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Nakayama, Shotaro (author), Blacquière, G. (author)
Acquisition of incomplete data, i.e., blended, sparsely sampled, and narrowband data, allows for cost-effective and efficient field seismic operations. This strategy becomes technically acceptable, provided that a satisfactory recovery of the complete data, i.e., deblended, well-sampled, and broadband data, is attainable. Hence, we explore a...
journal article 2021
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Zoutendijk, M. (author), Mitici, M.A. (author)
The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting...
journal article 2021
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Sun, J. (author), Veefkind, j. Pepijn (author), Van Velthoven, Peter (author), Levelt, Pieternel Felicitas (author)
Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic...
journal article 2021
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Sellevold, R. (author), Vizcaino, M. (author)
Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relationships between quantities consistently...
journal article 2021
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Guo, Rui (author), Weingärtner, S.D. (author), Šiuryté, P. (author), T. Stoeck, Christian (author), Füetterer, Maximilian (author), E. Campbell-Washburn, Adrienne (author), Suinesiaputra, Avan (author), Jerosch-Herold, Michael (author), Nezafat, Reza (author)
Cardiovascular disease is the leading cause of death and a significant contributor of health care costs. Noninvasive imaging plays an essential role in the management of patients with cardiovascular disease. Cardiac magnetic resonance (MR) can noninvasively assess heart and vascular abnormalities, including biventricular structure/function,...
review 2021
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van Dijk, M.P. (author), Kok, M. (author), Berger, Monique A.M. (author), Hoozemans, Marco J.M. (author), Veeger, H.E.J. (author)
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the...
journal article 2021
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de Hoop, S. (author), Voskov, D.V. (author)
The main objective of this study is to perform Uncertainty Quantification (UQ) using a detailed representation of fractured reservoirs. This is achieved by creating a simplified representation of the fracture network while preserving the main characteristics of the high-fidelity model. We include information at different scales in the UQ...
conference paper 2021
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Doan, Nguyen Anh Khoa (author), Polifke, W. (author), Magri, L. (author)
We propose a physics-constrained machine learning method - based on reservoir computing - to time-accurately predict extreme events and long-term velocity statistics in a model of chaotic flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics...
journal article 2021
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Iakovidis, Dimitris K. (author), Ooi, Melanie (author), Kuang, Ye Chow (author), Demidenko, Serge (author), Shestakov, Alexandr (author), Sinitsin, Vladimir (author), Henry, Manus (author), Sciacchitano, A. (author), Fioranelli, F. (author)
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in...
review 2021
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Yilmaz, Kaan (author), Yorke-Smith, N. (author)
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy...
journal article 2021
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Hendrickx, Rik (author), Zoutendijk, M. (author), Mitici, M.A. (author), Schäfer, Jeffrey (author)
A key part of efficient airport operational planning is to have insight into potential flight delays and cancellations. For airport planners, it is important to obtain flight delay or cancellation predictions with a high degree of certainty, i.e. a high precision. This allows planners to make sound decisions based on these predictions. To obtain...
conference paper 2021
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Giunti, Guido (author), Isomursu, M. (author), Gabarron, E. (author), Solad, Y. (author)
Advances in voice recognition, natural language processing, and artificial intelligence have led to the increasing availability and use of conversational agents (chatbots) in different settings. Chatbots are systems that mimic human dialogue interaction through text or voice. This paper describes a series of design considerations for...
conference paper 2021
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Fioranelli, F. (author), Le Kernec, Julien (author)
In this paper, radar sensing in the domain of human healthcare is discussed, specifically looking at the typical applications of human activity classification (including fall detection), gait analysis and gait parameters extraction, and vital signs monitoring such as respiration and heartbeat. A brief overview of open research challenges and...
conference paper 2021
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Makrodimitris, S. (author)
Billions of people world-wide rely on plant-based food for their daily energy intake. As global warming and the spread of diseases (such as the banana Panama disease) is substantially hindering the cultivation of plants, the need to develop temperature- and/or disease-resistant varieties is getting more and more pressing. The field of plant...
doctoral thesis 2021
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Coppola, M. (author)
The paradigm of swarm robotics aims to enable several independent robots to collaborate together toward collective goals. The distributed nature of a swarm, whereby each robot acts independently in accordance with its perceived environment, is expected to provide the system with a high degree of flexibility, robustness, and scalability. However,...
doctoral thesis 2021
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OUYANG, Boya (author), LI, Yuhai (author), SONG, Yu (author), WU, Feishu (author), YU, Huizi (author), WANG, Yongzhe (author), BAUCHY, Mathieu (author), SANT, Gaurav (author)
Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, machine learning (ML) methods offer a new solution to this problem. Although ML can handle the complex, non-linear, non-additive...
conference paper 2021
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Ugwuoke, C.I. (author)
The genome is the blueprint of life and has a detailed genotype and phenotype description of any organism. This in itself attributes sensitivity to genetic data, be it in the biological or electronic format. The possibility of sequencing the genome has opened doors to further probing of the data in its electronic form. Post sequencing of the...
doctoral thesis 2021
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Prisacaru, Alexandru (author)
This thesis describes a series of experiments, algorithms, and methodology development for implementing Prognostics and Health Management (PHM) in the field of automotive electronics. Furthermore, a new PHM framework is proposed explicitly tailored for the harsh environment electronics. In addition, the entire apparatus is built, such as the...
doctoral thesis 2021
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Ghavamian, F. (author)
We study the acceleration of the finite element method (FEM) simulations using machine learning (ML) models. Specifically, we replace computationally expensive (parts of) FEM models with efficient ML surrogates. We develop three methods to speed up FEM simulations. The primary difference between these models is their degree of intrusion into the...
doctoral thesis 2021
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Kim, J. (author), Jonoski, Andreja (author), Solomatine, D.P. (author)
Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on...
journal article 2022
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