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Classifying Human Manual Control Behavior Using LSTM Recurrent Neural Networks

Versteeg, Rogier (author), Pool, D.M. (author), Mulder, Max (author)

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...

journal article 2024

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Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions

van de Kamp, Lars (author), Reinders, Joey (author), Hunnekens, Bram (author), Oomen, T.A.E. (author), van de Wouw, Nathan (author)

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on...

journal article 2024

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Scale Learning in Scale-Equivariant Convolutional networks

Basting, Mark (author)

In real-life scenarios, there are many variations in sizes of objects of the same category and the objects are not always placed at a fixed distance from the camera. This results in objects taking up an arbitrary size of pixels in the image. Vanilla CNNs are by design only translation equivariant and thus have to learn separate filters for...

master thesis 2023

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BoundED: Neural boundary and edge detection in 3D point clouds via local neighborhood statistics

Bode, Lukas (author), Weinmann, M. (author), Klein, Reinhard (author)

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...

journal article 2023

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Interpretation of the Machine Learning Fault Classification Model Results Using Explainable Features

Georgiou, Thomas (author)

Electrical faults in the distribution network can lead to interruptions in the power supply of the customers. Therefore penalties are applied to the DSOs if they overcome the benchmark set based on all the DSOs reliability performance. Hence, the fast restoration of the power supply is crucial for the grid operator in order for the operational...

master thesis 2022

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Using pre-trained convolutional neural networks to predict maturity levels of strawberries

Buitenweg, Jurriaan (author)

To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity...

bachelor thesis 2022

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The impact of spatial resolution and satellite data type characteristics on Automated Damage Assessment using a Convolutional Neural Network

Verschoor, Fleur (author)

Satellite data, such as optical and Synthetic Aperture Radar imagery, can provide information about the location and level of destruction caused by natural hazards. This information is essential to optimise the rescue mission logistics by humanitarian aid organisations and save people in need. Currently, many Automatic Damage Assessment (ADA)...

master thesis 2022

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Automatic Identification of Fault Types in the Distribution Network using Supervised Learning

Ranganathan, Archana (author)

Electrical faults in the distribution system can lead to interruptions in customer power supply resulting in penalties that are borne by the distribution system operator. Accurate fault classification is an important step in locating the fault to achieve faster network restoration times. This reduces the operational costs of the system operator...

master thesis 2021

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A Spiking Neural Network classification architecture for spatial-temporal data processing

Arriëns, Roy (author)

A big catalyst of the AI revolution has been Artificial Neural Networks (ANN), abstract computation models based on the biological neural networks in the brain. However, they require an immense amount of computational resources and power to configure and when deployed often are dependent on cloud resources to function. This makes ANNs less...

master thesis 2021

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Classification prognostics approaches in aviation

Lourenço Baptista, M. (author), Henriques, Elsa M.P. (author), Prendinger, Helmut (author)

Traditionally, prognostics approaches to predictive maintenance have focused on estimating the remaining useful life of the equipment. However, from an industrial point of view, the goal is often not to predict the residual life but to determine the need for a maintenance action at a given time window. This approach allows us to frame the...

journal article 2021

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An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds

Nurunnabi, A. (author), Teferle, F. N. (author), Li, J. (author), Lindenbergh, R.C. (author), Hunegnaw, A. (author)

Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant...

journal article 2021

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Recursive Tensor Network Bayesian Learning of Large-Scale LS-SVMs

Lucassen, Max (author)

Least-squares support-vector-machines are a frequently used supervised learning method for nonlinear regression and classification. The method can be implemented by solving either its primal problem or dual problem. In the dual problem a linear system needs to be solved, yet for large-scale problems this can be impractical as current methods...

master thesis 2020

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Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan

Trommel, Kars (author)

During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour. To gain more understanding in the wind patterns occurring, a deep learning based approach is taken. The goal of this research is to find out if unsupervised learning methods...

master thesis 2020

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Evaluating and Improving Large-Scale Machine Learning Frameworks

Graur, Dan (author)

Given the increasing popularity of Machine Learning, and the ever increasing need to solve larger and more complex learning challenges, it is unsurprising that numerous distributed learning strategies have been brought forward in recent years, along with many large scale Machine Learning frameworks. It is however unclear how well these...

master thesis 2019

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Towards Understanding of Deep Learning in Profiled Side-Channel Analysis: Similarity of predictors measured and explained

van der Valk, Daan (author)

Side-channel attacks (SCA) aim to extract a secret cryptographic key from a device, based on unintended leakage. Profiled attacks are the most powerful SCAs, as they assume the attacker has a perfect copy of the target device under his control. In recent years, machine learning (ML) and deep learning (DL) techniques have became popular as...

master thesis 2019

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Exploring Gravitational Waves Recordings with Machine Learning Techniques

Diab Montero, Hamed (author)

The study of Gravitational Waves (GWs) opened a new window of possibilities to improve our understanding of the Universe. GWs provide suitable astronomical messengers for studying events that were not possible before through electromagnetic radiation, or in other cases complementing their observations. Ground-based interferometers like LIGO have...

master thesis 2019

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Exploiting synthetic images for real-world image recognition

Maton, Max (author)

Creating big datasets is often difficult or expensive which causes people to augment their dataset with rendered images. This often fails to significantly improve accuracy due to a difference in distribution between real and rendered datasets. This paper shows that the gap between synthetic and real-world image distributions can be closed by...

bachelor thesis 2018

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Side-Channel Attacks using Convolutional Neural Networks: A Study on the performance of Convolutional Neural Networks on side-channel data

Samiotis, Ioannis Petros (author)

Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this...

master thesis 2018

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Classification of large scale outdoor point clouds using convolutional neural networks

Hemmes, Tom (author)

There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three-dimensional data of our physical surroundings. Most of these...

master thesis 2018

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Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

Sorgedrager, Riemer (author)

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...

master thesis 2018