Majlesi Journal of Electrical Engineering Sliding Mode Contact Force Control of n-Dof Robotics by Force Estimation Volume 14 (2020) Issue 4, December 2020 2024 02 15 Sliding Mode Contact Force Control of n-Dof Robotics by Force Estimation 10.29252/mjee.14.4.1 EN Majid Namnabat Department of Control Engineering, College of Technical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran. Amir Hossein Zaeri Department of Electrical Engineering, Shahinshahr Branch, Islamic Azad University, Isfahan, Iran Mohammad Vahedi Department of Mechanical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran. Journal Article 2024 02 15 Control of the force exerted on an object is important for boosting system performance in robotics manipulators. Any undesired applied force may leave remarkable effects on the system, with the potential to damage the object. In addition, measuring external force is another challenge associated with such cases. Proposing an appropriate force estimation algorithm is a solution to overcome this deficiency. In this research, a control strategy is proposed to control the external force applied on the n-dof robotics. To eliminate force measurement in the controller, a force estimation strategy based on a disturbance observer is employed. Subsequently, a sliding-mode based control is implemented to cope with the force estimation error. The closed-loop stability of the system in the presence of estimated force is analytically considered. The proposed algorithm was implemented on piezoelectric actuators as the experimental setup. The experimental results confirm that by employing the proposed control scheme, precise force control is achievable. The force estimation algorithm can also suitably estimate external force.

Majlesi Journal of Electrical Engineering Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG Volume 14 (2020) Issue 4, December 2020 2024 02 15 Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG 10.29252/mjee.14.4.11 EN Mahtab Vaezi Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran. 0000-0002-9254-3584 Mehdi Nasri Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran. Journal Article 2024 02 15 Sleep is a normal state in humans and the subconscious level of brain activity increases during sleep. The brain plays a prominent role during sleep, so a variety of mental and brain-related diseases can be identified through sleep analysis. A complete sleep period according to the two world standards R&K and AASM consists of seven and five steps, respectively. To diagnose diseases through sleep, it is necessary to identify different stages of sleep because the disorder at each stage indicates a certain disease. On the other hand, efficient and useful features should be selected to increase the accuracy of sleep stage classification. In this paper, at first, different statistical, entropy, and chaotic features are extracted from sleep data. Afterwards, by introducing and using the Laplacian score selector, the best feature set is selected. At the end, some conventional classification algorithms such as SVM, ANN and KNN are used to classify different sleep stages. Simulation results confirms the superiority of the proposed method based on the classification results. With the proposed algorithm, 2, 3, 4, 5 and 6 stages of sleep were classified by SVM and decision tree with 98.0%, 98.0%, 97.3%, 96.6%, and 95.0% accuracy, which are more superior to previous method’s results.

Majlesi Journal of Electrical Engineering Development of a Neutron Radiography System based on a 10 MeV Electron Linac Volume 14 (2020) Issue 4, December 2020 2024 02 15 Development of a Neutron Radiography System based on a 10 MeV Electron Linac 10.29252/mjee.14.4.21 EN Jacob Fantidis Department of Electrical Engineering-Department of Physics, International Hellenic University, Agios Loukas, 65404 Kavala, Greece. G Nicolaou Laboratory of Nuclear Technology, Department of Electrical and Computer Engineering, ‘Democritus’ University of Thrace, Xanthi, Greece. Journal Article 2024 02 15 A thermal neutron radiography unit using the neutrons which emits a 10 MeV electron linac compact has been designed and simulated via MCNPX Monte Carlo code. The facility was carried out for an extensive range of values for the collimator ratio L/D, the main parameter which describes the quality of the produced radiographic images. The results show that the presented facility provides high thermal neutron flux; while with the use of single sapphire filter fulfills all the suggested values which characterize a high quality thermal neutron radiography system. A comparison with other similar facilities indicates that the use of a photoneutron source using a 10 MeV electrons beam is a useful substitutional for radiographic purposes.

