Crystalline silicon (c-Si) has low optical absorption due to its high surface reflection of incident light. Nanotexturing of c-Si which produces black silicon (b-Si) offers a promising solution. In this work, effect of H2O2 concentrations towards surface morphological and optical properties of b-Si fabricated by two-step silver-assisted wet chemical etching (Ag-based two-step MACE) for potential photovoltaic (PV) applications is presented. The method involves a 30 s deposition of silver nanoparticles (Ag NPs) in an aqueous solution of AgNO3:HF (5:6) and an optimized etching in HF:H2O2:DI H2O solution under 0.62 M, 1.85 M, 2.47 M, and 3.7 M concentrations of H2O2 at 5 M HF. On the b-Si, nanowires with 250-950 nm heights and an average diameter of 150-280 nm are obtained. Low concentrations of H2O2 result in denser nanowires with an average length of 900-950 nm and diameters of about 150-190 nm. The b-Si exhibit outstanding broadband antireflection due to the refractive index grading effect represented as WAR within the 300-1100 nm wavelength region. B-Si obtained after etching in a solution with 0.62 M concentration of H2O2, demonstrate WAR of 7.5%. WAR of 7.5% results in an absorption of up to 95.5 % at a wavelength of 600 nm. The enhanced broadband light absorption yields maximum potential short-circuit current density (Jsc(max)) of up to 38.2 mA/cm2, or 45.2% enhancement compared to the planar c-Si reference.
This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
Moreover, the proposed controller i
... Show MoreThe drones have become the focus of researchers’ attention because they enter into many details of life. The Tri-copter was chosen because it combines the advantages of the quadcopter in stability and manoeuvrability quickly. In this paper, the nonlinear Tri-copter model is entirely derived and applied three controllers; Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID), and Nonlinear PID (NLPID). The tuning process for the controllers’ parameters had been tuned by using the Grey Wolf Optimization (GWO) algorithm. Then the results obtained had been compared. Where the improvement rate for the Tri-copter model of the nonlinear controller (NLPID) if compared with
The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present s
... Show MoreIn this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.
Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over
... Show MoreRadiation measuring devices need to periodic calibration process to examine their sensitivity and the extent of the response. This study is used to evaluate the radiation doses of the workers in the laboratories of the Directorate of Safety as a result of the use of point sources in calibrating of the devices in two ways, the first is the direct measurement by the FAG device and the others using RESRAD and RAD PRO programs. The total doses values using FAG were (2.57 μSv/y, 102.3 μSv/y and 20.75 μSv/y for TLD laboratory, Gamma spectroscopy analyses (GSA) laboratory and equipment store respectively, and the total doses that calculated using RESRAD and RAD PRO were 1.518 μSv/y, 76.65 μSv/y and 21.2 μSv/y for the above laboratories. t
... Show MoreThis work, deals with Kumaraswamy distribution. Kumaraswamy (1976, 1978) showed well known probability distribution functions such as the normal, beta and log-normal but in (1980) Kumaraswamy developed a more general probability density function for double bounded random processes, which is known as Kumaraswamy’s distribution. Classical maximum likelihood and Bayes methods estimator are used to estimate the unknown shape parameter (b). Reliability function are obtained using symmetric loss functions by using three types of informative priors two single priors and one double prior. In addition, a comparison is made for the performance of these estimators with respect to the numerical solution which are found using expansion method. The
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