Searched for: subject%3A%22processing%22
(1 - 19 of 19)
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Daoutidis, Prodromos (author), Lee, Jay H. (author), Rangarajan, Srinivas (author), Chiang, Leo (author), Gopaluni, Bhushan (author), Schweidtmann, A.M. (author), Harjunkoski, Iiro (author), Mercangöz, Mehmet (author), Mesbah, Ali (author)
This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27–29, 2022. The session included two invited talks and three short contributed presentations followed by extensive discussions. This paper does not...
journal article 2024
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Theisen, M.F. (author), Nishizaki Flores, K.F. (author), Schulze Balhorn, L. (author), Schweidtmann, A.M. (author)
Advances in deep convolutional neural networks led to breakthroughs in many computer vision applications. In chemical engineering, a number of tools have been developed for the digitization of Process and Instrumentation Diagrams. However, there is no framework for the digitization of process flow diagrams (PFDs). PFDs are difficult to...
journal article 2023
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van 't Sant, S. (author), Thakolkaran, P. (author), Martínez, Jonàs (author), Kumar, Siddhant (author)
Advancements in machine learning have sparked significant interest in designing mechanical metamaterials, i.e., materials that derive their properties from their inherent microstructure rather than just their constituent material. We propose a data-driven exploration of the design space of growth-based cellular metamaterials based on star-shaped...
journal article 2023
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Bode, Lukas (author), Weinmann, M. (author), Klein, Reinhard (author)
Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...
journal article 2023
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Pollack, Justin (author)
Nowadays, machine learning (ML) methods rapidly evolve for their use in model-based control applications. Model-based control requires an accurate model description of the dynamical system to reassure the performance of the controller. Conventionally, this model description is retrieved from first-principles modelling which can be problematic if...
master thesis 2022
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van den Akker, Daniel (author)
Multi-Layer Perceptron and Support Vector Machine have both been widely used in machine learning. In this research paper, these models have been applied to binary classification on an individual time series basis. The goal was to see whether they can predict earthquakes, using earthquakes measured at specific stations across New Zealand. As it...
bachelor thesis 2022
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Meijer, Caspar (author)
Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...
bachelor thesis 2022
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de Groot, N.M.S. (author), Shah, Dipen (author), Boyle, Patrick M. (author), Anter, Elad (author), Clifford, Gari D. (author), Deisenhofer, Isabel (author), van Dessel, Pascal (author), Dilaveris, Polychronis (author), van der Veen, A.J. (author)
We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps,...
journal article 2022
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Wahlstrom, Johan (author), Kok, M. (author)
The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian...
journal article 2022
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Wesselius, F.J. (author), van Schie, M.S. (author), de Groot, N.M.S. (author), Hendriks, R.C. (author)
Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and...
journal article 2022
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van Veelen, Frank (author)
In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change....
master thesis 2021
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Brederveld, Jan (author)
Various machine learning algorithms have been applied to find optimal lowthrust<br/>satellite trajectories, however, no fair comparison of their accuracy has been made yet. In this paper, two common and promising supervised machine learning algorithms are compared for their regression capacities:<br/>the artificial neural network and the...
master thesis 2021
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Klijn, M.E. (author), Hubbuch, Juergen (author)
Imaging is increasingly more utilized as analytical technology in biopharmaceutical formulation research, with applications ranging from subvisible particle characterization to thermal stability screening and residual moisture analysis. This review offers a comprehensive overview of analytical imaging for scientists active in...
journal article 2021
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Liu, Y. (author), Noomen, R. (author), Visser, P.N.A.M. (author)
Inspired by the Keplerian Map and the Flyby Map, a Gravity Assist Mapping using Gaussian Process Regression for the fully spatial Circular Restricted Three-Body Problem is developed. A mapping function for quantifying the flyby effects over one orbital period is defined. The Gaussian Process Regression model is established by proper mean and...
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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Imani, Maryam (author), Hasan, Md Mahmudul (author), Bittencourt, Luiz Fernando (author), McClymont, Kent (author), Kapelan, Z. (author)
Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of...
journal article 2021
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Wesselius, F.J. (author), van Schie, M.S. (author), de Groot, N.M.S. (author), Hendriks, R.C. (author)
Aims: Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into...
review 2021
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Lingmont, Hidde (author)
The advent of machine learning and the availability of big data brought a novel approach for researchers to discover fundamental laws of motion. Computers allow to quickly find underlying physical laws from experimental data, without having in-depth knowledge of the system. Applications are widespread among numerous fields such as physics,...
master thesis 2020
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van Duijvenbode, J.R. (author), Buxton, M.W.N. (author), Soleymani Shishvan, M. (author)
Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help...
journal article 2020
Searched for: subject%3A%22processing%22
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