Majlesi Journal of Electrical Engineering Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm Volume 14 (2020) Issue 4, December 2020 2024 02 15 Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm 10.29252/mjee.14.4.29 EN Rasool Ebrahimi Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran. 0000-0003-2774-4694 Ghazanfar Shahgholian Department of Electronics Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Bahador Fani Department of Electronics Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Journal Article 2024 02 15 Modern distribution system including Distributed Generation (DG) requires reliable and fast islanding detection algorithms in order to determine the grid status. In this paper, a new multi-model classification-based method is proposed, in order to detect islanding condition for photovoltaic units. Decision tree is chosen as the classification algorithm to classify input feature vectors. The final result is based on voting among three decision tree algorithms. First order derivatives of electrical parameters are employed to construct feature vectors. To cover intermittent nature of renewable sources, different generating states for PV unit are assumed. Probable events are simulated under different system operating states to generate classification data set. The pro­po­sed method is tested on typical distribution system including the PV unit, different loads, and synchronous generator. This study sh­o­wed that this method succeeds in highly fast islanding det­ec­tion. This quick response can be used in micro-grid application as well as anti-islanding strategy. The results revealed that the proposed vot­ing-base algorithm could classify instances with very high acc­ur­a­cy which leads to reliable operation of distributed gene­rat­i­on units.

Majlesi Journal of Electrical Engineering Emotional Speech Recognition using Deep Learning Volume 14 (2020) Issue 4, December 2020 2024 02 15 Emotional Speech Recognition using Deep Learning 10.29252/mjee.14.4.39 EN Othman Khalifa International Islamic University Malaysia, Electrical and Computer Engineering, Malaysia. M. Alhamada Aisha Abdalla Journal Article 2024 02 15 Emotion speech recognition (SER) is to study the formation and change of speaker’s emotional state from his/her speech signal. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. The reliability of the current speech emotion recognition systems is far from being achieved. However, this is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. In this paper, an overview of Deep Learning techniques that could be used in Emotional Speech recognition is presented. Different extracted features like MFCC as well as feature classifications methods like HMM, GMM, LTSTM and ANN were discussion.  Also, the review covers databases used, emotions extracted, contributions made toward speech emotion recognition

Majlesi Journal of Electrical Engineering A Low-power, CMOS Optical Communication Receiver System for 5Gbps Applications based on RGC Structure Volume 14 (2020) Issue 4, December 2020 2024 02 15 A Low-power, CMOS Optical Communication Receiver System for 5Gbps Applications based on RGC Structure 10.29252/mjee.14.4.57 EN Sima Honarmand Department of Electrical Engineering, Beyza Branch, Islamic Azad University, Beyza, Iran. Soorena Zohoori Department of Electrical Engineering, Beyza Branch, Islamic Azad University, Beyza, Iran. Kavoos Abbasi Department of Physics, College of Science, Yasouj University, Yasouj, Iran Journal Article 2024 02 15 An optical communication receiver system is presented in this research using 65nm CMOS, which consists of three low-power active differential stages as Limiting Amplifier (LA) following an ultra-low-power RGC-Based Transimpedance Amplifier (RB-TIA). The presented active circuit of the RB-TIA is followed by a gain stage that extends the -3dB frequency of the circuit by creating a resonance for the load capacitance. Thus, needless of consuming extra power, a wide-bandwidth circuit has been designed. In addition, employing active-inductor loads within the LA stages enables obtaining a 5Gbps receiver system. The RB-TIA consumes 573µW and provides 3.52GHz frequency, while the complete optical receiver consumes only 4.76mW power to provide -3dB frequency of 3.5GHz and high gain of 80dB (10’000). The circuits have been mathematically presented and discussed, and simulations have justified the presented circuit design.

