WOS - Web of Science
Permanent URI for this collectionhttps://acikarsiv.thk.edu.tr/handle/123456789/2552
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Publication Smart prospects for solar-based cooling and heating systems in the Middle East and Turkey(IEEE, 2022) Baba, Abdullatif; Al Shehabi, Shadi; Bonny, M. Talal; Baba, Abdullatif; Turkish Aeronautical Association; Turk Hava Kurumu University; University of SharjahSolar-based systems represent the focal interest field for most of the newest studies of renewable energy. Solar heating and cooling systems convert thermal energy coming from the sun into electricity or heat to provide residential, commercial, and industrial areas with hot water, or to retain a specific required temperature. These technologies replace the classical fossil energy sources that are characterized by their damaging environmental impacts as well as their expensive costs. In this paper, we look at the ambitious solar projects that are already established or will be constructed in different countries of the ME and Turkey. Then, we discuss in brief most of the available technologies that rely on solar-based approaches for heating and cooling purposes. To overcome some challenges that may affect the performance of solar plants like cloudy or night environments, smart management techniques for interconnected units, or the cross-border power units of neighbored countries, are presented here and clarified by a case study. Finally, we conclude with a summary describing the most important features of our paper.Publication Electricity-consuming forecasting by using a self-tuned ANN-based adaptable predictor(ELSEVIER SCIENCE SA, 2022) Baba, Abdullatif; Baba, Abdullatif; Turkish Aeronautical Association; Turk Hava Kurumu UniversityAccurate forecasting of power consumption is an essential and modern approach for planning smart infrastructural projects that are required to overcome the future challenges of power markets. In this context, a new design of a self-tuned ANN-based adaptable predictor is presented in this paper. At first, the main design of the original adaptable predictor is clarified including its new architecture that partially relies on the Hebbian law, its training process is also explained as well as the dataset which is used for training. Then, all the details of the self-tuning-based technique are explained with all the relevant results that prove its high capability to produce more accurate forecasting outcomes. The impact of our suggested approach is introduced by explaining two different practical examples that employ the K-means clustering algorithm, and the genetic algorithm when it is used to optimize the operating/outage schedule for a group of local solar units, respectively.Publication Advanced AI-based techniques to predict daily energy consumption: A case study(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Baba, Abdullatif; Baba, Abdullatif; Turkish Aeronautical Association; Turk Hava Kurumu UniversityIn this paper, we compare the efficiency of three different techniques used to predict the daily power consumption for a local industrial region (the studied case). At first, a variant of the Multiple Model Particle Filter is suggested as a probabilistic approach. Then, two different ANNs with one and two hidden layers respectively are designed and tested. Finally, we demonstrate a developed ANN-based design that has the ability to adapt its own structure according to the historical fluctuations provided by a given dataset that contains the consumed power for the same regarded region between 2011 and 2015; 1825 days. The potential of AI-based techniques will be emphasized by summarizing a complement heuristic study that employs the genetic algorithm to suggest an optimal outage schedule for the generators supplying the upper-mentioned region to accomplish maintenance activities that could be needed from time to time or to rest some of the units if the predicted consumption for a given period doesn't require the total produced power.Publication FPGA-based parallel implementation to classify Hyperspectral images by using a Convolutional Neural Network(ELSEVIER, 2023) Baba, Abdullatif; Bonny, Talal; Baba, Abdullatif; Kuwait College Science & Technology; Turkish Aeronautical Association; Turk Hava Kurumu University; University of SharjahThanks to its richness in extractable features, Hyperspectral images (HSI) find an accelerated use in medical, industrial, agricultural, and environmental fields. In this paper, we present a wavelet-based reduction technique that creates a Hypercube containing the most significant features extracted from the original HSI and representing a multi-dimensional array that is utilized for training a Convolutional Neural Network (CNN), which is designed here to classify different types of surfaces or materials. The performance of this approach is tested and proved using two distinct datasets. Then, we compare the same approach with the PCA, a widely used reduction method. The most important contribution of this paper is the implementation of an FPGA-based parallel accelerator to train the same suggested CNN in only 10% of the computational time compared to the classical CPU-based techniques. The Microblaze will be explained and exploited here to play the role of an embedded microprocessor.Publication Voice encryption using a unified hyper-chaotic system(SPRINGER, 2023) Bonny, Talal; Al Nassan, Wafaa; Baba, Abdullatif; Baba, Abdullatif; University of Sharjah; Turk Hava Kurumu University; Turkish Aeronautical AssociationA Chaos-based cryptosystem is a vital method to enhance information protection in communication systems. The previous works have addressed this topic either by using highly complicated algorithms that are difficult to apply in practice or have a few encryption keys. This paper presents a new, highly secure chaos-based secure communication system that combines a conventional cryptography algorithm with two levels of chaotic masking technique. Furthermore, to enhance the security level, we employ the characteristic of a unified hyper-chaotic system to generate three different types of attractors. A Simulink of the stated system is implemented using MATLAB SIMULINK (R2013) to transmit a voice signal. Several testing methods such as power spectral density, spectrogram, histogram analysis, key sensitivity, correlation coefficient, signal to noise ratio (SNR), Percent Residual Deviation (PRD) are carried out to evaluate the quality of the proposed algorithm in several domains, time, frequency, and statistics. The simulation and comparison results demonstrate the high efficiency of the suggested cryptosystem and robustness against various cryptographic attacks.