Iraqi Journal for Electrical And Electronic Engineering

Inductors play a major role in the power electronics domain, particularly in DC-DC converter design. The objective of this paper is to reach inductance value by means of fewer turns, using Litz wire wound on a ferrite core. In the manufacture of inductors, the key aspects of the design criteria include the choice of the core material, the type of copper coil and insulation materials, and their overall size. Taking into consideration the design parameters with no compromises on performance, Litz wire with the least turns is introduced into an inductor in certain DC-DC converters. Once the DC settled voltage is reached, it is given to a single-phase inverter for loading and application measures. This approach provides a small-level inductor design for maximized efficiency with improved thermal behavior. The hardware model for the proposed method has been developed using a DC-DC converter fed with a single-phase inverter model. The proposed DC-DC converter has been tested, performance-wise, by applying different load levels. It is observed, from the results, that the Litz wire-based approach achieves maximum efficiency with improved thermal behavior.

The Internet of Things (IoT) has become a major enabler for sustainable development and has begun to have an impact
on society as a whole. Marshes are significant ecosystems for the environment that are essential to biodiversity support
and ecological equilibrium. However, environmental changes and human activity are posing an increasing threat to these
fragile ecosystems. An Internet of Things (IoT)-based marsh monitoring system was created and put into operation to
gather data in real-time on a variety of environmental factors, such as wind speed, CO2 and hydrogen levels, temperature,
humidity, voltage, and current. The system makes use of a network of sensors spread out throughout the marsh, which
may promote sustainable development. send data to a central node for processing before sending it to a platform hosted
in the cloud. After that, an interactive online application is used to visualize the data, giving stakeholders important
information about the condition and health of the marsh. Because the suggested system makes it possible to monitor and
manage marsh ecosystems effectively, it may promote sustainable development.

The operational variables of Proton Exchange Membrane Fuel Cell (PEMFC) such as cell temperature, hydrogen gas pressures, and oxygen gas pressures are highly effect on the power generation from the PEMFC. Therefore, the Maximum Power Point Tracker (MPPT) should be used to increase the efficiency of PEMFC at different operational variables. Unfortunately, the majority of conventional MPPT algorithms will cause PEMFC damage and power loss by producing steady-state oscillations. This paper focuses on enhancing the efficiency of the Proton Exchange Membrane Fuel Cell through the utilization of advanced control methods: Grey Wolf Optimizer (GWO), GWO with a PID controller and perturbation and observation (P&O) techniques. The objective is to effectively manage power output by pinpointing the maximum power point and reducing stable oscillations. The study evaluates these methods in swiftly changing operational scenarios and compares their performances. The obtained results show that the GWO with a PID controller increase generation power.

Wind turbine (WT) is now a major renewable energy resource used in the modern world. One of the most significant technologies that use the wind speed (WS) to generate electric power is the horizontal-axis wind turbine. In order to enhance the output power over the rated WS, the blade pitch angle (BPA) is controlled and adjusted in WT. This paper proposes and compares three different controllers of BPA for a 500-kw WT. A PID controller (PIDC), a fuzzy logic controller (FLC) based on Mamdani and Sugeno fuzzy inference systems (FIS), and a hybrid fuzzy-PID controller (HFPIDC) have been applied and compared. Furthermore, Genetic Algorithm (GA) and Particle swarm optimization (PSO) have been applied and compared in order to identify the optimal PID parameters (kp, ki, kd). The objective of GA and PSO is minimized the error signal in output power based on actual WS. The results for three different controllers show that the optimal hybrid FPIDC based on the Sugeno inference system with PSO produces the optimal results regard to reduce the error signal and stable output power under actual WS.

Drug addiction remains one of the key problems, which troubles each nation nowadays. Though social and economic factors have been contributing to its escalation significantly, recently in recent years a marked rise with drug addiction has witnessed in Iraq. Governments and societies are therefore working hard to find ways of counteracting this trend. Notably, social media networks have become major conduits of the dissemination sensitization about the risks involved in substance abuse addiction as well as consequences that are faced by drug abusers users. On the other hand, there are no studies analyzing user’s sentiment regarding drug addiction on social media in Iraq. This paper offers a new approach to fill this gap by presenting an analytical framework for identifying such sentiments of people from posts published on different popular platforms including Facebook and Twitter. In order to achieve this, a new dataset was generated from one of the relevant Facebook pages and comprised three distinct levels of user engagement data. Our goal is to create a direct connection between the research objectives and practical applications which can benefit society. This study’s results contribute significantly to the understanding of sentimental movements regarding drug addiction and affect public perceptions on this significant problem. This study makes contributions to such fields are sentiment analysis, social media research and public health by revealing the complex interaction of social media itself, user’s feelings towards it or even drug addiction in Iraq. The new approach to analysis of multi-level user engagement data and offers an evidence based solution for dealing with the challenges presented by drug abuse in society. Using a neural network algorithm, the classification model developed has shown excellent performance with an accuracy rate of about 91%.