Majlesi Journal of Electrical Engineering Haptic Interface Controller Design using Intelligent Techniques Volume 14 (2020) Issue 4, December 2020 2024 02 15 Haptic Interface Controller Design using Intelligent Techniques 10.29252/mjee.14.4.67 EN Naveen Kumar Department of Electrical Engineering, NIT Kurukshetra, India. Jyoti Ohri National Institute of Technology Kurukshetra, Kurukshetra, 136119, India Journal Article 2024 02 15 Haptic technology has enormous applications in several fields from medical, military, and in our day-to-day life’s products including video games, smartphones, and smart cities. The Haptic Interface Controller (HIC), a key circuitry for interaction between the user and the virtual world, has two main control issues: stability and transparency. These two issues are complementary to each other i.e. emphasis on one will degrade the other and vice-versa. To address this, intelligent control techniques including Genetic Algorithm (GA), Feed-Forward Neural Network (FFNN), and Fuzzy Logic Control (FLC) have been used in design of the HIC. To ensure the performance in real-time, in system parametric uncertainty and delay have been added while designing the HIC so that a balance could be maintained between the two issues.

Majlesi Journal of Electrical Engineering Design, Optimization and Prototype of a Multi-Phase Fractional Slot Concentrated Windings Surface Mounted on Permanent Magnet Machine Volume 14 (2020) Issue 4, December 2020 2024 02 15 Design, Optimization and Prototype of a Multi-Phase Fractional Slot Concentrated Windings Surface Mounted on Permanent Magnet Machine 10.29252/mjee.14.4.75 EN Amir Nekoubin Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran. Jafar Soltani Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran. Milad Dowlatshahi Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran. Journal Article 2024 02 15 The multi-phase permanent-magnet motors are suitable choices for certain purposes like aircrafts, marine, and electric vehicles due to the fault tolerance and high-power density capabilities. The paper aims to design and prototype an optimized five-phase fractional slot concentrated windings surface mounted permanent magnet motor. To optimize the designed multi-phase motor, a multi-objective optimization technique based on the genetic algorithm method has been applied. The machine design objectives are to minimize mass and loss, subsequently, to determine the best choice of the designed machine parameters. Afterwards, 2-Dimensional Finite Element Method (2D-FEM) has been used to verify the performance of the optimized machine. Finally, the optimized machine has been prototyped. The results of the prototyped machine have validated the results of the theatrical analyses of the machine, and accurate consideration of the parameters improved the performance of the machine.

Majlesi Journal of Electrical Engineering Improving Person Re-Identification Rate in Security Cameras by Orthogonal Moments and a Distance-based Criterion Volume 14 (2020) Issue 4, December 2020 2024 02 15 Improving Person Re-Identification Rate in Security Cameras by Orthogonal Moments and a Distance-based Criterion 10.29252/mjee.14.4.85 EN Ali Dadkhah Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Saeed Nasri Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Journal Article 2024 02 15 Surveillance and security cameras help security forces in public places such as airports, railway stations, universities and office buildings to perform high-level surveillance tasks such as detecting suspicious activity or anticipating undesirable events. Re-Identification (Re-ID) is defined as the process of communicating between images of the person in different cameras in a surveillance environment. Changing the field of view of any camera presents challenges such as changing body posture, changing brightness, noise and blockage. This article focuses on extracting the most distinctive features to overcome these challenges. The features of Hu moment, Zernike moment in 9th order and Legendre moment in 9th order for each image are extracted and merged into a single feature vector to form a single feature vector for each image. Principal Component Analysis (PCA) was used to reduce the vector dimensionality and finally the Mahalanobis distance criterion was used for identification. The proposed method in the VIPeR database has achieved a re-ID rate of 96.5. Although the presented method is simple, the outcome has been superior compared to many of the state-of-the-art methods.

