Searched for: subject:"neural%5C+network"
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Varsamopoulos, S. (author)
Quantum error correction (QEC) is key to have reliable quantum computation and storage, due to the fragility of qubits in current quantum technology and the imperfect application of quantum operations. In order to have efficient quantum computation and storage, active QEC is required. QEC consists of an encoding and a decoding process. The way...
doctoral thesis 2019
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Sharifi Noorian, S. (author), Psyllidis, A. (author), Bozzon, A. (author)
Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general mor- phological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and iden- tity of a POI. Recent advancements in computer vision could...
conference paper 2019
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Baireuther, P.S. (author), Caio, M. D. (author), Criger, D.B. (author), Beenakker, CWJ (author), O'Brien, T.E. (author)
A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment - without the need...
journal article 2019
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Liu, S. (author), Oosterlee, C.W. (author), Bohte, Sander M. (author)
This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a...
journal article 2019
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Duives, D.C. (author), Wang, Guangxing (author), Kim, Jiwon (author)
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most...
journal article 2019
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Booth, Brian G. (author), Sijbers, Jan (author), Huysmans, T. (author)
journal article 2019
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Elmahdy, Mohamed S. (author), Jagt, Thyrza (author), Zinkstok, Roel Th. (author), Qiao, Yuchuan (author), Shahzad, Rahil (author), Sokooti, Hessam (author), Yousefi, Sahar (author), Incrocci, Luca (author), Marijnen, C.A.M. (author), Hoogeman, Mischa (author), Staring, M. (author)
Purpose: To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity-Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning. Methods: A three-dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation...
journal article 2019
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Meijer, D.W.J. (author), Scholten, L. (author), Clemens, F.H.L.R. (author), Knobbe, Arno (author)
Sewer pipes are commonly inspected in situ with CCTV equipment. The CCTV footage is then reviewed by human operators in order to classify defects in the pipes and make a recommendation on possible interventions. This process is both labor-intensive and error-prone. Other researchers have suggested machine learning techniques to (partially)...
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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Scharenborg, O.E. (author), van der Gouw, Nikki (author), Larson, M.A. (author), Marchiori, Elena (author)
In this paper, we investigate the connection between how people understand speech and how speech is understood by a deep neural network. A naïve, general feed-forward deep neural network was trained for the task of vowel/consonant classification. Subsequently, the representations of the speech signal in the different hidden layers of the DNN...
conference paper 2019
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Nakayama, S. (author), Blacquière, G. (author), Ishiyama, Tomohide (author)
Blended acquisition along with efficient spatial sampling is capable of providing high-quality seismic data in a cost-effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a...
journal article 2019
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Poort, Jonah P. (author), Ramdin, M. (author), van Kranendonk, J. (author), Vlugt, T.J.H. (author)
Vapor-liquid phase equilibrium —flash— calculations largely contribute to the total computation time of many process simulations. As a result, process simulations, especially dynamic ones, are limited in the amount of detail that can be included due to simulation time restrictions. In this work, artificial neural networks were investigated...
journal article 2019
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Ewald, V.A. (author), Groves, R.M. (author), Benedictus, R. (author)
In our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals...
conference paper 2019
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Liu, S. (author), Borovykh, Anastasia (author), Grzelak, L.A. (author), Oosterlee, C.W. (author)
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The...
journal article 2019
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Mahajan, N. (author), Hegyi, A. (author), Hoogendoorn, S.P. (author), van Arem, B. (author)
Intelligent vehicle technologies are opening new possibilities for decentralized vehicle routing systems, suitable for regulating large traffic networks, and at the same time, capable of providing customized advice to individual vehicles. In this study, we perform a rigorous simulation-based analysis of an in-vehicle routing strategy that...
journal article 2019
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Bormans, R. P.A. (author), Lindenbergh, R.C. (author), Karimi Nejadasl, F. (author)
One of the biggest challenges for an autonomous vehicle (and hence the WEpod) is to see the world as humans would see it. This understanding is the base for a successful and reliable future of autonomous vehicles. Real-world data and semantic segmentation generally are used to achieve full understanding of its surroundings. However, deploying...
journal article 2018
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Reale, C. (author), Gavin, K. (author), Librić, Lovorka (author), Jurić-Kaćunić, Danijela (author)
Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the...
journal article 2018
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Wang, Rongxiao (author), Chen, B. (author), Qiu, S. (author), Ma, Liang (author), Zhu, Zhengqiu (author), Wang, Yiping (author), Qiu, Xiaogang (author)
Locating and quantifying the emission source plays a significant role in the emergency management of hazardous gas leak accidents. Due to the lack of a desirable atmospheric dispersion model, current source estimation algorithms cannot meet the requirements of both accuracy and efficiency. In addition, the original optimization algorithm can...
journal article 2018
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Lago Garcia, J. (author), De Ridder, Fjo (author), Vrancx, Peter (author), De Schutter, B.H.K. (author)
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive...
journal article 2018
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Pezzotti, N. (author), Hollt, T. (author), van Gemert, J.C. (author), Lelieveldt, B.P.F. (author), Eisemann, E. (author), Vilanova Bartroli, A. (author)
Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The...
journal article 2018
Searched for: subject:"neural%5C+network"
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