Zobrazeno 1 - 10
of 13 652
pro vyhledávání: '"A. Bazan"'
Publikováno v:
Archives of Foundry Engineering, Vol vol. 22, Iss No 4, Pp 121-127 (2022)
One of the main problems of machining of moulds is the need for an effective monitoring system of wear of cutting tools. This paper presents the results of coordinate measurements of a cutting tool which were obtained by using the non-contact measuri
Externí odkaz:
https://doaj.org/article/0a1ba38377e443bda767fa3075200df4
Autor:
Romero-Tapiador, Sergio, Tolosana, Ruben, Morales, Aythami, Lacruz-Pleguezuelos, Blanca, Pastor, Sofia Bosch, Marcos-Zambrano, Laura Judith, Bazán, Guadalupe X., Freixer, Gala, Vera-Rodriguez, Ruben, Fierrez, Julian, Ortega-Garcia, Javier, Espinosa-Salinas, Isabel, Pau, Enrique Carrillo de Santa
Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss cha
Externí odkaz:
http://arxiv.org/abs/2409.08700
This paper addresses the convergence analysis of a variant of the LevenbergMarquardt method (LMM) designed for nonlinear least-squares problems with non-zero residue. This variant, called LMM with Singular Scaling (LMMSS), allows the LMM scaling matr
Externí odkaz:
http://arxiv.org/abs/2408.10370
Autor:
Caron, Sascha, Dobreva, Nadezhda, Sánchez, Antonio Ferrer, Martín-Guerrero, José D., Odyurt, Uraz, Bazan, Roberto Ruiz de Austri, Wolffs, Zef, Zhao, Yue
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step
Externí odkaz:
http://arxiv.org/abs/2407.07179
Autor:
Atkins, Carolyn, Chahid, Younes, Lister, Gregory, Tuck, Rhys, Isherwood, David, Yu, Nan, Sun, Rongyan, Noto, Itsuki, Yamamura, Kazuya, Civitani, Marta, Vecchi, Gabriele, Pareschi, Giovanni, Alcock, Simon G., Nistea, Ioana-Theodora, Da Silva, Murilo Bazan
Additive manufacturing (AM; 3D printing) has clear benefits in the production of lightweight mirrors for astronomy: it can create optimised lightweight structures and combine multiple components into one. New capabilities in AM ceramics, silicon carb
Externí odkaz:
http://arxiv.org/abs/2407.05314
Autor:
Odyurt, Uraz, Dobreva, Nadezhda, Wolffs, Zef, Zhao, Yue, Sánchez, Antonio Ferrer, Bazan, Roberto Ruiz de Austri, Martín-Guerrero, José D., Varbanescu, Ana-Lucia, Caron, Sascha
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorith
Externí odkaz:
http://arxiv.org/abs/2405.17325
Autor:
Bárcenas-Luque, Antonio José, Moral-Pulido, Francisco, Gutiérrez-Montes, Cándido, Coenen, Wilfried, Martínez-Bazán, Carlos
This work is motivated by the interest in determining the effect of the micro-anatomy of the spinal subarachnoid space on the cerebrospinal fluid flow and on the associated transport of solutes. To that aim, we focus on a canonical model problem in w
Externí odkaz:
http://arxiv.org/abs/2404.01021
Autor:
Bazan, Eduardo Franco Sotelo
A simplified model of an initially excited oscillator as a quantum system interacting with a large number of oscillators acting as a reservoir has been developed in this work. All these oscillators are in their ground state uncoupled each other and a
Externí odkaz:
http://arxiv.org/abs/2402.19307
Autor:
Bazán, Eduardo Franco Sotelo
In this work was researched the problems of classicalization and measurement unifying them in a single problem: the problem of collapse, for this purpose was developed a working program -- the classicalization's program -- in an interpretation of obj
Externí odkaz:
http://arxiv.org/abs/2403.01584
Autor:
Shen, Yuesong, Daheim, Nico, Cong, Bai, Nickl, Peter, Marconi, Gian Maria, Bazan, Clement, Yokota, Rio, Gurevych, Iryna, Cremers, Daniel, Khan, Mohammad Emtiyaz, Möllenhoff, Thomas
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for
Externí odkaz:
http://arxiv.org/abs/2402.17641