 2023

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Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data

Moradi, M. (author), Chiachío, Juan (author), Zarouchas, D. (author)

Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This...

conference paper 2023

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Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges

Ye, Jun (author), Li, Chengxi (author), Wen, Weisong (author), Zhou, Ruiping (author), Reppa, V. (author)

Autonomous surface ships have become increasingly interesting for commercial maritime sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model-based. The development of artificial intelligence (AI) has prompted new challenges in the maritime industry. A detailed literature study and examination of DL...

review 2023

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Less Machine (=) More Vision: Approaches towards Practical and Efficient Machine Vision with Applications in Face Analysis

Gudi, A.A. (author)

Machines that interact with humans can do so better if they can also visually understand us, but they have limited resources to do so. The main topic of this dissertation is contrasting the use of resources by machine vision systems against the accuracy obtained by them. This thesis focuses on reducing the need for data, memory, and computation...

doctoral thesis 2022

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Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems

Aliyu, Ibrahim (author), van Engelenburg, S.H. (author), Mu'azu, Muhammed Bashir (author), Kim, Jinsul (author), Lim, Chang Gyoon (author)

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be...

journal article 2022

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Bio-Remote Sensing in Predicting Infection in Neonates With Thermal Imaging and Machine Learning

Rassels, K. (author), French, P.J. (author)

Premature birth complications have different causes and vary in different parts of the world with sepsis as one of the leading causes of these complications. The body releases anti-inflammatory substances when an infection is detected and this, in turn, could damage healthy organs, especially when they are not fully developed. Preterm babies...

conference paper 2022

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Interpretable neural network with limited weights for constructing simple and explainable HI using SHM data

Moradi, M. (author), Komninos, P. (author), Benedictus, R. (author), Zarouchas, D. (author)

Recently, companies all over the world have been focusing on the improvement of autonomous health management systems in order to enhance performance and reduce downtime costs. To achieve this, the remaining useful life predictions have been given remarkable attention. These predictions depend on the proper designing process and the quality of...

conference paper 2022

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Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market

Bellizio, Federica (author), Xu, Wangkun (author), Qiu, Dawei (author), Ye, Yujian (author), Papadaskalopoulos, Dimitrios (author), Cremer, Jochen (author), Teng, Fei (author), Strbac, Goran (author)

Digitalization is one of the key drivers for energy system transformation. The advances in communication technologies and measurement devices render available a large amount of operational data and enable the centralization of such data storage and processing. The greater access to data opens up new opportunities for a more efficient and...

journal article 2022

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Sensing in the wild: A DCODE DRS Lab exploring a more-than-human approach to distributed urban sensing

Turtle, G.L. (author), Guerrero Millan, Carlos (author), Özçetin, Seda (author), Patil, M.S. (author), Bendor, R. (author)

The Sensing in the Wild Lab is a speculative experiment in designing a de- centralised urban sensing system from a more-than-human perspective. It is part of DCODE, an H2020-ITN project that explores the future of designing with AI. During the Lab participants assume different identities – roleplaying as children but also as moss, as municipal...

conference paper 2022

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Self-Organizing Multi-Agent Systems

van Leeuwen, C.J. (author)

In this thesis I research the ability of groups of agents to organize their collective behavior, without any human intervention. Using a framework for gathering information of the behavior, analyzing the performance, and updating the behavior, the agents can adapt to changing environments or user requirements. In my thesis I use different...

doctoral thesis 2021

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Ethics by design and research ethics for artificial intelligence

Jansen, Philip (author), Henschke, A.H. (author), Erden, Y. J. (author), Marchiori, S. (author), Brey, Philip (author), Hoefsloot, Marit (author)

report 2021

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Evaluating XAI: A comparison of rule-based and example-based explanations

van der Waa, J.S. (author), Nieuwburg, Elisabeth (author), Cremers, Anita (author), Neerincx, M.A. (author)

Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on users' experience of, and behavior in...

journal article 2021

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Orchestrating Game Generation

Liapis, Antonios (author), Yannakakis, Georgio N. (author), Nelson, Mark J. (author), Preuss, Mike (author), Bidarra, Rafael (author)

—The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an...

journal article 2019