Majlesi Journal of Electrical Engineering
Volume:17 Issue: 3, Sep 2023

The focus is on the longevity and energy efficiency of wireless sensor networks (WSN). WSNs face many obstacles in terms of data transmission. WSNs face difficulties in reducing energy output and shortening life cycles, including node configuration, leader selection, and optimal routing selection. The provisioning of nodes, selection of cluster leaders, and optimal paths have all been recommended using many current methods. However, none of the currently used methods yield sufficient grid energy optimization results. Therefore, this study proposes a modified Moth Flame Optimization Algorithm (MFOOA). Nature passed it on to us. The main inspiration for this optimizer is the lateral flight pattern used by moths in nature. At night, the moth maintains a constant angle to the moon. This is a particularly efficient way to drive long distances in a straight line. Nevertheless, artificial light is everywhere around these amazing creatures, encircling them in a fruitless and deadly spiral. Here, this behavior is theoretically modeled for optimization. The suggested program places the sensor nodes using the flame optimization technique. These sensor nodes might be either dynamic or static depending on the network scenario. The cluster head and the optimum route are chosen using this technique. Within the predetermined search space, it also does phase balancing between the exploration and development phases. In terms of residual energy, sensor node lifetime, used energy, end-to-end latency, and a maximum number of cycles, it differs from current classical and swarm intelligence (SI) techniques. According to the results, MFOOA is superior to its counterpart.

This paper proposes a novel method for rapidly and accurately detecting multiple sclerosis (MS) lesions and analyzing the progression of lesions and the disease based on differences between histograms of hemispheres and volumetric changes in brain regions over time. The brightness and contrast of pixels are first improved, and MRI slices are then analyzed to detect and eliminate the effects of motion artifacts while imaging. However, an accurate diagnosis tracks changes in volumes of brain regions caused by plaques emerging on brain MRIs in white matter, gray matter, and cerebrospinal fluid (CSF) and the concurrent analysis of differences between histograms of hemispheres. The marker-controlled watershed algorithm was employed to extract MS lesions and plaques. Various MRI centers differ in imaging diameters for which there are no unified standards, leading to different MRI slices. Hence, an individual's two MRI slices of two different occasions are not comparable. Measuring the brain volume can make the proposed method independent of the imaging diameter. This study analyzed the patients with at least three imaging records in the archives of imaging centers. The images were collected from Pars MRI Center and Hajar Hospital MRI Center in Shahrekord, Chararmahal and Bakhtiari Province, Iran. Both centers used Avanto MRI devices and performed imaging at 1 T and 1.5 T, respectively.

Renewable energy sources, such as wind and solar, are becoming increasingly popular due to their environmentally friendly and sustainable nature. However, one major challenge associated with these systems is their intermittent nature, which makes it difficult to rely on them as a consistent source of energy. To address this challenge, researchers have developed a combined system that incorporates wind and solar resources with a battery as a storage device, which can provide a sample load pattern independent of the grid. The primary objective of this system is to determine the optimal economic combination of these resources, which can ensure a reliable and consistent supply of electricity. To achieve this objective, the Cuckoo Optimization Algorithm (COA), a metaheuristic optimization algorithm, has been used to optimize the system. The objective function has been implemented in accordance with the constraints, and the results provide insight into the optimal combination of resources. This paper provides a comprehensive analysis of the design and optimization of a wind-solar hybrid energy system with battery storage, using the COA, as well as the results of this analysis. The outcomes indicated that the optimal hybrid system model may be able to reduce system costs by 10–25%. This research's findings can be used to inform the design of sustainable and dependable renewable energy systems.

This study presents a photonic bandgap formation in a non-uniform 2D photonic crystal structure with low index rods relative to the air background. In order to find an absolute photonic band gap formation, photonic lattices of two non-uniform square and triangular configurations with different rod radiuses and ring-shaped rods are studied. Based on PWE simulations, it was possible to achieve PBGs for both TM and TE polarizations by changing parameters such as rod dielectric constants and radius sizes. In the adjusted square lattice, the symmetry reduction opened a 39% PBG width. Both structures' PBGs are strongly influenced by their dielectric constants and geometry parameters.

