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Lee, Y. (author)
E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...
doctoral thesis 2024
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Lu, C.J. (author)
Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some...
journal article 2024
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Martinez Lopez, V.A. (author), van Urk, G.A. (author), Doodkorte, P.J.F. (author), Zeman, M. (author), Isabella, O. (author), Ziar, H. (author)
Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short...
journal article 2024
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van Nuland, T.D.H. (author)
The universal approximation theorem is generalised to uniform convergence on the (noncompact) input space R<sup>n</sup>. All continuous functions that vanish at infinity can be uniformly approximated by neural networks with one hidden layer, for all activation functions φ that are continuous, nonpolynomial, and asymptotically polynomial at ±∞...
journal article 2024
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Wang, X. (author), Corzo, Gerald (author), Lü, Haishen (author), Zhou, Shiliang (author), Mao, K. (author), Zhu, Yonghua (author), Duarte Prieto, F.S. (author), Liu, Mingwen (author), Su, Jianbin (author)
Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land...
journal article 2024
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Paredes-Vallés, Federico (author)
In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...
doctoral thesis 2023
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do Lago, Cesar A.F. (author), Giacomoni, Marcio H. (author), Bentivoglio, Roberto (author), Taormina, R. (author), Gomes, Marcus N. (author), Mendiondo, Eduardo M. (author)
Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for...
journal article 2023
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Rahmani, S. (author), Baghbani, Asiye (author), Bouguila, Nizar (author), Patterson, Zachary (author)
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...
journal article 2023
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Cardaioli, Matteo (author), Conti, M. (author), Orazi, Gabriele (author), Tricomi, Pier Paolo (author), Tsudik, Gene (author)
Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers...
journal article 2023
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Esmi, Nima (author), Golshan, Yasaman (author), Asadi, Sara (author), Shahbahrami, A. (author), Gaydadjiev, G. (author)
The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning,...
journal article 2023
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Glynis, K.G. (author), Kapelan, Z. (author), Bakker, Martijn (author), Taormina, R. (author)
Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new...
journal article 2023
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Abdellatif, Alaa Awad (author), Mhaisen, N. (author), Mohamed, Amr (author), Erbad, Aiman (author), Guizani, Mohsen (author)
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement...
journal article 2023
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Ossenkoppele, B.W. (author), Luijten, Ben (author), Bera, Deep (author), de Jong, N. (author), Verweij, M.A. (author), van Sloun, Ruud J.G. (author)
There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count...
journal article 2023
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Nastos Konstantopoulos, C. (author), Komninos, P. (author), Zarouchas, D. (author)
A hybrid methodology based on numerical and non-destructive experimental schemes, which is able to predict the structural level strength of composite laminates is proposed on the current work. The main objective is to predict the strength by substituting the up to failure experiments with non-destructive experiments where the investigated...
journal article 2023
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Song, Yang (author), Wang, H. (author), Frøseth, Gunnstein (author), Nåvik, Petter (author), Liu, Zhigang (author), Rønnquist, Anders (author)
The interaction performance of the pantograph-catenary is of great importance as it directly determines the current collection quality and operational safety of trains. The finite element method (FEM) is dominantly used for simulating pantograph-catenary interaction, which is normally computationally heavy. In this work, addressing the...
journal article 2023
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Yin, Xilin (author), Wang, H. (author), Pisano, F. (author), Gavin, Kenneth (author), Askarinejad, A. (author), Zhou, Hongpeng (author)
Predicting the non-linear loading response is the key to the design of suction caissons. This paper presents a systematic study to explore the applicability of deep learning techniques in foundation design. Firstly, a series of three-dimensional finite element simulations was performed, covering a wide range of embedment ratios and different...
journal article 2023
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)
Internet of Things (IoT) applications are growing in popularity for being widely used in many real-world services. In an IoT ecosystem, many devices are connected with each other via internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major concern in its deployment is network security and user privacy. To...
journal article 2023
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Madadi, B. (author), Correia, Gonçalo (author)
This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function...
journal article 2023
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Abolfazli, Amir (author), Spiegelberg, Jakob (author), Anand, A. (author), Palmer, Gregory (author)
Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the...
conference paper 2023
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Bafti, Alireza Ghaderi (author), Ahmadi, Arman (author), Abbasi, A. (author), Kamangir, Hamid (author), Jamali, Sadegh (author), Hashemi, Hossein (author)
Actual evapotranspiration (ET<sub>a</sub>) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ET<sub>a</sub> is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ET<sub>a</sub> values,...
journal article 2023
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