Zobrazeno 1 - 10
of 24 263
pro vyhledávání: '"A., Heydari"'
By considering the analytic, static and spherically symmetric solution for the Schwarzschild black holes immersed in dark matter fluid with non-zero tangential pressure \cite{Jusufi:2022jxu} and Hernquist-type density profiles \cite{Cardoso}, we comp
Externí odkaz:
http://arxiv.org/abs/2408.16020
In this paper, a new heterostructure based on the hybridization of graphene-LiF layers with a nonlinear material is introduced and studied. The numerical results are depicted and discussed in detail. A high value of FOM (FOM=24.5) at the frequency of
Externí odkaz:
http://arxiv.org/abs/2410.12739
This paper introduces a novel model-free control strategy for a complex multi-stage gearbox electromechanical linear actuator (EMLA) system, driven by a permanent magnet synchronous motor (PMSM) with non-ideal ball screw characteristics. The proposed
Externí odkaz:
http://arxiv.org/abs/2409.12406
The global push for sustainability and energy efficiency is driving significant advancements across various industries, including the development of electrified solutions for heavy-duty mobile manipulators (HDMMs). Electromechanical linear actuators
Externí odkaz:
http://arxiv.org/abs/2409.11849
Autor:
Shahna, Mehdi Heydari, Mattila, Jouni
To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework
Externí odkaz:
http://arxiv.org/abs/2409.11828
In-wheel drive (IWD) systems enhance the responsiveness, traction, and maintenance efficiency of vehicles by enabling each wheel to operate independently. This paper proposes a novel robust torque-observed valve-based control (RTOVC) framework to add
Externí odkaz:
http://arxiv.org/abs/2409.11823
This paper introduces a novel framework combining LLM agents as proxies for human strategic behavior with reinforcement learning (RL) to engage these agents in evolving strategic interactions within team environments. Our approach extends traditional
Externí odkaz:
http://arxiv.org/abs/2409.10372
Autor:
Ghaffarzadeh-Esfahani, Mohammadreza, Ghaffarzadeh-Esfahani, Mahdi, Salahi-Niri, Arian, Toreyhi, Hossein, Atf, Zahra, Mohsenzadeh-Kermani, Amirali, Sarikhani, Mahshad, Tajabadi, Zohreh, Shojaeian, Fatemeh, Bagheri, Mohammad Hassan, Feyzi, Aydin, Tarighatpayma, Mohammadamin, Gazmeh, Narges, Heydari, Fateme, Afshar, Hossein, Allahgholipour, Amirreza, Alimardani, Farid, Salehi, Ameneh, Asadimanesh, Naghmeh, Khalafi, Mohammad Amin, Shabanipour, Hadis, Moradi, Ali, Zadeh, Sajjad Hossein, Yazdani, Omid, Esbati, Romina, Maleki, Moozhan, Nasr, Danial Samiei, Soheili, Amirali, Majlesi, Hossein, Shahsavan, Saba, Soheilipour, Alireza, Goudarzi, Nooshin, Taherifard, Erfan, Hatamabadi, Hamidreza, Samaan, Jamil S, Savage, Thomas, Sakhuja, Ankit, Soroush, Ali, Nadkarni, Girish, Darazam, Ilad Alavi, Pourhoseingholi, Mohamad Amin, Safavi-Naini, Seyed Amir Ahmad
Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materi
Externí odkaz:
http://arxiv.org/abs/2409.02136
Autor:
Ghanbari, Farnaz, Heydari, Abbas
In this article, we investigate the K\"ahler immersions of special Asymptotically Locally Euclidean (ALE) K\"ahler metrics into complex space forms. We provide a relation between K\"ahler immersions problem of these metrics and the sign of their mass
Externí odkaz:
http://arxiv.org/abs/2408.15551
Autor:
Yalcinkaya, Dilek M., Youssef, Khalid, Heydari, Bobak, Wei, Janet, Merz, Noel Bairey, Judd, Robert, Dharmakumar, Rohan, Simonetti, Orlando P., Weinsaft, Jonathan W., Raman, Subha V., Sharif, Behzad
Background. Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-cente
Externí odkaz:
http://arxiv.org/abs/2408.04805