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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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Modelling of kinematic higher pairs by lower pairs

Meijaard, J.P. (author)

Kinematic joints are classified in lower pairs and higher pairs. Most multibody modelling techniques focus on lower pairs, because a complete classification in six types is available. Higher pairs are more diverse. In this article, higher pairs that can be exactly modelled by lower pairs are investigated. A complete classification of higher...

journal article 2024

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Dimensions of data sparseness and their effect on supply chain visibility

van Schilt, Isabelle M. (author), Kwakkel, J.H. (author), Mense, Jelte P. (author), Verbraeck, A. (author)

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of...

journal article 2024

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Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Freites, Alfredo (author), Corbett, P. W.M. (author), Rongier, G. (author), Geiger, S. (author)

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is...

journal article 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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Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

Castillo, J.M. (author), Arif, M. (author), Starmans, M.P.A. (author), Niessen, W.J. (author), Bangma, C.H. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...

journal article 2022

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An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Wesselius, F.J. (author), van Schie, M.S. (author), de Groot, N.M.S. (author), Hendriks, R.C. (author)

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and...

journal article 2022

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Cluster-based identification algorithm for in-line recycled concrete aggregates characterization using Laser-Induced Breakdown Spectroscopy (LIBS)

Chang, C. (author), Di Maio, F. (author), Rem, P.C. (author), Gebremariam, A.T. (author), Mehari, Fanuel (author), Xia, Han (author)

To upcycle End-of-Life (EoL) concrete from demolished buildings, it is essential to efficiently identify the different materials that may contaminate it. The precise identification and classification of materials and contaminants are vital processes for in-line quality inspection of recycled concrete aggregates transported on a conveyor belt....

journal article 2022

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Probabilistic vegetation transitions in dunes by combining spectral and lidar data

Kathmann, H.S. (author), van Natijne, A.L. (author), Lindenbergh, R.C. (author)

Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in...

journal article 2022

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Iterative bias estimation for an ultra-wideband localization system

van der Heijden, D.S. (author), Ledergerber, Anton (author), Gill, Rajan (author), D'Andrea, Raffaello (author)

An iterative bias estimation framework is presented that mitigates position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The framework is experimentally evaluated on a quadcopter platform, resulting in...

journal article 2021

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Adjustment and Extension of the Hansen and Rattray Estuarine Classification Diagram

Dijkstra, Y.M. (author), Schuttelaars, H.M. (author)

The classification diagram developed by Hansen and Rattray is one of the classic papers on classification of estuarine salinity dynamics. However, we found several inconsistencies in both their stratification–circulation and estuarine classification diagrams. These findings considerably change the interpretation of their work. Furthermore, while...

journal article 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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Integrating intensity and context for improved supervised river ice classification from dual-pol Sentinel-1 SAR data

de Roda Husman, S. (author), Van der Sanden, Joost (author), Lhermitte, S.L.M. (author), Eleveld, M.A. (author)

River ice is a major contributor to flood risk in cold regions due to the physical impediment of flow caused by ice jamming. Although a variety of classifiers have been developed to distinguish ice types using HH or VV intensity of SAR data, mostly based on data from RADARSAT-1 and -2, these classifiers still experience problems with breakup...

journal article 2021

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Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method

Widyaningrum, E. (author), Bai, Q. (author), Fajari, Marda K. (author), Lindenbergh, R.C. (author)

Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise...

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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Automatic change detection of digital maps using aerial images and point clouds

Dahle, F. (author), Arroyo Ohori, G.A.K. (author), Agugiaro, G. (author), Briels, Sven (author)

In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change...

journal article 2021

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A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes

Skrodzki, M. (author), Zimmermann, Eric (author)

In this paper, we define and evaluate a weighting scheme for neighborhoods in point sets. Our weighting takes the shape of the geometry, i.e., the normal information, into account. This causes the obtained neighborhoods to be more reliable in the sense that connectivity also depends on the orientation of the point set. We utilize a sigmoid to...

journal article 2021

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A review and experimental analysis of active learning over crowdsourced data

Sayin, Burcu (author), Krivosheev, Evgeny (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)

Training data creation is increasingly a key bottleneck for developing machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by selecting the most informative instances for labeling. Labels in real applications are often collected from crowdsourcing, which engages...

journal article 2021

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A unifying approach to subtidal salt intrusion modeling in tidal estuaries

Dijkstra, Y.M. (author), Schuttelaars, H.M. (author)

The salinity structure in estuaries is classically described in terms of the salinity structure as well mixed, partially mixed, or salt wedge. The existing knowledge about the processes that result in such salinity structures comes from highly idealized models that are restricted to either well-mixed and partially mixed cases or subtidal salt...

journal article 2021