Zobrazeno 1 - 10
of 47 735
pro vyhledávání: '"AS, Sani"'
The potential health risks of cement dust exposure are increasingly raising concern worldwide as the cement industry expands in response to rising cement demand. This necessitates the need to determine the nature of the risks in order to develop appr
Externí odkaz:
http://arxiv.org/abs/2407.00420
Autor:
Souri, Naser, Mehrizi-Sani, Ali
Due to the increasing popularity of DC loads and the potential for higher efficiency, DC microgrids are gaining significant attention. DC microgrids utilize multiple parallel converters to deliver sufficient power to the load. However, a key challeng
Externí odkaz:
http://arxiv.org/abs/2406.07513
Hybrid Machine Learning Approach for Cyberattack Mitigation of Parallel Converters in a DC Microgrid
Autor:
Souri, Naser, Mehrizi-Sani, Ali
Cyberattack susceptibilities are introduced as the communication requirement increases with the incorporation of more renewable energy sources into DC microgrids. Parallel DC-DC converters are utilized to provide high current and supply the load. Nev
Externí odkaz:
http://arxiv.org/abs/2406.07503
Graph hypernetworks (GHNs), constructed by combining graph neural networks (GNNs) with hypernetworks (HNs), leverage relational data across various domains such as neural architecture search, molecular property prediction and federated learning. Desp
Externí odkaz:
http://arxiv.org/abs/2405.20882
Autor:
Vietri, A., Berton, M., Järvelä, E., Kunert-Bajraszewska, M., Ciroi, S., Varglund, I., Barba, B. Dalla, Sani, E., Crepaldi, L.
The term 'active galactic nuclei' (AGN) subtends a huge variety of objects, classified on their properties at different wavelengths. Peaked sources (PS) represent a class of AGN at the first stage of evolution, characterised by a peaked radio spectru
Externí odkaz:
http://arxiv.org/abs/2405.17552
Autor:
Iacob, Alex, Sani, Lorenzo, Marino, Bill, Aleksandrov, Preslav, Shen, William F., Lane, Nicholas Donald
The reliance of language model training on massive amounts of computation and vast datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into question practically, legally, and ethically. Federated learning provides a
Externí odkaz:
http://arxiv.org/abs/2405.14446
Autor:
Sani, Lorenzo, Iacob, Alex, Cao, Zeyu, Marino, Bill, Gao, Yan, Paulik, Tomas, Zhao, Wanru, Shen, William F., Aleksandrov, Preslav, Qiu, Xinchi, Lane, Nicholas D.
Generative pre-trained large language models (LLMs) have demonstrated impressive performance over a wide range of tasks, thanks to the unprecedented amount of data they have been trained on. As established scaling laws indicate, LLMs' future performa
Externí odkaz:
http://arxiv.org/abs/2405.10853
Autor:
Alam, Mohammad Shafiul, Faria, Fatema Tuj Johora, Moin, Mukaffi Bin, Wase, Ahmed Al, Sani, Md. Rabius, Hasib, Khan Md
Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques. However, when applied to new conditions, these applications frequently face the difficulty of overfitting, resulting in lower
Externí odkaz:
http://arxiv.org/abs/2405.07332
100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has st
Externí odkaz:
http://arxiv.org/abs/2405.07310
As the composition of the power grid evolves to integrate more renewable generation, its reliance on distributed energy resources (DER) is increasing. Existing DERs are often controlled with proportional integral (PI) controllers that, if not properl
Externí odkaz:
http://arxiv.org/abs/2405.07108