Institution
Iran University of Science and Technology
Education•Tehran, Iran•
About: Iran University of Science and Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 12917 authors who have published 24965 publications receiving 372013 citations. The organization is also known as: Governmental Technical Institute & Advanced Art College.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a positive charged hybrid nanofiltration membrane was prepared by the incorporation of Triethylenetetetramine (TETA) functionalized multiwall carbon nanotube (MWCNT) into Polyethersulfone (PES) matrix.
101 citations
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TL;DR: This paper focuses on phase I monitoring of multivariate multiple linear regression profiles and develops four methods for this purpose and develops a diagnostic scheme to find the out-of-control samples.
Abstract: In some statistical process control applications, there are some correlated quality characteristics which can be modeled as linear functions of some explanatory variables. We refer to this structure as multivariate multiple linear regression profiles. When the correlation structure between quality characteristics is ignored and profiles are monitored separately then misleading results could be expected. Hence, developing methods to account for this multivariate structure is required. In this paper, we specifically focus on phase I monitoring of multivariate multiple linear regression profiles and develop four methods for this purpose. The performance of the developed methods is compared through simulation studies in terms of probability of a signal. In addition, a diagnostic scheme to find the out-of-control samples is developed. Finally, the application of the proposed methods is illustrated using a calibration application at the National Aeronautics and Space Administration (NASA) Langley Research Center. Copyright © 2009 John Wiley & Sons, Ltd.
101 citations
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TL;DR: Due to the highly amount of amino functional groups and magnetic properties of the synthesized nanobiocomposite, it was successfully utilized as a heterogeneous catalyst for the one-pot multicomponent synthesis of 1,4-dihydropyridines.
101 citations
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TL;DR: This study considers the use of artificial neural networks (ANNs) to predict the maximum dry density (MDD) and optimum moisture content (OMC) of soil-stabilizer mix and demonstrates that the accuracy of the proposed models is satisfactory as compared to the experimental results.
Abstract: This study considers the use of artificial neural networks (ANNs) to predict the maximum dry density (MDD) and optimum moisture content (OMC) of soil-stabilizer mix. Multilayer perceptron (MLP), one of the most widely used ANN architectures in the literature, is utilized to construct comprehensive and accurate models relating the MDD and OMC of stabilized soil to the properties of natural soil such as particle-size distribution, plasticity, linear shrinkage, and the type and quantity of stabilizing additives. Five ANN models are constructed using different combinations of the input parameters. Two separate sets of ANN prediction models, one for MDD and the other for OMC, and also a combined ANN model for multiple outputs are developed using the potentially influential input parameters. Relative-importance values of various inputs of the models are calculated to determine the significance of each of the predictor variables to MDD and OMC. Inferring the most relevant input parameters based on Garson's algorithm, modified ANN models are separately developed for MDD and OMC. The modified ANN models are utilized to introduce explicit formulations of MDD and OMC. A parametric study is also conducted to evaluate the sensitivity of MDD and OMC due to the variation of the most influencing input parameters. A comprehensive set of data including a wide range of soil types obtained from the previously published stabilization test results is used for training and testing the prediction models. The performance of ANN-based models is subsequently analyzed and compared in detail. The results demonstrate that the accuracy of the proposed models is satisfactory as compared to the experimental results.
101 citations
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TL;DR: In this article, the authors proposed a real-time voltage control algorithm for distribution networks with renewable distributed generations, where all the capacitors are equipped with Remote Terminal Unit (RTU) and completely accessible and controllable.
101 citations
Authors
Showing all 13049 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peter Hall | 132 | 1640 | 85019 |
Josep M. Guerrero | 110 | 1197 | 60890 |
Rahman Saidur | 97 | 576 | 34409 |
Victor C. M. Leung | 91 | 1585 | 40397 |
Mehdi Dehghan | 83 | 875 | 29225 |
Amir H. Gandomi | 67 | 375 | 22192 |
Toraj Mohammadi | 64 | 394 | 14043 |
Emil Björnson | 62 | 458 | 17954 |
Amir A. Zadpoor | 61 | 294 | 11653 |
Majid R. Ayatollahi | 60 | 373 | 10771 |
Ali Kaveh | 58 | 753 | 16647 |
David Andrew Barry | 57 | 462 | 13363 |
Miguel A. Mariño | 53 | 291 | 8304 |
Ali Saberi | 51 | 448 | 10959 |
Ali Maleki | 51 | 376 | 8853 |