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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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Ewald, Vincentius (author), Sridaran Venkat, Ramanan (author), Asokkumar, Aadhik (author), Benedictus, R. (author), Boller, Christian (author), Groves, R.M. (author)Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non...journal article 2021
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Classification of airborne laser scanning point cloud using point-based convolutional neural networkZhu, Jianfeng (author), Sui, Lichun (author), Zang, Y. (author), Zheng, He (author), Jiang, Wei (author), Zhong, Mianqing (author), Ma, Fei (author)In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be...journal article 2021
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Li, Bo (author), Niessen, W.J. (author), Klein, Stefan (author), de Groot, Marius (author), Ikram, M. Arfan (author), Vernooij, Meike W. (author), Bron, Esther E. (author)This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and...journal article 2021
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Šabanovič, Eldar (author), Kojis, Paulius (author), Šukevičius, Šarūnas (author), Shyrokau, B. (author), Ivanov, Valentin (author), Dhaens, Miguel (author), Skrickij, Viktor (author)With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this...journal article 2021
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Schweidtmann, A.M. (author), Esche, Erik (author), Fischer, Asja (author), Kloft, Marius (author), Repke, Jens Uwe (author), Sager, Sebastian (author), Mitsos, Alexander (author)The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio-)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering ...review 2021
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Lumban-Gaol, Y. A. (author), Chen, Z. (author), Smit, M. (author), Li, X. (author), Erbaşu, M. A. (author), Verbree, E. (author), Balado Frías, J. (author), Meijers, B.M. (author), van der Vaart, C.G. (author)Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...journal article 2021
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Jamali-Rad, H. (author), Szabó, Attila (author)Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art...journal article 2021
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Sabidussi, E. R. (author), Klein, S. (author), Caan, M. W.A. (author), Bazrafkan, S. (author), den Dekker, A. J. (author), Sijbers, J. (author), Niessen, W.J. (author), Poot, D.H.J. (author)In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T<sub>1</sub> and T<sub>2</sub> mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood...journal article 2021
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Pastor Serrano, O. (author), Lathouwers, D. (author), Perko, Z. (author)Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...journal article 2021
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Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Tumwiine, Julius (author), Wario, Ruth (author), Obungoloch, Johnes (author)Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained...review 2021
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Calkoen, Floris (author), Luijendijk, Arjen (author), Rivero, Cristian Rodriguez (author), Kras, Etienne (author), Baart, F. (author)Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines....journal article 2021
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Andersson, K.H. (author), Oosterlee, C.W. (author)In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, e.g., a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for...journal article 2021
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Shen, Chunguang (author), Wang, Chenchong (author), Huang, Minghao (author), Xu, Ning (author), van der Zwaag, S. (author), Xu, W. (author)We present an electron backscattered diffraction (EBSD)-trained deep learning (DL) method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope (SEM) images. In this method, EBSD...journal article 2021
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Wu, L. (author), Perin, G. (author)In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural...conference paper 2021
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Lago, Jesus (author), Marcjasz, Grzegorz (author), De Schutter, B.H.K. (author), Weron, Rafał (author)While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The...review 2021
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Ippolito, Giulia (author)This work is part of the low-fieldMRI project, which aims to bring portable, affordable, low-fieldMRI scanners to low-income countries. Replacing the superconducting magnets of conventional scanners with standard ones can significantly reduce the costs, but it also has a negative impact on the Signal-to-Noise Ratio (SNR). In order to circumvent...master thesis 2020
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Sellik, Hendrig (author)Mistakes in binary conditions are a source of error in many software systems. They happen when developers use < or > instead of <= or >=. These boundary mistakes are hard to find for developers and pose a manual labor-intensive work. While researches have been proposing solutions to identify errors in boundary conditions, the problem...master thesis 2020
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Molenaar, Mitchel (author)Patellar tendinopathy (PT) is a common manifestation in jumping sports characterized by pain and a reduced load bearing capacity. The exact cause of PT has not been determined, which makes it difficult to prevent and treat. A stiffer landing technique might be a risk-factor for PT. Retraining of the landing technique into a less stiffer...master thesis 2020
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Simion-Constantinescu, Andrei (author)This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...master thesis 2020