Majlesi Journal of Electrical Engineering 2022-09-24T09:36:43+00:00 Dr. Hossein Emami Open Journal Systems <p>The scope of MJEE is ranging from mathematical foundation to practical engineering design in all areas of electrical engineering. The editorial board is international and original unpublished papers are welcome. The journal is devoted primarily to research papers, but very high quality survey and tutorial papers are also published. There is no publication charges for non-Iranian authors.</p> Sensing Behavior Study of Cobalt Zinc Ferrite Nanoparticles Against Acetone in Various Temperatures 2022-09-08T04:24:33+00:00 Alireza Ghasemi Hamid Reza Ebrahimi Mohsen Ashourian Hassan Karimi Maleh Gholam Reza Amiri <p>The Cobalt zinc ferrite nanoparticles with diameters less than 20 nm were prepared. By XRD (X-ray diffraction), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) the morphology and the structure of this ferrite were studied. The X-ray analysis shows the formation of manganese zinc ferrite in the spinel phase. SEM photograph is shown the spherical shape of nanoparticles. And the TEM conīŦrmed the nanoscale dimensions of the samples. The cobalt zinc ferrite nanoparticles crystallite sizes, calculated by the Debye-Scherer formula, were found near 13 nm. The sensitivity properties of this ferrite are investigated in a totally isolated plexi glass box. By injecting 1 mL of liquid and vaporizing it, we will have 200 ppm concentration of each sample in this box. Then the injected vapored sample in this box is exposed to the ferrite. After this step, the conductivity of the ferrite in a closed circuit was changed. By changing the sample type amount of this conductivity was varied. Six gases were tested in this project: ethanol, nitrile alcohol, dimethyl formamide, carbon tetrachloride, acetonitrile, and acetone. Among these samples, the carbon tetrachloride had the best sensitivity performance. Finally, the sensor equation for carbon tetrachloride was extracted by applying different concentrations of it from 20 to 200 ppm.</p> <p>&nbsp;</p> 2022-09-08T04:24:25+00:00 ##submission.copyrightStatement## Topology optimization of cyber network impacts on smart grid adequacy evaluation considering cyber-power interdependency 2022-09-08T04:25:26+00:00 Hossein Askarian Abyaneh <p class="MJEE-Abstract">Smart grid is comprised of two distinct and intricate cyber-power networks. In smart grids, cyber networks are employed to control, monitor, and protect various kinds of physical structures. Most of proposed methods have considered that cyber components will not fail. This clarification causes error in reliability study; namely in smart grids with advanced cyber network topologies. Based on how cyber failure affects the power system, cyber and power networks may have direct or indirect interdependency. This paper introduces a new analytical reliability assessment methodology, which considers impact of direct cyber network failures on power networks, effectively. The proposed method evaluates the smart grid reliability while taking power and cyber component (monitoring/control/protection devices) failures into account. In this paper, a new procedure based on changing cyber topology structure is suggested. In addition, an applicable cyber network structure is found and employed to improve smart grid reliability. The proposed method is applied to a realistic distribution system in Iran. The results prove that the study of both cyber and power effects on reliability assessment of smart grid are essential to be carried out by system operators. Therefore, an optimized cyber network configuration is introduced as a reliability improvement method.</p> 2021-11-16T00:00:00+00:00 ##submission.copyrightStatement## IMPACT OF THE PENETRATION OF RENEWABLE ENERGY ON DISTRIBUTED GENERATION SYSTEMS 2022-09-08T04:25:37+00:00 Oyinlolu Ayomidotun Odetoye Oghenewvogaga James Oghorada Adeleke Olusola Alimi Babatunde Adetokun Uchenna N Okeke Paul K Olulope, Dr. Matthew O. Olanrewaju John O. Onyemenam John O. Onyemenam <p><em>As the proportion of total generation by renewable sources compared to non-renewable sources increases, the relative inertial stability provided by large rotating generators in electricity grids is found to shrink and is not being replaced by sources such as photovoltaic and wind power, which are already known for their inherent variability. This leads to electricity generation systems being less stable, less flexible, and less adequate in applications with a high diversity factor, and literature shows that the penetration of renewable energy sources in distribution-generation/microgrid system frequently presents several technical and economic challenges in their usual applications. This work examines how increased renewable energy penetration impacts the distribution-generation system and suggests approaches and measures for tackling the challenges that are associated with it.