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A Human-In-the-Loop System for Interpreting Image Recognition Models

Ziengs, Bart (author)

Interpretability of ML models and image recognition models specifaclly, is a increasing problem. In this thesis, the design and implementation of Brickroutine: a system that used a trained model, is presented. Using human annotations, semantic interpretations are given to image classification problems. By giving an iterative approach in terms of...

master thesis 2022

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Using Explainable Artificial Intelligence to Improve Transparency of Reinforcement Learning for Online Adaptive Flight Control: Breaking Open the Black Box

van Zijl, Job (author)

Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...

master thesis 2022

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Preventive-corrective contingency control of distribution power grids using convolutional neural networks

Treurniet, Wiljo (author)

To achieve the goals on greenhouse gas emissions, the energy supply and demand is in transition. Distribution power grids therefore are increasingly reaching their capacity limits due to electrification and the vast increase of distributed energy resource (DER) connection requests with large peak power output. Increasing physical grid capacity...

master thesis 2022

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A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Kim, J. (author), Jonoski, Andreja (author), Solomatine, D.P. (author)

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on...

journal article 2022

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Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs

Overwater, R.W.J. (author), Babaie, M. (author), Sebastiano, F. (author)

Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their...

journal article 2022

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Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

Bij de Vaate, I. (author), Martin, Ericka (author), Slobbe, D.C. (author), Naeije, M.C. (author), Verlaan, M. (author)

In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-,...

journal article 2022

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Health monitoring: a machine learning approach for anomaly detection in multi-sensor networks

Hajee, Bram (author), Wisse, Kees (author), Mohajerin Esfahani, P. (author)

Multi-sensor networks are becoming more and more popular in order to assess the post-occupancy performance of smart buildings, since they enable continuous monitoring with a high spatial resolution of the occupancy, thermal comfort and indoor air quality. An urgent, but poorly attended topic in this field is the automated detection of sensor...

conference paper 2022

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Disadvantaged communities have lower access to urban infrastructure

Nicoletti, L.A. (author), Sirenko, M. (author), Verma, T. (author)

Disparity in spatial accessibility is strongly associated with growing inequalities among urban communities. Since improving levels of accessibility for certain communities can provide them with upward social mobility and address social exclusion and inequalities in cities, it is important to understand the nature and distribution of spatial...

journal article 2022

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Learning to Feel Textures: Predicting Perceptual Similarities From Unconstrained Finger-Surface Interactions

Richardson, Benjamin A. (author), Vardar, Y. (author), Wallraven, Christian (author), Kuchenbecker, Katherine J. (author)

Whenever we touch a surface with our fingers, we perceive distinct tactile properties that are based on the underlying dynamics of the interaction. However, little is known about how the brain aggregates the sensory information from these dynamics to form abstract representations of textures. Earlier studies in surface perception all used...

journal article 2022

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Inferring the number of floors for residential buildings

Roy, E.I. (author), Pronk, Maarten (author), Agugiaro, G. (author), Ledoux, H. (author)

Data on the number of floors is required for several applications, for instance, energy demand estimation, population estimation, and flood response plans. Despite this, open data on the number of floors is very rare, even when a 3D city model is available. In practice, it is most often inferred with a geometric method: elevation data is used...

journal article 2022

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Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model

Janssens, M. (author), Hulshoff, S.J. (author)

Data-driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational-Multiscale (VMS) Artificial Neural...

journal article 2022

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A Generalized Transformer-Based Pulse Detection Algorithm

Dematties, Dario (author), Wen, C. (author), Zhang, Shi Li (author)

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse...

journal article 2022

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Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

Barber, Sarah (author), Lima, Luiz Andre Moyses (author), Sakagami, Yoshiaki (author), Quick, Julian (author), Latiffianti, Effi (author), Liu, Y. (author), Ferrari, Riccardo M.G. (author), Letzgus, Simon (author), Zhang, Xujie (author), Hammer, Florian (author)

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for...

journal article 2022

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Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Karkalousos, D. (author), Noteboom, S. (author), Hulst, H. E. (author), Vos, F.M. (author), Caan, M.W.A. (author)

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity,...

journal article 2022

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Graph machine learning for design of high-octane fuels

Rittig, J. (author), Ritzert, Martin (author), Schweidtmann, A.M. (author), Winkler, Stefanie (author), Weber, J.M. (author), Morsch, Philipp (author), Heufer, Karl Alexander (author), Grohe, Martin (author), Mitsos, Alexander (author), Dahmen, Manuel (author)

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by...

journal article 2022

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A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information

Prakoso, Emanuel Febrianto (author), Maknoon, M.Y. (author), Pel, A.J. (author), Tavasszy, Lorant (author), Vanga, R. (author)

Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under...

journal article 2022

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Massive Uncoordinated Multiple Access for Beyond 5G

Mohammadkarimi, M. (author), Dobre, Octavia A. (author), Win, Moe Z. (author)

Existing wireless communication systems have been mainly designed to provide substantial gain in terms of data rates. However, 5G and Beyond will depart from this scheme, with the objective not only to provide services with higher data rates. One of the main goals is to support massive machine-type communications (mMTC) in the Internet-of-Things...

journal article 2022

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An Approach for Sleep Apnea Detection based on Radar Spectrogram Envelopes

Han, Y. (author), Yarovoy, Alexander (author), Fioranelli, F. (author)

This research aims to develop a contactless, radar-based sleep apnea detection method. A novel identification approach for this is proposed, based on the envelope of UWB radar spectrograms and machine learning. The envelope of the spectrogram is extracted by an image-based method, followed by signal smoothing via variational mode decomposition ...

conference paper 2022

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Estimating PV Curtailed Power as a Voltage Support Service using Data-Driven Approaches

Verhoeven, Gijs (author), Vergara Barrios, P.P. (author), Salazar Duque, Edgar Mauricio (author), Kok, Koen (author)

To guarantee a successful deployment of a droop-based control strategy to mitigate overvoltage problems caused by solar photovoltaic (PV) generation, Distribution System Operators (DSOs) will need to estimate the amount of active power curtailed by the PV inverters for billing purposes. This paper provides a structural elaboration on the...

conference paper 2022

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Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario

Verduzco, Alejandro (author), Páramo Balsa, Paula (author), Gonzalez-Longatt, Francisco (author), Andrade, Manuel A. (author), Acosta Montalvo, Martha Nohemi (author), Rueda, José L. (author), Palensky, P. (author)

This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can...

conference paper 2022