Task scheduling in Cloud-Fog computing environments is a critical aspect of optimizing resource allocation and enhancing performance. This study presents an improved version of the Dingo Optimization Algorithm (IDOA) specifically designed for task scheduling in Cloud-Fog computing. The enhanced IDOA incorporates novel modifications to address the limitations of the original algorithm and improve the efficiency and effectiveness of task allocation. The algorithm incorporates modifications to the fitness evaluation function, a dynamic update mechanism, and a neighborhood search technique to enhance task allocation efficiency. Extensive simulations and comparisons with existing algorithms are conducted to evaluate the performance of the IDOA. The results demonstrate its superiority in terms of task makespan time, VM failure rate, and degree of imbalance. Overall, the improved Dingo Optimization Algorithm offers a promising solution for efficient task scheduling in Cloud-Fog computing environments. The algorithm effectively balances exploration and exploitation, facilitating efficient task scheduling in Cloud-Fog computing environments and optimizing cloud-based applications and services.

This study aimed to design a system utilizing an ESP8266 microcontroller, GPS, and MPU6050 sensors to observe and record the movement patterns of elderly patients. A scale prototype was developed by integrating an MPU6050 IMU sensor and a GPS module with an ESP8266 module configured for Wi-Fi communication with a nearby server. Data were collected and evaluated by sensors positioned on test subjects to imitate the movements of elderly patients. Upon successful real-time tracking of the test subject's movements, the data was transmitted to the network using Blynk and email applications for analysis and subsequent display. The collected data yielded valuable insights into the patient's conduct, encompassing frequent walking, pacing, and wandering. The utilization of this device has the potential to improve the safety and well-being of elderly patients and provide valuable data to healthcare professionals and caregivers through its real-time tracking capabilities.

In this work, we study the generation of ultrashort femtosecond laser pulses in photonic crystal fibers through the widely used symmetric split-step Fourier method (S-SSFM). The results are compared with the method of fourth-order Runge-Kutta method (RK4) as the most recognized and powerful method in the analysis of the evolution of ultrashort femtosecond laser pulses. The results show that although S-SSFM is a widely used method, its accuracy decreases for the selected relatively large step sizes of  in comparison with RK4. In contrast, the accuracy of the mentioned methods becomes closer to each other by selecting relatively small step sizes of .

The development of renewable energy-based applications is nowadays a forced demand of society, for chasing the target set by the governments and the concerned organizations, to reduce or limit the carbon penetration in the environment. Researchers and scholars putting their sincere efforts toward the development of efficient and reliable renewable energy-based applications. The scope of this paper is to present a Renewable Energy based Irrigation System capable of maintaining the application-based power demand. The REBIS consists of a photovoltaic generator as the main power source supported by an energy storage system (battery). For this hybrid system, the development of a utility-based Adaptive Hybrid Control is proposed to regulate the charge/discharge cycle of the battery energy while maintaining the load demand simultaneously. A case study, supporting the time-bound irrigation, is considered to verify the viability and performance of the proposed system. This proposed system and controller are first simulated in MATLAB, and then implemented at the hardware level in the LabVIEW environment.

Biometric characteristics of the human body can play a decisive role in the accuracy of automatic signature verification systems due to their stability over time and resistance to variability in different conditions. In this study, the accuracy of an automatic handwritten signature verification system is checked for nine months. In this system, the electromyography (EMG) signals from the hand muscles of people during signing are recorded at different times up to nine months, and after the pre-processing of the signals, muscle synergy patterns are extracted by the non-negative matrix factorization (NMF) method. Finally, the patterns extracted by the SVM classifier are classified into two classes: genuine and forgery signatures.

This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive Neuro-Fuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate.

Automatic and accurate bone segmentation has important medical applications. Thresholding-based segmentation is the most widely used method to segment the object of interest from the background. Although bone tissue is among the brightest tissues in MRI T2 images, bone has a similar intensity and comparable characteristics to particular other tissues, such as fat, which may cause misclassifications and undesirable results. We have proposed an automatic, accurate, and rapid, with less computational complexity and time segmentation method for the knee bone using iterative thresholding and Support Vector Machines (SVMs). The initial threshold value is first obtained by Otsu Thresholding to partition the image into two classes: bone and non-bone candidate areas. The SVM detected the bone region from the bone candidate areas based on location and shape. The iterative process significantly improved the thresholding value until the bone was identified. The post-processing step utilized a Canny edge filter and image opening to eliminate the undesired area and to more accurately extract the bone. The proposed segmentation technique distinguished between bone and similar structures, such as fat. The object (bone) detection rate was 1, and the average segmentation accuracy was 0.96 using the Dice Similarity Index.

