Institution
Universiti Teknologi Malaysia
Education•Johor Bahru, Malaysia•
About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.
Topics: Membrane, Adsorption, Control theory, Catalysis, Antenna (radio)
Papers published on a yearly basis
Papers
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TL;DR: In this article, a two-stage methodology was adopted to distinguish pixel and sub-pixel targets in the satellite images, where the first stage used Continuum Removal (CR) spectral mapping tool and Independent Components Analysis (ICA) technique were applied to Landsat-8 and ASTER spectral bands to map the pixels related to poorly exposed lithological units.
140 citations
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TL;DR: The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach and revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock.
Abstract: Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases.
140 citations
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01 Dec 2010TL;DR: A MATLAB Simulink simulator for photovoltaic (PV) system using the two-diode model to represent the PV cell which is known to have better accuracy at low irradiance level which allows for a more accurate prediction of PV system performance.
Abstract: This paper proposes a MATLAB Simulink simulator for photovoltaic (PV) system. The main contribution of this work is the utilization of the two-diode model to represent the PV cell. This model is known to have better accuracy at low irradiance level which allows for a more accurate prediction of PV system performance. To reduce computational time, the input parameters are reduced to four and the values of Rp and Rs are estimated by an efficient iteration method. Furthermore, all the inputs to the simulator are information available on standard PV module datasheet. The simulator supports large array simulation that can be interfaced with MPPT algorithms and power electronic converters. The accurateness of the simulator is verified by applying the model to two PV modules. It is envisaged that the proposed work can be very useful for PV professionals who require simple, fast and accurate PV simulator to design their systems.
140 citations
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TL;DR: The development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp, found the GA-ANN model to be better than ANN model in estimating AOp induced by blasting.
Abstract: Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination (R2) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R2, 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.
139 citations
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TL;DR: In this article, a new type of thin film nanocomposite (TFN) forward osmosis (FO) membranes was prepared by incorporating different quantities of halloysite nanotubes (HNTs) into the polyamide layer via interfacial polymerization.
139 citations
Authors
Showing all 21852 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xin Li | 114 | 2778 | 71389 |
Muhammad Imran | 94 | 3053 | 51728 |
Ahmad Fauzi Ismail | 93 | 1357 | 40853 |
Bin Tean Teh | 92 | 471 | 33359 |
Muhammad Farooq | 92 | 1341 | 37533 |
M. A. Shah | 92 | 583 | 37099 |
Takeshi Matsuura | 85 | 540 | 26188 |
Peter Willett | 76 | 479 | 29037 |
Peter C. Searson | 74 | 374 | 21806 |
Ozgur Kisi | 73 | 478 | 19433 |
Imran Ali | 72 | 300 | 19878 |
S.M. Sapuan | 70 | 713 | 19175 |
Peter J. Fleming | 66 | 529 | 24395 |
Mohammad Jawaid | 65 | 503 | 19471 |
Muhammad Tahir | 65 | 1636 | 23892 |