</em></p> 2022-08-28T00:00:00+00:00 ##submission.copyrightStatement## Investigation of Stator Windings Looseness of Polyphase Induction Machine after Rewinded in Workshop: Numerical and Experimental Analysis 2022-09-08T04:25:47+00:00 V Sri Ram Prasad Prasad Kapu Varsha Singh, Dr. <p>Electrical machines, especially induction motors, are broadly employed in industry as they need low maintenance. In this article, the Experimental Modal Analysis (EMA) test plays a significant role in evaluating electrical machine vibrations. The hammer test is a popular EMA approach for locating the exact location of winding looseness. To acquire the modal parameters for validation with a theoretical and numerical model, the EMA is performed on the stator end winding structure. The EMA's Operational Deflection Shape (ODS) confirms the windings' precise deformation. The induction machine's driving end (DE) and non-driving end (NDE) windings are tested to EMA to evaluate the stator slot structure's looseness. The proposed technique was compared to the Finite Element Method (FEM) and theoretical calculations for verification.</p> 2022-08-24T00:00:00+00:00 ##submission.copyrightStatement## Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers 2022-09-08T04:25:53+00:00 Tifouti Issam Rahmouni Salah Meriane Brahim <p>Recently, many studies have examined filters for reducing or remove speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, Finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR)&nbsp; are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty.</p> 2022-08-28T00:00:00+00:00 ##submission.copyrightStatement## Single Image Super-Resolution Enhancement using Luminance Map and Atmospheric Light Removal 2022-09-08T04:26:04+00:00 Mohammad Amin Shayegan ; Samira Poormajidi <p>Image enhancement is used in many image processing applications such as medical diagnostics, satellite image analysis, surveillance cameras, etc. Super resolution attempts to reconstruct high resolution images from low resolution images and it can be considered as a preprocessing step for object recognition and image classification. Various algorithms have been introduced for single-image super resolution, but these algorithms often face important challenges such as poorly matching the reconstructed image with the original image, as well as the blurring of edges and texture details. The aim of this manuscript is to introduce a preprocessing operation to improve the performance of the super resolution process in natural images. In the proposed method, the low resolution input image is enhanced before entering the resolution change module. Calculating the brightness of the pixels in the image channels, creating the luminance map and removing atmospheric light, applying the transmittance map by using the luminance coefficients, and recovering the natural image in all three color channels are the above preprocessing steps. The proposed method succeeded in increasing the PSNR parameter by 4.35%, 10.62%, and 8.31%, as well as 0.23%, 3.10%, and 7.91% of the SSIM parameter for Set5, Set14, and BSD100 datasets compared to its closest state-of-the-art methods.</p> 2022-08-31T00:00:00+00:00 ##submission.copyrightStatement## Direct Matching Antennas in RF Energy Harvesting Systems: A Review 2022-09-17T12:37:58+00:00 Vahid Honarvar Farzad mohajeri <p>A historical review of Radio Frequency Energy Harvesting (RFEH) Rectenna (Rectifier Antenna) Systems without a matching network is performed, with emphasis on the antenna part. As the antenna, matching network and rectifier are the main parts of the rectenna systems, the reasons behind the elimination of the matching network are presented and different special antennas suitable for direct matching to the rectifier, without using a matching network, are reviewed. Since the diode in the rectifier is a nonlinear element, its input impedance is changed with varying operating conditions such as input power, frequency and output load impedance of the rectifier. So, it is a challenge for researchers to match the antenna impedance directly to the rectifier in variable operational conditions.