A three-level scenario-based model for optimal operational planning in order to form a coalition between multiple microgrids is presented. The proposed model is based on the cooperative game theory method. Then the basis of the coalition is to achieve optimal cumulative energy management of all coalition participants. In the proposed model, At first, a bi-level problem is designed to give the optimal exchanges that happen between independent elements (e.g. an energy storage system and a wind power plant) and microgrids. The proposed model uses a cooperative game theory method in which the players try to find a way to achieve the highest profits for the whole coalition. The bi-level model is represented as an MPEC problem. After solving this problem and determining the number of exchanges, each of the local microgrids is operated separately from the perspective of the local operator in the third level of the introduced model. In this way, the amounts of production of electrical and thermal generation units and also the energy status of the system of storing energy are determined.

Recent developments in the field of internet innovation have made it feasible for new methods to be developed for the delivery of medical care. Sharing of resources may be encouraged via the use of network infrastructure and shared points of access.</em> Patient data and clinical information make the Internet an ideal tool for the many applications that include remote patient monitoring. In this paper, we provide an experimental model that was developed for monitoring and checking on the patient's current state of health using sensors.</em> The framework is dependent on an e-health sensor that is connected to a cloud platform. The cloud platform is responsible for collecting the data that is gathered from the sensors through the Arduino microcontroller. The data collected by the sensors includes readings from body temperature, heart rate, and oxygen. The data then is easily available for further processing, including the analysis of certain correlations between the measured parameters and the health status of the patients.</em>

In today's rapidly advancing technological landscape, the proliferation of motorized vehicles, particularly motorcycles, continues to grow. Motorcycles play a crucial role in facilitating transportation for a broader community. As more daily activities become reliant on road networks, the volume of road users has escalated significantly, resulting in an increased risk of accidents. Unfortunately, many motorcycle riders neglect the importance of wearing helmets, a fundamental safety measure. To address this pressing issue, we propose the development of a helmet detection system for motorcycle riders. This system aims to encourage responsible helmet usage and minimize negligence among motorists. Our research employs an Arduino Uno microcontroller and various components, including the NRF24L01 wireless module, Limit Switch, battery, charger module for input, and Arduino Uno, NRF24L01, and Relay for output. Our study demonstrates that the designed system functions effectively. It operates only when the rider is wearing a helmet, enabling the motorcycle to start by activating a relay that connects power to the motorcycle socket. The Arduino status is triggered when both Limit Switch 1 and 2 are pressed. The maximum distance between the two NRF24L01 modules is found to be 2 meters. Through our experiments, we have successfully demonstrated that the motorcycle can be started only when the rider is wearing a helmet and securely fastens the helmet strap. This innovation promotes driving safety and encourages responsible helmet usage.

The Finite-Set Model Predictive Power Control (FS-PPC) is one of the most intriguing model predictive approaches for the induction machine. This control is successful because it </span>operates without weight coefficients and does not require the rotor flux position, as in the Predictive Torque Control (FS-PTC) and the Predictive Current Control (FS-PCC), respectively. A simple extension to the double-star induction generator results in significant current harmonics and common mode voltage. To fix these issues, this paper proposes an improved FS-PPC applied to an asymmetric double-star induction generator based wind energy conversion system by introducing two concepts: (a) the virtual voltage vector (VVV), in order to eliminate the (x, y) components of the stator currents. (b) the zero common mode voltage vectors (ZCMV), to eliminate the common mode voltage. A simulation of the developed ZCMV-FS-PPC system control is created in MATLAB/Simulink. The results show the effectiveness of this approach with CMV equal to zero and negligible (x, y) components of the stator currents. Moreover, the elimination of the CMV not only avoids its damage but also reduces the computation by 50%.

