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de Bruin, T.D. (author), Kober, J. (author), Tuyls, K.P. (author), Babuska, R. (author)
Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two...
journal article 2018
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Lago, Jesus (author), De Ridder, Fjo (author), De Schutter, B.H.K. (author)
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and...
journal article 2018
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Lago, Jesus (author), De Brabandere, Karel (author), De Ridder, Fjo (author), De Schutter, B.H.K. (author)
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain,...
journal article 2018
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Suryanarayana, Gowri (author), Lago, Jesus (author), Geysen, Davy (author), Aleksiejuk, Piotr (author), Johansson, Christian (author)
Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper...
journal article 2018
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Buşoniu, Lucian (author), de Bruin, T.D. (author), Tolić, Domagoj (author), Kober, J. (author), Palunko, Ivana (author)
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the...
review 2018
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Gudi, A.A. (author), Bittner, M. (author), Lochmans, Roelof (author), van Gemert, J.C. (author)
Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine...
conference paper 2019
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Mozaffar, M. (author), Bostanabad, R. (author), Chen, W. (author), Ehmann, K. (author), Cao, J. (author), Bessa, M.A. (author)
Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of...
journal article 2019
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Dubost, Florian (author), Yilmaz, Pinar (author), Adams, Hieab (author), Bortsova, Gerda (author), Ikram, M. Arfan (author), Niessen, W.J. (author), Vernooij, Meike (author), de Bruijne, Marleen (author)
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would...
journal article 2019
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Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a...
journal article 2019
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Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
In this ongoing work, we describe several architectures that generalize convolutional neural networks (CNNs) to process signals supported on graphs. The general idea of the replace time invariant filters with graph filters to generate convolutional features and to replace pooling with sampling schemes for graph signals. The different...
conference paper 2019
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Paltrinieri, Nicola (author), Comfort, Louise (author), Reniers, G.L.L.M.E. (author)
Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are...
journal article 2019
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Ghavamian, F. (author), Simone, A. (author)
FE<sup>2</sup> multiscale simulations of history-dependent materials are accelerated by means of a recurrent neural network (RNN) surrogate for the history-dependent micro level response. We propose a simple strategy to efficiently collect stress–strain data from the micro model, and we modify the RNN model such that it resembles a nonlinear...
journal article 2019
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Cao, Wen-Jun (author)
The train wheel flat is one of the most common damages in the railway system. It occurs when a wheel locks up while the train is moving. The early detection of wheel-flat severity is crucial for passenger comfort and the safety of the railway operation. However, it is still challenging to quantify the properties of wheel flats (e.g., sizes)...
conference paper 2020
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Wu, L. (author), Picek, S. (author)
In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further...
journal article 2020
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Weissbart, L.J.A. (author), Chmielewski, Łukasz (author), Picek, S. (author), Batina, Lejla (author)
Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning...
journal article 2020
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Guédon, Annetje C.P. (author), Meij, S.E.P. (author), Osman, Karim N.M.M.H. (author), Kloosterman, Helena A. (author), van Stralen, Karlijn J. (author), Grimbergen, Matthijs C.M. (author), Eijsbouts, Quirijn A.J. (author), van den Dobbelsteen, J.J. (author), Twinanda, Andru P. (author)
perating room planning is a complex task as pre-operative estimations of procedure duration have a limited accuracy. This is due to large variations in the course of procedures. Therefore, information about the progress of procedures is essential to adapt the daily operating room schedule accordingly. This information should ideally be...
journal article 2020
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Bonsignorio, Fabio (author), Hsu, David (author), Johnson-Roberson, Matthew (author), Kober, J. (author)
Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...
contribution to periodical 2020
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Dubost, Florian (author), Adams, Hieab (author), Yilmaz, Pinar (author), Bortsova, Gerda (author), Tulder, Gijs van (author), Ikram, M. Arfan (author), Niessen, W.J. (author), Vernooij, Meike W. (author), Bruijne, Marleen de (author)
Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method...
journal article 2020
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Tian, Jinyan (author), Wang, Le (author), Yin, Dameng (author), Li, Xiaojuan (author), Diao, Chunyuan (author), Gong, Huili (author), Shi, Chen (author), Menenti, M. (author), Ge, Yong (author)
Invasive Spartina alterniflora (S. alterniflora), a native riparian species in the U.S. Gulf of Mexico, has led to serious degradation to the ecosystem and biodiversity as well as economic losses since it was introduced to China in 1979. Although multi-temporal remote sensing offers unique capability to monitor S. alterniflora over large...
journal article 2020
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Castillo, Jose M.T. (author), Arif, Muhammad (author), Niessen, W.J. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)
Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification...
journal article 2020
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