Semnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Calculation of Rotor Bars Current in Squirrel Cage Induction Motor Under Unbalanced and Harmonic Voltage Conditions Using Multiple-Coupled Circuits18945610.22075/mseee.2025.34158.1158ENHamedShadfarFaculty of Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.Hamid RezaIzadfarFaculty of Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.Journal Article20240517Unbalanced and harmonic voltages are important issues in the power quality field. These factors can have destructive effects on induction motors (IMs and can lead to problems such as high temperature, increased losses, speed and torque fluctuations, and noise in the machine. Therefore, analyzing these factors and their effects on motor performance is very important in the interaction of this machine and the power system. The rotor current is one of the characteristics that is affected by these factors. Calculating the rotor bars current (RBC) using stator data is not easy, but it can be used to predict the behavior of the motor in different conditions. This paper proposes an algorithm based on the repetition method using the stator data based on the multiple-coupled circuits model (MCC) to calculate the RBC in the squirrel cage induction motor (SCIM) under unbalanced and harmonic voltage conditions. For this purpose, a 2-pole SCIM with nominal specifications of 1.1 kW, 220/380 V, and 50 Hz is subjected to experimental testing and simulation. To confirm the practical results, this motor is simulated in the Maxwell software and the results are compared and evaluated with the experimental results.https://mseee.semnan.ac.ir/article_9456_e9318603f462823f07f3e856fa00caf6.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Cluster Synchronization for Discrete-Time Zero-Sum Graphical Games with Unknown Constrained-Input Systems918945710.22075/mseee.2025.33849.1155ENZahraJahanElectrical Engineering Department, Semnan University, Semnan, Iran.AbbasDidebanElectrical Engineering Department, Semnan University, Semnan, Iran.FarzanehAbdollahiElectrical Engineering Department, Amirkabir University, Tehran, Iran.Journal Article20240420This paper addresses the synchronization issue of agents with their respective leaders in each cluster for unknown discrete-time zero-sum graphical games with constrained input. To solve the coupled Hamilton-Jacobi-Isaacs equations under the assumption of unknown dynamics, an adaptive optimal distributed technique based on value iteration heuristic dynamic programming is proposed. An actor-critic framework is employed to approximate the value functions, control policies, and worst-case disturbance policies necessary for implementing the proposed method. Additionally, neural network identifiers are utilized to determine each agent's unknown dynamics. To prevent system instability, a constraint on control inputs is incorporated into the design method. By considering disturbances in the dynamics, the proposed solutions are made robust against unpredictable events, enhancing performance and stability. Furthermore, the closed-loop system's stability is proven. Finally, the theoretical results are validated through simulation outcomes.https://mseee.semnan.ac.ir/article_9457_84358b3ef8af362f8567ca98b4a068eb.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Application of Data Mining and Machine Learning Techniques to Predict Loan Approval and Payment Time1928942110.22075/mseee.2025.35663.1183ENEhsan AllahKhoshkhoy NilashPhD Student of Information Technology Management, Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran.MansourEsmaeilpourAssociate Professor, Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.0000-0002-2475-518XBehroozBayatAssistant Professor, Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran.AlirezaIsfandyari MoghaddamProfessor, Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran.ErfanHassannayebiAssistant Professor, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.Journal Article20241019One of the most important issues regarding banks is knowing the customers, their behaviors, and the decisions these institutions make regarding customers' preferences. Their main task is to provide banking facilities. Bank facilities carry the risk of default in repayment. Failure to evaluate and review factors related to repayment can cause significant damage to banks. On the other hand, investment in the private sector and various industries is also increasingly important. This action can lead to economic growth, increased employment, and national income. This research aims to identify the effective features related to the fixed capital facility data of one of the active banks in Iran, in line with the classification of customers into two categories good customers