The Experts below are selected from a list of 126 Experts worldwide ranked by ideXlab platform
Nor Aziah Buang - One of the best experts on this subject based on the ideXlab platform.
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A Review of the Properties and Applications of Poly (Methyl Methacrylate) (PMMA)
Polymer Reviews, 2015Co-Authors: Umar Ali, Khairil Juhanni Bt Abd Karim, Nor Aziah BuangAbstract:Advances in the use of poly (methyl methacrylate) (PMMA) have opened up a wide range of applications in the field of nanotechnology. The knowledge of the properties of PMMA has contributed a lot to the recent boosts in the synthesis, modification, and applications of the polymer. However, there is a need to condense these developments in the form of an article for better understanding and easy access. This review highlights the fundamental physical properties of PMMA, coupled with experimental evidence of its essential chemistry, such as solubility, hydrolysis, grafting, combustion reactions, reactions with amines, and thermal decomposition. The recent developments in the applications of PMMA in biomedical, optical, Solar, Sensors, battery electrolytes, nanotechnology, viscosity, pneumatic actuation, molecular separations, and polymer conductivity were also revealed.
Ali Alalili - One of the best experts on this subject based on the ideXlab platform.
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a hybrid Solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks
Applied Energy, 2017Co-Authors: Sajid Hussain, Ali AlaliliAbstract:Abstract Assessment of Solar potential over a location of interest is an important step towards the successful planning of renewable energy projects. However, Solar data are not available for every point of interest due to the absence of meteorological stations and sophisticated Solar Sensors, so Solar radiation has to be estimated using models. This paper presents a hybrid technique to improve the performance of a widely used modeling technique i.e. artificial neural network (ANN). Four different architectures of ANN, namely: multilayer perceptron (MLP), Adaptive neuro-fuzzy inference system (ANFIS), Nonlinear autoregressive recurrent exogenous neural network (NARX), and generalized regression neural networks (GRNN), are used in this study. A wavelet multiresolution analysis is applied to decompose the complex meteorological signals into relatively simple parts, wavelet sub-series, using discrete wavelet transformation (DWT) algorithm. The wavelet sub-series are modeled by the ANN models and reconstructed to estimate the original signal. Hence, enhancing the learning process of these models. Four meteorological parameters, namely: temperature (T), relative humidity (RH), wind speed (WS), and sunshine duration (SSD), are used to mode the global horizontal irradiation (GHI) over Abu Dhabi, the United Arab Emirates. The proposed approach is compared to standalone ANN models and validated using well-known statistical validation metrics including coefficient of determination (R 2 ), root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and t -statistics. In addition, wavelet cross spectrum (WCS) is used as a visual indicator of the model performance in time, frequency, and phase domains. The results show that using the proposed strategy considerably improves the modeling performance of the ANN with a maximum improvement of 6.84% in R 2 for MLP. In addition, minimum RMSE of 2.78% is observed for GRNN.
Luiz Machado - One of the best experts on this subject based on the ideXlab platform.
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comparison of artificial intelligence methods in estimation of daily global Solar radiation
Journal of Cleaner Production, 2018Co-Authors: Abbas Khosravi, R O Nunes, Mamdouh El Haj Assad, Luiz MachadoAbstract:Abstract Assessment of Solar potential over a location of interest is introduced as an important step for the successful planning of Solar energy systems (photovoltaic or thermal). Due to the absence of meteorological stations and sophisticated Solar Sensors, Solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate Solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global Solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R2) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m2/day), 0.9886 and 0.0608 (kWh/m2/day), respectively.
Francisco Javier Martinez-de-pison - One of the best experts on this subject based on the ideXlab platform.
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HAIS - An Algorithm Based on Satellite Observations to Quality Control Ground Solar Sensors: Analysis of Spanish Meteorological Networks
Lecture Notes in Computer Science, 2018Co-Authors: Ruben Urraca, J. Antonanzas, Andres Sanz-garcia, Alvaro Aldama, Francisco Javier Martinez-de-pisonAbstract:We present a hybrid quality control (QC) for identifying defects in ground Sensors of Solar radiation. The method combines a window function that flags potential defects in radiation time series with a visual decision support system that eases the detection of false alarms and the identification of the causes of the defects. The core of the algorithm is the window function that filters out groups of daily records where the errors of several radiation products, mainly satellite-based models, are greater than the typical values for that product, region and time of the year.
Tian Wei - One of the best experts on this subject based on the ideXlab platform.
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Signal Processing of Satellite Attitude Based on Artificial Neural Network
Journal of Shanghai Jiaotong University, 1999Co-Authors: Tian WeiAbstract:One of the difficult techniques of a remote sensing satellite is to make its attitude control system to a certain accuracy. Usually, the attitude of a satellite is determined by a single sensor. But in the case that a high accuracy of the attitude is required, we should use several Sensors and integrate them in order to process their data. A BP neural network with a feedback loop was proposed and designed for the satellite attitude measurement system which consists of inertial Sensors, infrared earth Sensors and Solar Sensors. It is shown that the effects of orbit periodic error of infrared earth sensor and shadow of sun on the accuracy of the measurement system can be restrained. The accuracy of the measurement system is improved a lot.
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Fault Diagnosis Technology for Satellite Attitude Measurement System
Journal of Shanghai Jiaotong University, 1999Co-Authors: Tian WeiAbstract:A kind of technology of fault diagnosis for the Sensors' hardware faults was studied. The technology is intended for an attitude measurement system of a remote sensing satellite, which consists of dynamically tuned gyroscopes (DTG), infrared earth Sensors and Solar Sensors. The formulas of the system were given. The types of the Sensors' faults were supposed. A method of fault diagnosis based on generalized simply Kalman filter (multiple filter) was applied for the system. As the Kalman filter is used for processing attitude information, the additional calculating amount of the method is limited. It is shown that this method is good in real time detecting and has a high rate of fault detection. The research is useful for improving the reliability of satellite attitude measurement system and for developing a high accuracy satellite.