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Mohammad Hossein Ahmadi

Bio: Mohammad Hossein Ahmadi is an academic researcher from University of Shahrood. The author has contributed to research in topics: Nanofluid & Exergy. The author has an hindex of 60, co-authored 477 publications receiving 11659 citations. Previous affiliations of Mohammad Hossein Ahmadi include Mazandaran University of Medical Sciences & Bu-Ali Sina University.


Papers
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Journal ArticleDOI
01 Nov 2019-Energy
TL;DR: The principles of thermoelectricity are described and an explanation of current and upcoming materials are presented and developed models and various performed optimization of thermOElectric applications by using non-equilibrium thermodynamics and finite time thermodynamics are discussed.

293 citations

Journal ArticleDOI
TL;DR: In this article, several experimental and theoretical studies conducted on the thermal conductivity of nanofluids are represented and investigated based on the reviewed studies, various factors affect thermal conductivities such as temperature, the shape of nanoparticles, concentration and etc.

281 citations

Journal ArticleDOI
TL;DR: In this article, a solar-powered high temperature differential Stirling engine was considered for optimization using multiple criteria, including the output power and overall thermal efficiency, and the Pareto optimal frontier was obtained and a final optimal solution was also selected using various decision-making methods.

227 citations

Journal ArticleDOI
TL;DR: In this paper, a combined cooling, heating, and power system with a gas turbine, an organic Rankine cycle, and an absorption refrigeration system is presented, and the results show that under design condition, the system can generate 33.67kW electricity, 2.56kW cooling and 1.82kW hot water with a round trip energy efficiency of 53.94%.

206 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

2,069 citations

01 Jan 2007

1,932 citations

01 Jan 2016

1,633 citations