Zobrazeno 1 - 10
of 2 078
pro vyhledávání: '"A. Gheibi"'
The present paper investigated automatic melody construction for Persian lyrics as an input. It was assumed that there is a phonological correlation between the lyric syllables and the melody in a song. A seq2seq neural network was developed to inves
Externí odkaz:
http://arxiv.org/abs/2410.18203
Autor:
AhmadReza Rahmati, A. Gheibi
Publikováno v:
Journal of Heat and Mass Transfer Research, Vol 6, Iss 2, Pp 75-84 (2019)
In the present work, the performances of improved two-stage multi inter-cooler trans- critical carbon dioxide (CO2) refrigeration cycles with ejector and internal heat exchanger have been examined. In the new improved cycles, an internal heat exchang
Externí odkaz:
https://doaj.org/article/7cd8c8f39cab4272a337142aefd82ff1
Autor:
Gheibi, Mohsen, Takahashi, Ryo
Let $R$ be a commutative noetherian local ring, and let $M$ be a finitely generated $R$-module. Inspired by works of Vasconcelos and Briggs on characterization of complete intersection local rings through the homological properties of the conormal mo
Externí odkaz:
http://arxiv.org/abs/2404.17680
Autor:
Lashkari, Mohammad, Gheibi, Amin
The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research. Despite widespread techniques used to diminish the over-fitting issue such as data augmentation, pseudo-labeling, regulari
Externí odkaz:
http://arxiv.org/abs/2403.08408
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 78, Iss 8, Pp 1-8 (2018)
Abstract We introduce analogue black holes (BHs) based on ideal magnetohydrodynamic equations. Similar to acoustic BHs, which trap phonons and emit Hawking radiation (HR) at the sonic horizon where the flow speed changes from super- to sub-sonic, in
Externí odkaz:
https://doaj.org/article/00d96aac365e455cb7419bd3a3e6fcd6
Autor:
AhmadReza Rahmati, A. Gheibi
Publikováno v:
Journal of Heat and Mass Transfer Research, Vol 5, Iss 1, Pp 1-9 (2018)
The lattice Boltzmann method (LBM) was used to analyze two-dimensional (2D) non-Fourier heat conduction with temperature-dependent thermal conductivity. To this end, the evolution of wave-like temperature distributions in a 2D plate was obtained. The
Externí odkaz:
https://doaj.org/article/81de366abcfd417c8ee4bd9e89b13c60
Autor:
Mohammad Lashkari, Amin Gheibi
Publikováno v:
AUT Journal of Mathematics and Computing, Vol 5, Iss 4, Pp 361-375 (2024)
The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lip
Externí odkaz:
https://doaj.org/article/c4b9081057d14de6831544d342e30115
Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One
Externí odkaz:
http://arxiv.org/abs/2306.01404
Autor:
Zargar, Majid Rahro, Gheibi, Mohsen
Let $(R,\fm)$ be a local ring, and let $C$ be a semidualizing complex. We establish the equality $r_R(Z) = \nu(\Ext^{g-\inf C}_R(Z,C))\mu^{\depth C}_R(\mathfrak{m}, C)$ for a homologically finite and bounded complex $Z$ with finite $\GC$-dimension $g
Externí odkaz:
http://arxiv.org/abs/2305.12251
Autor:
Lashkari, Mohammad, Gheibi, Amin
The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lip
Externí odkaz:
http://arxiv.org/abs/2303.16464