</p> 2022-09-17T12:37:58+00:00 ##submission.copyrightStatement## Automatic Diagnosis of Breast Cancer in Histopathologic Images Based on Convolutional AutoEncoders and Reinforced Feature Selection 2022-09-19T08:45:30+00:00 Ali Abdulhussain Fadhil Miaad Adnan Hamza Radhi Mahmood Al-Mualm Mahmood Hasen Alubaidy Mohamed Salih Sarah Jaafar Saadoon <p>Breast cancer is one the most ubiquitous types of cancer which affect a considerable number of women around the globe. It is a malignant tumor, whose origin is in the glandular epithelium of the breast and causes serious health-related problems for patients. Although there is no known way of curing this disease, early detection of it can be very fruitful in terms of reducing the negative ramifications. Thus, accurate diagnosis of breast cancer based on automatic approaches is demanded immediately. Computer vision-based techniques in the analysis of medical images, especially histopathological images, have proved to be extremely performant. In this paper, we propose a novel approach for classifying malignant or non-malignant images. Our approach is based on the latent space embeddings learned by convolutional autoencoders. This network takes a histopathological image and learns to reconstruct it and by compressing the input into the latent space, we can obtain a compressed representation of the input. These embeddings are fed to a reinforcement learning-based feature selection module which extracts the best features for distinguishing the normal from the malicious images. We have evaluated our approach on a well-known dataset, named BreakHis, and used the K-Fold Cross Validation technique to obtain more reliable results. The accuracy, achieved by the proposed model, is 96.8% which exhibits great performance.</p> 2022-09-19T08:45:30+00:00 ##submission.copyrightStatement## Low Power Broadband sub-GHz CMOS LNA with 1 GHz Bandwidth for IoT Applications 2022-09-20T05:51:21+00:00 Sayed Vahid Mir-Moghtadaei Farshad Shirani Bidabadi <p>This paper presents a broadband low-power CMOS low noise amplifier (LNA) in 130 nm technology for sub-GHz Internet of Things (IoT) applications. The proposed circuit consists of a current reuse common source amplifier (CSA) in the forward path, and a positive simple transconductance amplifier (PSTA) in the feedback path. Theoretical calculation of the input admittance shows a positive part that presents a parallel inductance. This equivalent parallel inductance in the input can cancel out the input capacitance of CSA and electrostatic discharge (ESD) pad, enhancing the frequency bandwidth in the sub-GHz frequency band. Post-layout simulated including ESD pads and package model in 130 nm CMOS technology, LNA achieves a voltage gain of 16.5 dB in a frequency bandwidth of 50 MHz to 1.1 GHz, noise figure (NF) of less than 2.4 dB, input return loss (S11) of -11 dB, input third order intercept point (IIP3) of -11 dBm and 1 mW power consumption from a 1 V power supply, showing a good figure of merit compared to other works. The occupied core area is less than 0.002 mm<sup>2</sup>.</p> 2022-09-20T05:51:21+00:00 ##submission.copyrightStatement## Electricity Demand Prediction by a Transformer-Based Model 2022-09-24T09:36:43+00:00 Ahmed Mohammed Mahmood Musaddak Maher Abdul Zahra Waleed Hamed Bashar S. Bashar Alaa Hussein Abdulaal Taif Alawsi Ali Hussein Adhab <p>The frighteningly high levels of power consumption at present are caused mainly by the expanding global population and the accessibility of energy-hungry smart technologies. So far, various simulation tools, engineering- and AI-based methodologies have been utilized to anticipate power consumption effectively. While engineering approaches forecast using dynamic equations, AI-based methods forecast using historical data. The modeling of nonlinear electrical demand patterns is still lacking for durable solutions, however, as the available approaches are only effective for resolving transient dependencies. Furthermore, because they are only based on historical data, the current methodologies are static in nature. In this research, we present a system based on deep learning to anticipate power consumption while accounting for long-term historical relationships. In our approach, a transformer-based model is used for the prediction of electricity demand on data collected from the regional facilities in Iraq. According to the conducted experiments, our approach claims competitive performance, achieving an error rate of 2.0 in predicting 1-day-ahead of electricity demand in the test samples.</p> 2022-09-24T09:36:43+00:00 ##submission.copyrightStatement##