Automatic handwriting recognition is a fundamental component of various applications in various fields. During the last three decades, it has become a challenging issue that has attracted much attention. Latin language handwriting recognition has been the primary focus of researchers. As for the Kurdish language, only a few researches have been conducted. This study uses a Kurdish character dataset, which contains 40,940 characters written by 390 native writers. We present an ensemble transfer learning-based model for automatically recognizing handwritten Kurdish letters using Densenet-201, InceptionV3, Xception, and an ensemble of these pre-trained models. The model’s performance and results obtained by the proposed ensemble model are promising, with a 97% accuracy rate, outperforming other studies on Kurdish character recognition.

The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method’s resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems’ performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution

The traditional economic dispatch (ED) inattention to the fossil fuels emission of thermal power plants no longer satisfies the environmental needs. As a result of the non-convex, non-smooth fuel cost functions in addition to the nonlinearity of the emission modelling. These make the combined economic and emission dispatch (CEED) a highly nonlinear optimization problem. Furthermore, different operation process constraints should be taken into account, such as loss in electrical networks and power balance of unit operation. These constraints increase the difficulty of obtaining the global optimal solution based on traditional methods. Recently, meta-heuristic population-based algorithms have successfully become a beneficial technique for solving nonlinear optimization problems. The major contribution in this work is presenting a recent meta-heuristic approach known as Mayfly algorithm (MA) for solving nonlinear and complex CEED problem. The numerical results are compared with results obtained from modern meta-heuristic algorithms like Jellyfish Search (JS) optimizer, Dwarf mongoose optimization (DMO), Tunicate swarm algorithm (TSA), Red deer algorithm (RDA), Tuna Swarm Optimization (TSO), Golden Eagle Optimizer (GEO) and Bald eagle search Optimization algorithm (BES). The standard IEEE 30-bus test system is used in this article. The simulation results are done using MATLAB environment. The results approve the reliability, stability, and consistency of the proposed approach. The proposed technique gives reliable, robust, and high-quality solution with faster computational time. Moreover, MA is more suitable for solving nonlinear CCED problem because it has a considerable convergence feature.

Recently, the need for high speed multiply-accumulate (MAC) operations is crucial in numerous systems like 5G, deep learning, in addition to many digital signal processing (DSP) applications. This work offers an improved MAC (I-MAC) block of different bit-size based on Vedic Mathematic and employing a hybrid adder consists of an enhanced Brent-Kung with a carry-select adder (HBK-CSLA) to achieve the sum of products for the MAC. The work is then, developed to design a new multimode fixed-point (FX-Pt) MAC block by exploiting the proposed design of the I-MAC architecture. The proposed multimode MAC block supports three modes of operation; single 64-bit MAC operation, dual 32-bit multiplication with 32-bit single addition, and single 32-bit MAC operation. The design has utilized an adjusted architecture for the Vedic-multiplier (Adjusted-VM), a 64-bit HBK-CSLA, and a control circuit to select the desired mode of operation. The performance of the multi-mode MAC is then optimized by exploiting pipelining concept. The proposed architectures are synthesized in various FPGA families utilizing VHDL language in Xilinx ISE14.7 tool. The performance results have exposed that the proposed 64-bit I-MAC block have attained observable lessen 9.767% in delay and area usage of 47.49% compared with the most existing MAC block designs.

The development of Fuzzy Logic Controllers (FLC) with low error rates and cost effectiveness has been the subject of numerous studies. This paper study goals to the investigation and then implementation an FLC using the readily accessible and reasonably priced Raspberry Pi technology. The FLC used in this work has two inputs, one output, and five Membership Functions (MFs) for each input and output. The FLC goes through two processes, tweaking the MF parameters and tuning input/ output Scaling Factors. The tuning technique makes use of the Genetic Algorithm (GA). The whole set of the FLC probabilities is taken into account as the tuned FLC controller, and then transformed into a lookup table. The Center of Gravity (COG) approach is used to determine the output for the tuned FLC controller. The resulting table is converted into values of digital binary using a specific type of encoder, and then extraction of the set of Boolean functions to apply this tuned circuit. Finally, the Python 3 programming language is used to define the resultant Boolean functions on the Raspberry Pi platform, and then a decoder extracted the appropriate control action from the output. The Benefit of this method is the use of a digital numbering system to define the FLC, which is implemented on Raspberry Pi technology and allows for fuzzified high processing speed output per second. The controller speed has not been unaffected by the quantity for these fuzzy rules.

