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Farhat, Arwa Ben,Chandel, Shyam.Singh,Woo, Wai Lok,Adnene, Cherif International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.2
In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.
Characterization of f-derivations of a BCI-algebra
Farhat Nisar 영남수학회 2009 East Asian mathematical journal Vol.25 No.1
In this paper we characterize f-derivations of a BCI-algebra as well as its center.
A hybrid lattice Boltzmann model for surfactant-covered droplets
Farhat, H.,Celiker, F.,Singh, T.,Lee, J. S. Royal Society of Chemistry 2011 SOFT MATTER Vol.7 No.5
<P>This paper proposes a hybrid model for the study of the droplet flow behavior in an immiscible medium with insoluble nonionic surfactant adhering to its interface. The evolution of the surfactant concentration on the interface is modeled by the time-dependent surfactant convection-diffusion equation and solved by a finite difference scheme. The fluid velocity field, the pressure and the interface curvature are calculated using the lattice Boltzmann method (LBM) for binary fluid mixtures. The coupling between the LBM and the finite difference scheme is achieved through the LBM variables and the surfactant equation of state. The Gunstensen LBM is used here because it provides local and independent application of a distinct interfacial tension on the individual nodes of the droplet interface. The hybrid model was developed and successfully applied to droplet deformations under a variety of flow conditions.</P> <P>Graphic Abstract</P><P>This paper proposes a hybrid model for the study of the droplet flow behavior in an immiscible medium with insoluble nonionic surfactant adhering to its interface. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c0sm00569j'> </P>
ON THE CLASS OF $S_3$-ALGEBRAS
Nisar, Farhat,Bhatti, Shaban Ali The Youngnam Mathematical Society Korea 2005 East Asian mathematical journal Vol.21 No.2
In this paper we investigate some more properties of of $S_3$-algebras. We also prove that the class of $S_3$-algebras is contained in the class of commutative BCI-algebras.
COMPUTATIONAL METHOD TO CHARACTERIZE $S_3$-ALGEBRAS
Nisar, Farhat,Bhatti, Shaban Ali The Youngnam Mathematical Society Korea 2007 East Asian mathematical journal Vol.23 No.1
In this paper we give a concrete computational method to characterize $S_3$-algebras.
Sardar Farhat Abbas,Taek-Soo Kim,Bum-Sung Kim 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.4
With the increase in global demand for highly functionalized materials, there is continued interest in exploiting the materialproperties of metals either individually or in the form of alloys. Copper–iron alloy is considered unique with its remarkablecombination of strength and high electrical conductivity. Due to the low cost of iron, this alloy is expected to replace alloyslike Cu–Ag and Cu–Nb. In order to explore the microstructural features, copper–iron alloy with three Different compositions(10, 30, and 50 at.% Fe) were prepared by a gas atomization process. A detailed microstructural characterization was performedusing scanning electron microscopy, X-ray diff raction, and electron backscattered diff raction. Spark plasma sinteringwas used to sinter the powders to evaluate their electrical conductivities. The mechanism of the microstructure formation isalso discussed in detail. As the Fe content increases, the Fe-rich phase changes its shape from spherical to irregular with aconcomitant sharp decrease in the electrical conductivity of the alloy.
Lattice Boltzmann Method을 이용한 적혈구의 정적인 모양과 동적변형에 대한 연구
Hassan Farhat,김용현(Y.H. Kim),이준상(J.S. Lee) 한국전산유체공학회 2008 한국전산유체공학회 학술대회논문집 Vol.2008 No.-
The dependence of the rheological properties of blood on shape, aggregation, and deformability of red blood cells (RBCs) has been investigated using hybrid systems by coupling fluid with solid models. We present a simple approach for simulating blood as a multi-component fluid, in which RBCs are modeled as droplets of acquired biconcave shape. We used lattice Boltzmann method (LBM) due to its excellent numerical stability as a simulation tool. The model enables us to control the droplet static shape by imposing non-isotropic surface tension force on the interface between the two components. The use of the proposed non-isotropic surface tension method is justified by the Norris hypothesis. This hypothesis states that the shape of the RBC is due to a non-uniform interfacial surface tension force acting on the RBC periphery. This force is caused by the unbalanced distribution of the lipid molecules on the surface of the RBC. We also used the same concept to investigate the dynamic shape change of the RBC while flowing through the microvasculature, and to explore the physics of the Fahraeus, and the Fahraeus-Lindqvist effects.
Artificial neural networks and aggregate consumption patterns in New Zealand
( Daniel Farhat ) 한양대학교 경제연구소 2014 JOURNAL OF ECONOMIC RESEARCH Vol.19 No.2
This study engineers a household sector where individuals process macroeconomic information to reproduce consumption spending patterns in New Zealand. To do this, heterogeneous artificial neural networks (ANNs) are trained to forecast changes in per worker consumption. In contrast to existing literature, results suggest that there exists a trained ANN that significantly outperforms a linear econometric model at out-of-sample forecasting. To improve the accuracy of ANNs using only in-sample information, methods for combining private knowledge into social knowledge are explored. For one type of ANN, relying on an expert is beneficial. For most ANN structures, weighting an individual``s forecast according to how frequently that individual``s ANN is a top performer during in-sample training produces more accurate social forecasts. By focusing only on recent periods, considering the severity of an individual``s errors in weighting their forecast is also beneficial. Possible avenues for incorporating ANN structures into artificial social simulation models of consumption are discussed.