Majlesi Journal of Electrical Engineering Optical Signal Transmission through Masked Aperture to Extend the Depth of Focus in Optical Coherence Tomography Volume 14 (2020) Issue 4, December 2020 2024 02 15 Optical Signal Transmission through Masked Aperture to Extend the Depth of Focus in Optical Coherence Tomography 10.29252/mjee.14.4.93 EN Pawan Tiwari Department of Physics, Birla Institute of Technology Mesra, Ranchi, India. 0000-0003-0239-2071 K. P. S. Parmar Department of Physics, University of Petroleum and Energy Studies, Dehradun, India. Suman Pandey Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea. Journal Article 2024 02 15 Optical Coherence Tomography (OCT) imaging technique has emerged as a non- or minimally invasive modality in the clinical pathogenesis such as deep tissue examining and optical biopsy etc. The OCT imaging increases the Depth of Focus (DoF) by devising mechanisms to increase an Optical Transfer Function (OTF) of the imaging system. This is achieved through an apodization technique on the surface of lens in conjugation with the femtosecond Bessel-type laser beam. An investigation on postulation of OTF through a masked aperture, or specifically a micro-dot is investigated to measure variations of intensity profile at the optical coordinates in the radial as well as axial directions. The intensity variations in the radial and axial coordinates are calibrated to obtain the information, which significantly helps in devising of OCT imaging system. A theoretical investigation of OTF matching the experimental relationship between spot size and DoF in response to obscuration ratio is presented in this paper. This mathematical approach could be applied to different types of masking functions by meticulously exploring the parameters of optical coordinates.

Majlesi Journal of Electrical Engineering Sophisticated Microgrid Communication System Management Volume 14 (2020) Issue 4, December 2020 2024 02 15 Sophisticated Microgrid Communication System Management 10.29252/mjee.14.4.123 EN Alaa Naji Al Hussein Directorate General of Education in Holy Karbala Province, Iraq. Maytham Abbas Department of Chemical engineering, College of engineering, University of Al-Qadisiyah, Iraq. Emad Jadeen Alshebaney Department of Chemical engineering, College of engineering, University of Al-Qadisiyah, Iraq. Mohammed Madhi Janabi Directorate General of Education in Al-Qadisiyah Province, Iraq. Journal Article 2024 02 15 Conversation and assurance issues play a crucial function when talking regarding to the wise grid. This particular paper presents opportunities of testing power framework assurance transfers and correspondence standards for smart supply. A depiction in the Smart Grid lab hardware and the key protection devices is usually presented in the paper. Further employ cases and uses offered by the Labrador equipment are referred to and the possibilities by dynamically setting up devices and program interaction are demonstrated. Ideas for the mix of checked and controllable decentralized vitality sources are demonstrated the network capacity and Quality of Services (QoS) are tested and evaluated.  By implementing adaptive modulation scheme, the served users were increased by 10% at heavy Traffic Load (TL).

Majlesi Journal of Electrical Engineering Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks Volume 14 (2020) Issue 4, December 2020 2024 02 15 Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks 10.29252/mjee.14.4.133 EN Shoorangiz Shams Shamsabad Farahani Department of Electrical Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran. Mohammad Mahdi Arefi Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, 71348-51154 Shiraz, Iran. Amir Hossein Zaeri Department of Electrical Engineering, Shahinshahr Branch, Islamic Azad University, Isfahan, Iran Journal Article 2024 02 15 Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by artifacts. Given the importance and the need for an automated method to remove artifacts, in this paper some intelligent automated methods are proposed which are composed of three main parts as extraction of effective input, filtering and filter optimization. Wavelet transform is utilized to extract the effective input, and the wavelet approximation coefficients are used as an effective input signal. In addition, Radial Basis Function Neural Network (RBFNN) has been used for filtering. The appropriate number of RBFs has been selected using extensive simulations, and the optimal value​​ of spread parameter has been achieved by Bees algorithm (BA). Finally, the proposed artifact removal schemes have been evaluated on some real contaminated EEG signals in Mashad Ghaem hospital database. The results show that the proposed artifact removal schemes are able to effectively remove artifacts from EEG signals with little underlying brain signal distortion.