The integration of a fuel cell and solar cell into a generator system presents an effective solution to numerous energy-related challenges. This system consists of solar panels, fuel cells, voltage converters, and a battery or supercapacitor. The performance of this electricity generation system is influenced by various factors, including load nature, system connection, and energy management. This study focuses on maximizing power point tracking in a grid-independent mode. To optimize efficiency, a DC/DC voltage converter is employed to align the load with the characteristics of the maximum power point. The algorithms used for maximum power point tracking are categorized into three groups: perturbation and observation (P&O), incremental impedance, and artificial neural networks (ANN). In this study, we introduce two novel algorithms based on neural networks and evaluate their performance in comparison to other neural networks. Additionally, we propose a control strategy based on a selected slip level for photovoltaic generators. The proposed approach demonstrates superior and more efficient performance compared to other methods, making it a promising technology for sustainable energy generation.

This paper proposed the multiobjective optimal allocation of renewable distributed generation and shunt capacitors in the distribution system using corona virus herd optimization techniques. The work aimed to achieve a technical benefit, total electricity cost reduction, and enhancement of greenhouse safety. The objectives considered are real power loss, voltage profile index (VPI), voltage stability index (VSI), total electricity cost (TEC), and total greenhouse gas emission (TGHGe). Weight function was used to combine the objectives for the six cases considered with different priorities. The proposed CHIO is validated on the standard IEEE 33 bus system and implemented on Dada 46 bus, a Nigerian practical distribution network. Various cases were considered for the two test systems. For IEEE 33 bus, the proposed method achieved 89.44% and 86.77% reduction in real and reactive power, respectively, with 93.73% and 39.27% in TGHGe and TEC. Also, for Dada 46 bus system, 89.44% and 86.77% reduction in real and reactive power loss respectively was achieved with 98.66% and 64.42% in TGHGe and TEC. Furthermore, the highest level of greenhouse gas emission reduction was achieved (says 99.69%) when high priority was placed on the reduction in TGHGe; this shows the significant impact of renewable energy in the distribution system. The results obtained are compared with the existing methods, such as PSO, GA, ABC, GABC, WOA, WCA, to mention a few. In other to show the performance of the proposed CHIO compared to others, the outcome reveals the excellent performance of the proposed algorithm in terms of an optimal result.

The hybrid electric train which operates without overhead wires or traditional power sources relies on hydrogen fuel cells and batteries for power. These fuel cell-based hybrid electric trains (FCHETs) are more efficient than those powered by diesel or electricity because they do not produce any tailpipe emissions making them an eco-friendly mode of transport. The target of this paper is to propose low-budget FCHETs that prioritize energy efficiency to reduce operating costs and minimize their impact on the environment. To this end, an energy management strategy [EMS] has been developed that optimizes the distribution of energy to reduce the amount of hydrogen required to power the train. The EMS achieves this by balancing battery charging and discharging. To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. </strong>DRL-based strategies are commonly used in energy management and they suffer from unstable convergence, slow learning speed, and insufficient constraint capability. To address these limitations, an action masking technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated is proposed. </strong> The DDPG+TL agent consumes up to 3.9% less energy than conventional rule-based EMS while maintaining the battery's charge level within a predetermined range. The results show that DDPG+TL can sustain battery charge at minimal hydrogen consumption with minimal training time for the agent.

Electricity is an indispensable requirement for modern society, and with the advancement of technology, the adoption of renewable energy sources, such as Solar Power Plants (PLTS), has been on the rise. PLTS harness the virtually limitless and abundant power of solar energy. The successful construction and operation of a Solar Power Plant necessitates meticulous planning to ensure optimal functionality. In our research aimed at PLTS development, we have chosen an on-grid operating system for installation on the roof of SDN 023905 Binjai. This decision aligns with our goal to reduce the reliance on conventional electrical sources at SDN 023905 Binjai, which currently receives its entire electrical supply from PLN. The proposed PLTS will boast a capacity of 1.2kWp, comprising four 300Wp solar panels connected in series, alongside a 1.2 kW to 3 kW inverter. Our comprehensive analysis validates the feasibility of this plan, as it yields a Performance Ratio (PR) of 84%, surpassing the minimum PR benchmark of 70%. From a financial perspective, the project entails an initial investment cost of IDR 31,555,222, with a remarkably swift payback period occurring within the first year and month of the PLTS's 20-year operational lifespan.