This research is devoted to design and implement a Supervisory Control and Data Acquisition system (SCADA) for monitoring and controlling the corrosion of a carbon steel pipe buried in soil. A smart technique equipped with a microcontroller, a collection of sensors and a communication system was applied to monitor and control the operation of an ICCP process for a carbon steel pipe. The integration of the built hardware, LabVIEW graphical programming and PC interface produces an effective SCADA system for two types of control namely: a Proportional Integral Derivative (PID) that supports a closed loop, and a traditional open loop control. Through this work, under environmental temperature of 30°C, an evaluation and comparison were done for two types of controls tested at low soil moisture (48%) and high soil moisture (80 %) to study the value of current, anode voltage, pipe to soil potential (PSP) and consumed power. The results show an decrease of 59.1% in consumed power when the moisture changes from the low to high level. It was reached that the closed loop controller PID is the best solution in terms of efficiency, reliability, fast response and power consumption.
Free Space Optical (FSO) technology offers highly directional, high bandwidth communication channels. This technology can provide fiber-like data rate over short distances. In order to improve security associated with data transmission in FSO networks, a secure communication method based on chaotic technique is presented. In this paper, we have turned our focus on a specific class of piece wise linear one-dimensional chaotic maps. Simulation results indicate that this approach has the advantage of possessing excellent correlation property. In this paper we examine the security vulnerabilities of single FSO links and propose a solution to this problem by implementing the chaotic signal generator “reconfigurable tent map”. As synchronizat
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The structure, optical, and electrical properties of SnSe and its application as photovoltaic device has been reported widely. The reasons for interest in SnSe due to the magnificent optoelectronic properties with other encouraging properties. The most applications that in this area are PV devices and batteries. In this study tin selenide structure, optical properties and surface morphology were investigated and studies. Thin-film of SnSe were deposit on p-Si substrates to establish a junction as solar cells. Different annealing temperatures (as prepared, 125,200, 275) °C effects on SnSe thin films were investigated. The structure properties of SnSe was studied through X-ray diffraction, and the results appears the increasing of the peaks
... Show MoreThe economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
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Copper Zinc Sulphide (Cu0.5Zn0.5S) alloy and thin films were fabricated in a vacuum. Nano crystallized (CZS) film with thick 450±20 nm was deposit at substrates glasses using thermal evaporation technique below ~ 2 × 10− 5 mbar vacuum to investigated the films structural, morphological and optical properties depended on annealing temperatures ( as-deposited, 423, 523 and 623) K for one hour. The influences annealed temperature on structurally besides morphologically characteristics on these films were investigated using XRD and AFM respectively. XRD confirms the formation a mixed hexagonal phase of CuS-ZnS in (102) direction with polycrystalline in nature having very fine crystallites size varying from (5.5-13.09) nm. AFM analys
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The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).
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Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Border
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In this work, a deep computational study has been conducted to assign several qualities for the graph . Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.