License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications, including parking management, vehicle access control, traffic control, and law enforcement. This project aims to provide a robust and dependable method for detecting license plates that will outperform existing approaches in accuracy and dependability. This observation method uses contemporary technology to address challenging troubles related to license plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established model for picture item detection. The novel thing, even though, is how Gray Wolf Optimization—which draws notion from the searching conduct of gray wolves—is mixed with the Faster R-CNN network. The accuracy is greatly improved by this synergistic combination, which also improves detection abilities. Moreover, an improved ResNet-50 model is blanketed to improve the classification system similarly, ensuring accurate license plate detection in several situations. The extensively utilized ”car license plate detection” dataset is used to assess the recommended technology very well, confirming its efficacy in practical settings. The empirical outcomes show exceptional performance, with a median precision of 98.21%, demonstrating how nicely the hybrid method works to attain the very best stage of license plate detecting accuracy. This painting establishes a new benchmark in license plate identity using cutting-edge technology and innovative techniques, starting the door for enhanced safety and surveillance.

An intelligent video system’s basic function is the detection of moving objects. Moreover, real-time systems frequently pose limitations for applications involving video processing. Practically, to increase the frame rate or in the case of limited hardware resources, the real-time processing is done on an interested image segment called the region of interest (ROI). Applying the background subtraction (BGS) algorithm to this region is considered the main preprocessing operation. This paper presents a practical study for video processing based on FPGA to detect moving objects using the BGS technique. The proposed algorithm was developed using Verilog hardware description language (HDL), synthesized, and implemented in the programmable logic (PL) part of the ZYBO-7Z010CLG400-1 platform. Finite State Machine (FSM) controller method was used to design the Intellectual Property (IP) module that controls data transfer with BRAM (loading and reading) of the input and reference image. The simulation results of the timing signal sequences of the proposed IP module with the practical test of the BGS to detect several traffic signs of image size (90×90) pixels demonstrate that the module functions as intended. The system that is being presented has a latency of 13.468 nanoseconds, making it appropriate for real-time applications.

The electrical and radio frequency (RF) characteristics of InAlGaN/GaN high electron mobility transistors (HEMTs) device with cap layer are presented in this work. In this work, Silicon carbide was used as a substrate for its excellent thermal conductivity. Here, the thermal model was used to investigate the simulation of temperature distribution at 300k. Moreover, the DC and AC performance characteristics of the device were investigated using Silvaco Atlas Technology Computer Aided Design TCAD simulator. The results showed that, the maximum obtained drain current that was 1.35 A. In addition to, the RF parameters were extracted. The cut-off frequency ft is (73 GHz), the maximum oscillation frequency fmax is (353 GHz), maximum stable gain (Gms) and maximum available gain (Gma) with a value of about (116 dB). The obtained results showed that the InAlGaN/GaN HEMT device based on SiC performance is suitable for microwave applications.

Enhancing the generated power. Different conventional reconfiguration techniques can be used for this purpose like totalcross- tied (TCT), bridge-linked (BL), and series-parallel (SP) . . . etc. This article propose a new static reconfiguration technique named Row Odd Even reconfiguration (ROE) to increase the maximum power generated from PV array with the effect of partial shading condition. The proposed reconfiguration has been tested on a 3×22 PV array suggested to provide power to the department of electronic and communications engineering at Al-Nahrain University, Baghdad, Iraq. The results of the proposed reconfiguration are compared with the (SP, TCT, and Zig-zag) in terms of mismatch power losses (MPL), fill factor (FF), and efficiency (η) at the maximum generated power of PV array. In all cases, the performance of the new reconfiguration gave the best performance when compared with (SP, TCT, and Zig-zag). The new reconfiguration achieved an improvement in the maximum power point (MPP) and efficiency about 33%, 28% and 7% when compared with the (SP), (TCT) and (Zig-zag) reconfigurations respectively.

