Journal of Modeling and Simulation in Electrical and Electronics Engineering
Volume:3 Issue: 2, Spring 2023

In today's digital age, we are bombarded with images from the internet, social media, and online magazines. It is fascinating how we can remember so many of these images and their details. However, not every image is equally memorable; some stay with us more than others. Scientists have explored why this is the case. In our research, we are particularly interested in how images that showcase Iranian life and culture stick in the memories of Iranian adults. To investigate this, we created a new collection called the SemMem dataset, which is full of culturally relevant images. We adapted a memory game from earlier studies to test how memorable these images are. To analyze memorability, we used two deep learning architectures, ResNet 50 and ResNet 101. These architectures helped us estimate which images are likely to be remembered. Our findings confirmed that images connected to Iranian culture are indeed more memorable to Iranians, highlighting the impact of familiar cultural elements on memory retention.

Loss of excitation (LOE) phenomenon can be hazardous for both the generator and power network stability. Previously presented LOE protection techniques are usually on the basis of the generator terminal impedance trajectory which have various drawbacks. Therefore, this study proposes a fast and reliable setting-free LOE detection method.  For this aim the derivative of various parameters of the generator including resistance ( ), reactance ( ), reactive power ( ) and flux ( ) have been utilized in order to propose three different combined indices. Consequently the performance of the proposed protection algorithm has been evaluated by simulations, considering all the introduced indices in order to select the best one. The simulations have been carried out in MATLAB software, under different operating scenarios. The extracted results demonstrate the best performance of the last combined index, which is based on using the derivative of  ,  and . This index also shows amazing speed, accuracy, and reliability in detection of LOE and discrimination of LOE with stable power swing (SPS), compared with the conventional impedance-based methods.

Predicting pedestrians' intentions to cross paths with cars, particularly at intersections and crosswalks, is critical for autonomous systems. While recent studies have showcased the effectiveness of deep learning models based on computer vision in this domain, current models often lack the requisite confidence for integration into autonomous systems, leaving several unresolved issues. One of the fundamental challenges in autonomous systems is accurately predicting whether pedestrians intend to cross the path of a self-driving car. Our proposed model addresses this challenge by employing convolutional neural networks to predict pedestrian crossing intentions based on non-visual input data, including body pose, car velocity, and pedestrian bounding box, across sequential video frames. By logically arranging non-visual features in a 2D matrix format and utilizing an RGB semantic map to aid in comprehending and distinguishing fused features, our model achieves improved accuracy in pedestrian crossing intention prediction compared to previous approaches. Evaluation against the criteria of the JAAD database for pedestrian crossing intention prediction demonstrates significant enhancements over prior studies.

Modeling of power electronic converters plays a significant role in examining the behaviour and designing control systems. Dual Active Bridge (DAB) converters, due to many advantages such as inherent soft-switching, bidirectional power transfer, and higher energy density, are used in various applications such as SST transformers, smart grids, and electric vehicle battery chargers. In this paper, a new reduced-order model for a DAB converter is introduced by modeling nonlinear elements such as semiconductor devices and transformers. By considering all power loss elements, and input/output filters, the modeling becomes more realistic. The performance and accuracy of the proposed model is improved compared to conventional reduced-order methods.  Small signal modeling for the DAB converter is curried out and control transfer functions of the system are investigated. Additionally, frequency response analysis of the proposed model under different conditions is compared with the detailed model of the DAB converter containing nonlinear elements implemented in PLECS software. Simulation results demonstrate a satisfactory accuracy of the proposed model in assessing the performance and dynamic behavior of the DAB converter under various operating conditions.

In this paper, a microstrip dual-band bandpass filter containing a disk resonator and a U-shaped waveguide is designed. The proposed structure generates two pass-bands with resonance frequencies of 3.7 and 5.7 GHz which can be used for Wi-MAX and WLAN applications, respectively. It is worth mentioning that two resonance frequencies are located in a relatively wide frequency range of 0 to 10 GHz. The simulation results show that the insertion losses and return losses of two pass-bands are better than 0.62, 0.75 dB, and 21.9, 20.1 dB, respectively. Furthermore, its total size is equal to 12.9×9.5 mm2. In addition to the simple structure of the proposed filter, its second resonance frequency can be tuned by changing only the radius of the disk resonator, without the need to change the overall structure or add another element to the filter structure. Furthermore, this filter's symmetrical structure has caused no distinction between the input and output ports, which facilitates the mass production of this structure. The other remarkable features of the suggested filter are its compact size, low insertion loss, high return loss, sharp transition bands, high attenuation level in the stop-bands, wide upper stop-band bandwidth, and sharpness of transient bands.

The efficient operation of electrical distribution systems is critical in modern industry, as it directly impacts the reliability, stability, and cost-effectiveness of delivering electricity to consumers. Capacitors are included in radial distribution systems to improve voltage profile and minimize losses by providing reactive power. Consequently, losses are reduced as the reactive power flow component is compensated. Furthermore, re-configuration of the distribution network, which involves altering the open/closed status of switches, is a vital approach that affects the steady flow of electricity through the network. Network reconfiguration and optimal capacitor placement are essential techniques for enhancing the performance of the distribution networks. This study utilizes the Cuckoo Search Algorithm (CSA) to solve the problems of network reconfiguration and optimal capacitor placement. The primary objective is to minimize power losses while ensuring that the voltage profile and reliability of the distribution system satisfy industry-level standards. The proposed method was tested on the IEEE 33 bus network. Five different scenarios were considered. The simulations were conducted in MATLAB software. The acquired improvements in the power loss reduction and voltage profile corroborate the effectiveness of this novel approach. Compared to previously explored methods, the results of the proposed scheme for solving optimal capacitor placement and network reconfiguration individually were found to be more effective in terms of reducing power loss (3.12% and 4.02%, respectively).

Controlling the speed and direction of DC motor rotation is vital in various industrial and educational applications. However, many college students do not understand the characteristics and control of DC motors. Therefore, this study aims to study the simulation of DC motors using the L298 motor driver and Arduino platform with the help of Proteus software. This study focuses on the simulation of controlling the speed and direction of DC motors using a potentiometer as a speed controller and a switch to change the direction of motor rotation (clock and anti-clock). The ACS712 current sensor and voltage sensor are used to monitor the current, voltage, and power displayed on the LCD screen. By using Arduino as a microcontroller, this system allows precise control and real-time monitoring of the motor's electrical parameters. The simulation results show that the designed system can control the speed and direction of DC motors well and provide important information about the operational characteristics of the motor. This study not only offers theoretical insights but also practical applications in the development of efficient and effective motor control systems. Thus, this study is expected to be an essential reference for students, researchers, and practitioners in understanding and developing better motor control systems.