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Xiuhua Si, Junshi Wang, Haibo Dong and Jinxiang Xi
This study presents a data-driven approach to identifying anomaly-sensitive parameters through a multiscale, multifaceted analysis of simulated respiratory flows. The anomalies under consideration include a pharyngeal model with three levels of constrict...
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Cihan Ates, Dogan Bicat, Radoslav Yankov, Joel Arweiler, Rainer Koch and Hans-Jörg Bauer
In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital tw...
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Carmine Paolino, Alessio Antolini, Francesco Zavalloni, Andrea Lico, Eleonora Franchi Scarselli, Mauro Mangia, Alex Marchioni, Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, Mattia Luigi Torres, Marcella Carissimi and Marco Pasotti
Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fiel...
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Yue Leng and Sheng Zhong
This paper addresses the challenge of reduced tracking accuracy in maritime electro-optical tracking equipment when dealing with high-mobility targets like speedboats and aircraft due to off-target error delays. We propose an innovative technique that le...
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Abha Pragati, Debadatta Amaresh Gadanayak, Tanmoy Parida and Manohar Mishra
Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission sy...
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