Increasing the penetration of Renewable Energy Sources (RES) into power systems created challenges and difficulties in the management of power flow since RES have variable power production based on their sources, such as Wind Turbines (WT), which depend on the wind speed. This article used Optimal Power Flow (OPF) to reduce these difficulties and to explain how the OPF can manage the power flow over the system, taking different cases of WT power production based on the different wind speeds. It also used Fixable AC Transmission (FACT) devices such as Thyristor-Controlled Series Compensators (TCSC) to add features to the controllability of the power system. The OPF is a non-linear optimization problem. To solve this problem, the artificial intelligence optimization technique is used. Particle Swarm Optimization (PSO) has been used in the OPF problem in this article. The Objective Functions O.F. discussed here are losses (MW), Voltage Deviation VD (p.u.), and thermal generation fuel Cost ($/h). This article used the wind turbine bus magnitude voltage and the reactance of TCSC as a control variable in OPF. To test this approach, the IEEE 30 bus system is used.

One of the important components of the gas-insulated switchgear (GIS) system are spacers. These insulators are not free from some manufacturing and operational defects that adversely affect their performance, and among these defects is the presence of air voids inside the solid insulator, which may be exposed to high electric fields that may lead to partial discharges within the voids. These partial discharges may cause the accumulation of charges on the surfaces of these insulators, increase the concentration of the electric field, and cause electric flashover across the interface surfaces between the SF6 gas and the solid insulator. With a 2D axisymmetric model based on finite element analysis (FEA) implemented in COMSOL Multiphysics software, this study investigates the impact of the charges that accumulated on the inner surfaces of the void on the electric field distribution of the spacer’s interior and exterior surfaces, as their effect is investigated when they are in different size and locations inside the insulator. This effect is more noticeable when the density of charges on the inner surface of the void increases to 1 (μC/m2) and the radius is 2 mm. When the void positioned 1 mm from both the inner and outer surfaces of the spacer, the electric field values are 14.55 and 9.4 MV/m, respectively. The impact site on the spacer surface is narrow within 3 mm and depends on the size of the void. The field enhancement factor may reach 2, and its value is higher on the outer surface than on the inner surface.

In recent years, self-driving cars and reducing the number of accident casualties have drawn a lot of attention. Although it is crucial to increase driver awareness on the road, autonomous vehicles can emulate human driving and guarantee improved levels of road safety. Artificial intelligence (AI) technologies are often employed for this purpose. However, deep learning, a subset of AI, is prone to numerous errors, a wide range of threats, and needs to handle vast amounts of data, which imposes high-performance hardware requirements. This study suggests a deep learning model for object recognition that employs characteristics to describe data rather than images. Our model employs the COCO dataset as the training foundation, and it was suggested that the features be retrieved using the principal component analysis PCA extraction method. The current results demonstrate the efficacy and precision of our model, with an accuracy of 99.96 %. Furthermore, the performance indices, i.e., recall, precision, and F1-score, achieved about 1 for most of the COCO classes in training phase and promising results in testing phase.

In recent years, the urgent need for robotics applications in various sensitive work areas and high buildings has led to a significant development in the design of robots intended for climbing rough surfaces. Where, attention became focused on the ideal clinging mechanism. In this paper, a gripper of the climbing robot has been designed to achieve clinging on rough walls. The objective of this design is to be lightweight with high performance of clinging, therefore, a robot gripper has been designed based on a model of a limb inspired by the hand and claws of a cat, in which the robot claws were implemented by fishing hooks. These hooks are arranged in an arc so that each hook can move independently on the wall’s surface to increase the force of clinging to the rough wall. SolidWorks platform has been used to design the clinging limb and implemented using a 3D printer. In addition, the proposed design has been validated by performing several simulations using the SolidWorks platform. Experimental work has conducted to test the proposed design, and the results proved the success of the design.

At recent days, the robot performs many tasks on behalf of humans or in support of humans. Among the most prominent benefits of robots for humans are removing the risk factor from humans, completing routine tasks for humans, saving a lot of time and effort, and mastering work. This paper presents a model of an eight-legged robot equipped with an intelligent controller that was simulated using MATLAB. The designed structure contains 24 controllers, three for each leg, to provide flexibility in movement and rotation. Proportional Integral Derivative (PID) controller has been used in this work , each leg contains three PIDs. A particle swarm optimization algorithm (PSO) was used to adjust the parameters of the PID controller (Kp , Ki and Kd). The structure of eight legs robot with controller is implemented using Simscape Multibody in the MATLAB program, where the movement of the eight-legged robot is visualized and analyzed without the need for complex analysis associated with a mathematical model. The simulation results were conducted in a three-dimensional environment and were presented in two scenarios . The first was implementing and simulating the robot without using a controller, which leads to the robot falling at the starting point. The second was when a PID controllers are used with the system, where better movement was obtained. Finally, the robustness of the controller was verified by changing the load that the robot bears.

