Zobrazeno 1 - 10
of 2 009
pro vyhledávání: '"Camponogara, A."'
Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws into the training of ESNs, Physics-Informed ESNs (PI-ESNs) were proposed
Externí odkaz:
http://arxiv.org/abs/2409.19140
This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in deep equilibrium models (DEQs) and physics-informed neural
Externí odkaz:
http://arxiv.org/abs/2406.03472
An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes in many robotic scenarios (including mobile robotics, surgical assist
Externí odkaz:
http://arxiv.org/abs/2405.20534
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problems defined over a network of computational nodes. Mathematically, these
Externí odkaz:
http://arxiv.org/abs/2405.16267
Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (OD
Externí odkaz:
http://arxiv.org/abs/2403.02289
Maximizing oil production from gas-lifted oil wells entails solving Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as the basic-sediment-to-water ratio and the gas-oil ratio, are updated, the problems must be repeatedly s
Externí odkaz:
http://arxiv.org/abs/2309.00197
Autor:
Pacheco, Bruno Machado, Seman, Laio Oriel, Rigo, Cezar Antonio, Camponogara, Eduardo, Bezerra, Eduardo Augusto, Coelho, Leandro dos Santos
This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbi
Externí odkaz:
http://arxiv.org/abs/2303.13773
This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust co
Externí odkaz:
http://arxiv.org/abs/2303.11890
Autor:
Emanuelli Mancio Ferreira da Luz, Oclaris Lopes Munhoz, Patrícia Bitencourt Toscani Greco, Bruna Xavier Morais, Silviamar Camponogara, Tânia Solange Bosi de Souza Magnago
Publikováno v:
Revista Brasileira de Enfermagem, Vol 77, Iss 6 (2024)
RESUMO Objetivos: verificar a prevalência e os fatores associados à dor musculoesquelética em trabalhadores do serviço hospitalar de limpeza. Métodos: estudo transversal, realizado com trabalhadores de limpeza de um hospital de ensino do Sul do
Externí odkaz:
https://doaj.org/article/2779d74e8a8741859673532143a34ed4
Neural networks achieved high performance over different tasks, i.e. image identification, voice recognition and other applications. Despite their success, these models are still vulnerable regarding small perturbations, which can be used to craft th
Externí odkaz:
http://arxiv.org/abs/2301.12001