Majlesi Journal of Electrical Engineering Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System Volume 14 (2020) Issue 4, December 2020 2024 02 15 Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System 10.29252/mjee.14.4.97 EN Gopisetti Manikanta Department of Engineering Electrical & Electronics, ASET, Amity University, Uttar Pradesh, Noida, India. Ashish Mani Amity University Uttar Pradesh, India. Hemender Pal Singh Department of Engineering Electrical & Electronics, ASET, Amity University, Uttar Pradesh, Noida, India. Devendra Kumar Chaturvedi Department of Engineering Electrical, F.O.E, Dayalbagh Educational Institute (Deemed University), Dayalbagh, Agra, India. Journal Article 2024 02 15 Distribution system supplies power to variety of load depending upon the consumer’s demand, which is increasing day by day and lead to high power losses and poor voltage regulation. The increase in demand can be met by integrating Distributed Generators (DG) into the distribution system. Optimal location and capacity of DG plays an important role in distribution network to minimize the power losses. Some researchers have studied this important optimization problem with constant power load which is independent of voltage. However, majority of consumers at load center uses voltage dependent load models, which are primarily dependent on magnitude of supply voltage. In practical distribution network, the assumption of constant power load can significantly affect the location and size of DG, which in turn can lead to higher power losses and poor voltage regulation. In this study, an investigation has been performed to find the increase in power loss due to the use of inappropriate load models, while solving the optimization problem. Furthermore, an attempt has been made in this study to reduce power losses occurring in large test bus systems with loads being dependent on voltage rather than the constant power load. Different test cases are created to analyse the power losses with appropriate load model and in-appropriate load model (constant power load model). The load at distribution network is not mainly dependent on any single type of load model, it is a combination of all load models.  In this study, a class of mix load viz., combination of residential, industrial, constant power, and commercial load, is also considered. In order to solve this critical combinatorial optimization problem with voltage dependent load model, which requires an extensive search, Adaptive Quantum inspired Evolutionary Algorithm (AQiEA) is used. The proposed algorithm uses entanglement and superposition principles, which does not require an operator to avoid premature convergence and tuning parameters for improving the convergence rate. A Quantum Rotation inspired Adaptive Crossover operator has been used as a variation operator for a better convergence. The effectiveness of AQiEA is demonstrated and computer simulations are carried out on two standard benchmark large test bus systems viz., 85 bus system and 118 bus system. In addition to AQiEA, four other algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), and Ecogeography-based Optimization (EBO) with Classification based on Multiple Association Rules (CMAR)) have also been employed for comparison. Tabulated results show that the location and size of DGs determined using in-appropriate load model (constant power load model) has significantly high power losses when applied in distribution system with different load model (other voltage dependent load models) as compared with the location and size of DGs determined using the appropriate load model. Experimental results indicate that AQiEA has a better performance compared to other algorithms which are available in the literature.

Majlesi Journal of Electrical Engineering Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques Volume 14 (2020) Issue 4, December 2020 2024 02 15 Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques 10.29252/mjee.14.4.145 EN Oryza Wisesa Department of Electrical Engineerng, Universitas Mercu Buana, Jakarta, Indonesia. Andi Andriansyah Department of Electrical Engineerng, Universitas Mercu Buana, Jakarta, Indonesia. Osamah Khalaf College of Information Engineering, Al-Nahrain University, Baghdad, Iraq. Journal Article 2024 02 15 Sales prediction analysis requires intelligent data mining techniques with accurate prediction models and high reliability. In most cases, business highly relies on information as well as demand forecast of the sales trends. This research uses B2B sales data for analysis. The B2B data could provide information on how telecommunication company should manage its sales team, products, and budgeting flows. The accurate estimates enable Telecommunication company to survive the market war and increase with market growth. Comprehensible predictive models were studied and analyzed using a technique of machine learning to improve the prediction of the future sale. It is hard to cope with big data and sale prediction accuracy if the system of traditional forecast is used. In this study, machine learning technique was also used to analyze the reliability of B2B sales. In addition, at the end of this research, other measures and techniques used to predict sales were introduced. The predictive model with best performance evaluation is recommended to forecast the trending B2B sales. The study results are put into an order of reliability and accuracy of the best method to predict and forecast including estimation, evaluation, and transformation. The best performance model found was Gradient Boost Algorithm. The result form graph the data close together from beginning till end of data target MSE and MAPE result are the best result than other method, MSE =24.743.000.000,00 and MAPE =0,18. This model performed maximum accuracy in predicting and forecasting of the future B2B sales.