and overdue customers to predict the duration of the facility payment. The five-step method is based on data mining techniques. The most important steps of this method are data preparation, analysis with rough set methods, and common classification techniques such as artificial neural networks, tree types, Bayes types, and support vector machines. One of the most important results of this research was the identification of the features that affect the repayment and duration of fixed capital facilities. Additionally, among other results of the present research, the ANN method demonstrated superior performance in evaluating credit risk with an accuracy value of 70.27%, and the J48 technique showed superior performance in predicting the duration of payment of facilities with an accuracy of 72.54%.https://mseee.semnan.ac.ir/article_9421_6f6961aea0c220387c2597899c82200d.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Wide Band THz Transmitarray Antenna Based on Graphene Slotted Lattice2935946010.22075/mseee.2025.34326.1162ENMahdiehGhaderiFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.PejmanRezaeiFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.Journal Article20240602A novel terahertz (THz) Transmitarray antenna using an identically shaped slotted graphene lattice with wide bandwidth, is presented in this paper. The transmissive surface consists of graphene frequency selective surfaces (FSSs), using at THz frequencies. The graphene FSS consists of a double layer 11×11 unit cells array on two dielectric layers. The total thickness of the structure is only at center frequency of 14 THz. The continuous slotted graphene sheets are connected to electrical bias, to control the chemical potential level of the graphene layers. A wideband, high gain, and high-efficiency transmitarray antenna using the presented graphene unit cells array, has been designed. Simulation results are shown the transmitarray antenna peak gain is 29.2 dB at 14 THz. The wideband transmitarray with a 3-dB gain bandwidth of about 30% and a 41.87% aperture efficiency is investigated.https://mseee.semnan.ac.ir/article_9460_904257374fa8bb8f947fae5126d903ff.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method3749949610.22075/mseee.2025.35788.1184ENAliehAshoorzadehDepartment of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.AbbasToloie EshlaghyDepartment of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.Mohammad AliAfshar KazemiDepartment of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.Journal Article20241102Load forecasting is a key component of electric utility operations and planning. Because of today's highly developed electricity markets and rapidly growing power systems, load forecasting is becoming an essential part of power system operation scheduling. This paper proposes a new short-term load forecasting model based on the large margin nearest neighbor (LMNN) classification algorithm to improve prediction accuracy. The accuracy of many classification methods, such as k-nearest neighbor (k-NN), is significantly influenced by the technique used to calculate sample distances. The Mahalanobis distance is one of the most widely used methods for calculating distance. Numerous techniques have been used to enhance k-NN performance in recent years, including LMNN. Our proposed approach aims to solve the local optimum problem of LMNN, compute data similarities, and optimize the cost function that establishes the distances between instances. Before using gradient descent to determine the ideal parameter values for the cost function, we employ a genetic algorithm to shrink the size of the solution space. Additionally, our method's forecasting errors are contrasted with those of the BPNN and ARMA models. The comparative findings show how well the recommended forecasting model performs in short-term load forecasting.https://mseee.semnan.ac.ir/article_9496_093b7b505718a6c71ff931574daf8ee5.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501A Novel Video Super-Resolution Enhancement Method Based on Residual Learning Using Hidden Markov Random Fields and a New Deep Learning Network Architecture5160952110.22075/mseee.2025.36069.1190ENMahnazMahdizadehDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.Ali AkbarKhazaeiDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad University,
