Zobrazeno 1 - 10
of 1 589
pro vyhledávání: '"Amini, Mohammad A."'
Autor:
Jing, Liwei, Amini, Mohammad, Fumega, Adolfo O., Silveira, Orlando J., Lado, Jose L., Liljeroth, Peter, Kezilebieke, Shawulienu
Topological crystalline insulators (TCIs) host topological phases of matter protected by crystal symmetries. Topological surface states in three-dimensional TCIs have been predicted and observed in IV-VI SnTe-class semiconductors. Despite the predict
Externí odkaz:
http://arxiv.org/abs/2410.06661
Autor:
Amini, Mohammad Hossein, Nejati, Shiva
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar distributions, which can pote
Externí odkaz:
http://arxiv.org/abs/2408.13950
Autor:
Maazallahi, Abbas, Thota, Sreehari, Kondaboina, Naga Prasad, Muktineni, Vineetha, Annem, Deepthi, Rokkam, Abhi Stephen, Amini, Mohammad Hossein, Salari, Mohammad Amir, Norouzzadeh, Payam, Snir, Eli, Rahmani, Bahareh
This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision T
Externí odkaz:
http://arxiv.org/abs/2404.15392
Autor:
Zhang, Jiadi, Li, Xiao, Amini, Mohammad Reza, Kolmanovsky, Ilya, Tsutsumi, Munechika, Nakada, Hayato
This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of nitrogen (N O
Externí odkaz:
http://arxiv.org/abs/2311.03552
This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV). A multi-objective model predictive control (MPC) framework is developed to optimize the fast charging performan
Externí odkaz:
http://arxiv.org/abs/2310.13883
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract Geological disasters occur frequently in the Loess Plateau due to the joint fissures in the strata and human engineering activities. Against this background, the deformation and failure mode of the loess slope with the structural plane under
Externí odkaz:
https://doaj.org/article/85d85833506f46b283732201bc6a7953
Autor:
Amini, Mohammad, Fumega, Adolfo O., González-Herrero, Héctor, Vaňo, Viliam, Kezilebieke, Shawulienu, Lado, Jose L., Liljeroth, Peter
Publikováno v:
Adv. Mater. 36, 2311342 (2024)
Progress in layered van der Waals materials has resulted in the discovery of ferromagnetic and ferroelectric materials down to the monolayer limit. Recently, evidence of the first purely two-dimensional multiferroic material was reported in monolayer
Externí odkaz:
http://arxiv.org/abs/2309.11217
Autor:
Vaňo, Viliam, Reale, Stefano, Silveira, Orlando J., Longo, Danilo, Amini, Mohammad, Kelai, Massine, Lee, Jaehyun, Martikainen, Atte, Kezilebieke, Shawulienu, Foster, Adam S., Lado, Jose L., Donati, Fabio, Liljeroth, Peter, Yan, Linghao
Publikováno v:
Phys. Rev. Lett. 133, 236203 (2024)
Designer heterostructures, where the desired physics emerges from the controlled interactions between different components, represent one of the most powerful strategies to realize unconventional electronic states. This approach has been particularly
Externí odkaz:
http://arxiv.org/abs/2309.02537
Autor:
Amini, Mohammad Reza, Jiang, Boxi, Liao, Yingqian, Naik, Kartik, Martins, Joaquim R. R. A., Sun, Jing
Control co-design (CCD) explores physical and control design spaces simultaneously to optimize a system's performance. A commonly used CCD framework aims to achieve open-loop optimal control (OLOC) trajectory while optimizing the physical design vari
Externí odkaz:
http://arxiv.org/abs/2302.03177
Autor:
Nekoei, Hadi, Badrinaaraayanan, Akilesh, Sinha, Amit, Amini, Mohammad, Rajendran, Janarthanan, Mahajan, Aditya, Chandar, Sarath
Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios where centralized training is either not possible or not practical. One of the critical challenges in decentrali
Externí odkaz:
http://arxiv.org/abs/2302.02792