Aircraft detection is a vital and significant field within object detection that has garnered considerable attention from academics, particularly following the advancement of deep learning methods. Aircraft detection has recently become widely utilized in several civil and military fields. This comprehensive survey meticulously categorizes and evaluates diverse deep learning methodologies in airplane detection research. Encompassing radar-based, image-based, and multimodal approaches, the paper presents a structured framework to enhance understanding of the evolving research landscape within this domain. The survey critically identifies gaps and discerns emerging trends, offering valuable insights into standard datasets of aircraft images, performance metrics, real-world applications, and challenges and limitations encountered by aircraft detection systems. Its potential contributions are underscored as pivotal for advancing the safety and security of air travel. This research paper is the inaugural publication of its kind in the domain of aircraft detection review papers, establishing itself as an all-encompassing reference for subsequent scholars.

Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhanced by carrying them to operate at ultimate energy mark tracking. In this study, a versatile maximum power point tracking (MPPT) model using a modified Flyback controller with artificial neural network (ANN) technique as our proposed system. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset is utilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with a Flyback controller. It is assumed that the results will be obtained by the ANN-MPPT system with the Flyback controller which provides low motions and shows a great implementation around the maximum power point compared to the PVG used with traditional MPPT algorithms such as Perturbation and Observation (P & O).

The performance of Sparse Code Multiple Access (SCMA) communication system with Logarithmic Message Passing Algorithm (log-MPA) decoder is introduced. To boost the performance, a Low-Density Parity-Check Code LDPC is used together with Belief Propagation (BP) decoder. LDPC is chosen due to its sparsity property that complements the sparsity nature of SCMA for maximum efficiency and minimum complexity. Three distinct SCMA configurations are used. These are: A (4 x 4 x 6), B (4 x 16 x 6), and C (5 x 4 x 10) where the (K x M x V) are numbers of resources, codewords and users respectively. The performance of these configuration is shown in various channel conditions, various LDPC code rates and various numbers of SCMA iterations (NSCMA), to find the local minimum value of log-MPA. Simulation results showed that the LDPC greatly boosted the performance in mentioned configurations: In A configuration, a gain of 13 dB was observed. Configuration B experienced a substantial improvement of 23.5 dB, while C achieved a gain of 20.5 dB. Notably, configuration B stood out with the highest gain, attributed to LDPC’s exceptional performance with high data rates, as the data transmitted in B was double that of A.

A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasis has been placed on extracted features from the High Density - surface Electromyography (HD-sEMG) signals and the size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gestures identification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signals for this mission. Several feature types have been extracted from FMG and HD-sEMG signals such as MEAN, RMS, MAD, STD, and Variance, these features have been validated under some classifiers such as decision tree (DT), linear discriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing that MEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achieved by the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65 isometric hand gestures is employed, where two (8×8) electrodes and 9 force sensors are used to collect the FMG data. This data was derived from 20 intact participants, the first preprocessing step was applied during the recording stage. Ten gestures are chosen to be classified from the 65 hand gestures. Results show the success of the proposed system while the classification accuracy arrived at 99.1%.

Precise Prediction of activity location is an essential element in numerous mobility applications and is especially necessary for the development of tailored sustainable transportation systems. Next-location prediction, which involves predicting a user’s future position based on their past movement patterns, has significant implications in various domains, including urban planning, geo-marketing, disease transmission, Performance wireless network, Recommender Systems, and many other areas. In recent years, various predictors have been suggested to tackle this issue, including state-of-the-art ones that utilize deep learning techniques. This study introduces a robust Model for predicting the future location path of a user based on their known previous locations. The study proposes the use of a Long Short-Term Memory (LSTM) prediction scheme, which is well-suited for learning from sequential data; then a fully connected neuron is employed to decrease the sparsity of the data, resulting in accurate predictions for the path of the user’s next location. The suggested strategy demonstrates superior prediction accuracy compared to a state-of-the-art method, with improvements of up to a loss error of 0.002 based on real-life datasets (Geolife). The results demonstrate that the reliability of forecasts is excellent, indicating the accuracy of the predictions.