Mashhad, Iran.Seyyed JavadSeyyed Mahdavi ChabokDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.FarzanKhatibDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.Journal Article20241127In today's world, improving the quality and clarity of videos has become increasingly important, particularly in the fields of surveillance, medicine, and imaging technologies. Traditional super-resolution methods primarily focus on the full reconstruction of video frames, which poses challenges in preserving fine details and complex structures. This paper introduces a novel approach based on parallel deep networks, effectively enhancing video quality by dividing video frames into three separate input branches: raw images, outputs based on Hidden Markov Random Fields (HMRF), and temporal images. The method also leverages techniques such as residual learning and random patching within a unified framework that combines spatial segmentation (HMRF) and temporal information. This integration allows the model to better capture spatial and temporal dependencies, leading to more accurate and efficient video frame reconstruction. To better focus on high-frequency details and mitigate the vanishing gradient problem, residual learning is employed, enabling the network to estimate only the additional details necessary for reconstructing high-resolution images. Additionally, through random patching, the network training process is designed to emphasize critical features and intricate textures. Experimental results demonstrate that the proposed method achieves an SSIM of 0.92857 and a PSNR of 34.8617, offering superior clarity in video reconstruction.https://mseee.semnan.ac.ir/article_9521_ae103f8b9c3e1e2e7e94ca0c5e62775e.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07864120240501Design and Simulating an X-Band Light Weight Phase Array Antenna with Integrated Phase Shifter6167952210.22075/mseee.2025.35482.1181ENMohammadrezaShahkhahImam Hossein University, Tehran, Iran.NimaBeiranvandIslamic Azad University Tehran Branch, Tehran, Iran.Journal Article20240930The advancement of technology has significantly increased the importance of defense systems that can scan and identify attacking targets. These systems rely on phased array antennas to achieve their functionality. The beam remains fixed in a perpendicular orientation without such antennas, preventing effective target detection. Historically, beam rotation was accomplished either mechanically or electronically. Mechanical methods involved the use of levers that required constant rotation, whereas electronic beam rotation was enabled solely by phased array antennas. This process necessitates the use of phase shifters, which are typically implemented using either pin diodes or ferrites. In this article, pin diodes are utilized due to their advantages, including high switching speed, reversibility, and superior availability compared to ferrites.<br /><br /><br /><br /><br />row<br /><br /><br />Parameters<br /><br /><br />Amount<br /><br /><br /><br /><br />1<br /><br /><br />Total angle covered<br /><br /><br />45Degree<br /><br /><br /><br /><br />2<br /><br /><br />half power beam width<br /><br /><br />°18<br /><br /><br /><br /><br />3<br /><br /><br />Angle change step<br /><br /><br />Less than 5°<br /><br /><br /><br /><br />4<br /><br /><br />Polarization<br /><br /><br />linear<br /><br /><br /><br /><br />5<br /><br /><br />Return Loss (VSWR)<br /><br /><br />Less than 1.5<br /><br /><br /><br /><br />6<br /><br /><br />Total weight (antenna, control board, and feeding network)<br /><br /><br />1 kilogram<br /><br /><br /><br /><br />7<br /><br /><br />Dimensions<br /><br /><br />15 cm × 15 cm × 10 mm<br /><br /><br /><br /><br />8<br /><br /><br />Tolerable power<br /><br /><br />1 watt<br /><br /><br /><br /><br />9<br /><br /><br />A(area)<br /><br /><br />126mm<br /><br /><br /><br /><br />10<br /><br /><br />Horn a<br /> <br /><br /><br />35mm<br /><br /><br /><br /><br />11<br /><br /><br />Horn b<br /><br /><br />27mm<br /><br /><br /><br /><br />12<br /><br /><br />horn flare length<br /><br /><br />2inch<br /><br /><br /><br /><br />13<br /><br /><br />C<br /><br /><br /> <br /><br /><br /><br /><br />14<br /><br /><br />Center frequency<br /><br /><br />9.5 G<br /><br /><br /><br /><br />15<br /><br /><br />λ=c/f<br /><br /><br />3× /9.5=31.5<br /><br /><br /><br /><br /> Rather than placing all the PIN diodes on a single unit and rotating the entire antenna pattern to the desired angle, this approach proves to be inefficient, as replacing the PIN diodes each time would be impractical. Additionally, constructing such a system would be highly complex. To address these challenges, a more efficient solution involves quantizing the PIN diode phases into two discrete states: 0° (off) and 180° (on), which are incorporated into a single-bit unit cell with dimensions of 7 × 7. This configuration allows for the beam to be rotated to the desired angle while maintaining system simplicity. However, one important characteristic of this type of antenna is that, as the scanning coverage angle increases, the antenna gain decreases.https://mseee.semnan.ac.ir/article_9522_c5f7044e76cd6841d1b191a101b8b786.pdf