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El Hasnaoui, Yassine
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Electrical & Electronics Engineering

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Design of higher gain 1×4 cylindrical dielectric resonator antenna for mm-wave base stations El Hasnaoui, Yassine; Mazri, Tomader
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7966

Abstract

In this work, a cylindrical-shaped dielectric resonator antenna (DRA) with four elements is designed and presented at 28 GHz for millimeter-wave applications. The microstrip line is designed to excite the dielectric resonator (DR) with a relative permittivity of 8.3, and then the proposed single cylindrical DR antenna has been mounted on a FR4-Epoxy substrate with a relative permittivity of 4.4 and a thickness of 1.8 mm. However, the optimized single element was used to create a particular array for improving the gain and achieving the required antenna performance. The resulting antenna enabled a response range for the reflection coefficient from 26.8 GHz to 30.6 GHz, which covers the operational frequency. This allows for a maximum gain of 19.07 dB, an impedance bandwidth of 3.8 GHz, and a total efficiency of 80.62%, meeting the requirements for millimeter-wave and fifth-generation applications.
Optimizing fatigue life predictions for scraper rings: classical vs modern models Fatihi, Sophia; El Hasnaoui, Yassine; Ouabida, Elhoussaine; Mharzi, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9256

Abstract

This study provides a comprehensive comparative evaluation of classical and modern predictive models for fatigue life in scraper rings of internal combustion engines, which operate under high thermo-mechanical stresses. Accurate fatigue life predictions are essential for optimizing engine component design, preventing both over- and under-engineering while ensuring long-term reliability. The effectiveness of both traditional models and newer advanced approaches was analyzed using loading profiles that replicate real-world engine operating conditions. Results indicate that stress-life models offer more reliable predictions for high-cycle fatigue scenarios, while strain-life models perform better under low-cycle fatigue conditions. Furthermore, fracture mechanics models show great promise in predicting crack propagation and identifying failure mechanisms. Detailed inspections and Légraud-Poirier (LP) tests confirmed fatigue-induced cracking at critical locations of the scraper rings, emphasizing the importance of incorporating multi-axial loading in fatigue assessments. The findings underscore the necessity for using comprehensive loading profiles and thorough inspections to enhance the accuracy and dependability of fatigue life predictions, which are critical for improving the performance and durability of engine components.
Deep learning-based cellular traffic prediction for 4G long-term evolution networks using three models Shindou, Hassnaa; El Hasnaoui, Yassine; Mohamed Nabil, Srifi
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9221

Abstract

Wireless networks can be seen as the essential element of contemporary communication systems, connecting, in one way or another, billions of people and technologies all over the world. As a result, there is more of requirement from the area of application for models, which should be able to help in the analysis of the time series of mobile traffic to enhance the quality of service (QoS) in the present networks as well as in the future ones. The primary objective of this article is to develop effective artificial intelligence (AI) models for traffic load prediction in cellular networks. To achieve this, we employ three models; gated recurrent unit (GRU), bidirectional long short time memory (BiLSTM), and long short time memory (LSTM), to make numerical estimates of the network traffic at 4G long-term evolution (LTE) cell towers. The empirical results indicate that the BiLSTM model outperforms both the LSTM and GRU models, achieving root mean squared error (RMSE), mean absolute error (MAE), and R2 values of 86.64, 67.12, and 93.23%, respectively. Although this research focuses on traffic modeling for 4G LTE networks, the proposed models hold significant value for the development and optimization of the upcoming generations.
Co-Authors Fatihi, Sophia Mazri, Tomader Mharzi, Hassan Mohamed Nabil, Srifi Ouabida, Elhoussaine Shindou, Hassnaa