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Li, G. (author), Knoop, V.L. (author), van Lint, J.W.C. (author)Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning...journal article 2021
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Qu, Dingran (author), Qiao, Tiezhu (author), Pang, Y. (author), Yang, Yi (author), Zhang, Haitao (author)Belt conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of conveyor belt damage detection based on ADCN (Adaptive Deep...journal article 2021
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Thangavel, Prasanth (author), Thomas, John (author), Peh, Wei Yan (author), Jing, Jin (author), Yuvaraj, Rajamanickam (author), Cash, Sydney S. (author), Chaudhari, Rima (author), Saini, Vinay (author), Dauwels, J.H.G. (author)Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection...journal article 2021
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Kulin, Merima (author), Kazaz, T. (author), De Poorter, Eli (author), Moerman, Ingrid (author)This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing...journal article 2021
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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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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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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