M. Khalaj-Amirhosseini, “Synthesis of Circular Antenna Arrays Using Least Mean Squared Error Method” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-8, 2024.

The synthesis of circular antenna arrays to have arbitrary radiation patterns is investigated. The difference between the array factor and the desired radiation pattern is made minimum by the least mean squared error (LMSE) method. The desired pattern of circular arrays is likened to patterns of linear arrays. A matrix equation is obtained to determine the excitation currents. The performance of the proposed method is verified by some comprehensive examples.

C.J. Kendeg Onla, G. Guidkaya, D. Saba, E.D. Kenmoe Fankem and J.Y. Effa, “Development of Permanent Magnet Brushless Doubly-Fed Machines Based on Segmented Structure for Wind Power Generation,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-8, 2024.

Brushless doubly-fed machines (BDFMs) have received renewed attention in the field of variable speed constant frequency (VSCF) wind power systems. Two new permanent magnet BDFMs (PM-BDFMs) with segmented structures are proposed in this paper to improve the performance of the BDFMs. Compared to conventional BDFMs, PM-BDFMs have two rotors. The internal permanent magnet rotor provides an excitation magnetic field for power and control windings. The external rotor is based on a field modulation ring that provides magnetic coupling between the control and power winding. Due to the absence of the exciting current and excitation loss, the efficiency and power density of the proposed PM-BDFMs can be increased. The segmented structure is adopted for the outer rotor to solve a series of problems caused by the introduction of the field modulation ring. Finally, the performances of the proposed PM-BDFMs are validated by comparison with six other structures of BDFMs using finite element analysis (FEA). The results show that the proposed PM-BDFMs perform better than some existing BDFMs structures.

A. Gupta, “Enhancing Interpretability in Neural Networks through Explainable AI (XAI) Techniques,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-5, 2024.

The rapid advancement of neural networks in various applications, from healthcare diagnostics to financial modeling, has significantly improved the accuracy and efficiency of decision-making processes. However, these models often operate as black boxes, providing little to no insight into how they arrive at specific predictions. This lack of interpretability presents a major barrier to their adoption in critical domains where trust, accountability, and transparency are paramount. This study aims to address this issue by developing a novel framework that integrates multiple explainable AI (XAI) techniques to enhance the interpretability of neural networks. The proposed framework combines feature importance analysis, layer-wise Relevance Propagation (LRP), and visual explanation methods such as Gradient-weighted Class Activation Mapping (Grad-CAM). These techniques collectively offer a comprehensive view of the decision-making processes of neural networks, making them more transparent and understandable to stakeholders. Our experimental results demonstrate that the integrated XAI framework not only improves interpretability but also maintains high levels of accuracy, thereby bridging the gap between performance and transparency. This research provides a foundational basis for the deployment of interpretable neural networks in critical applications, ensuring that AI-driven decisions are reliable and comprehensible.

C.J. Kendeg Onla, G. Guidkaya, E.D. Kenmoe Fankem, A. Dountio Tchioffo and J.Y. Effa, “Reluctance Network Modeling and Optimization of Brushless Doubly-Fed Machines for Wind Power Generation,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-11, 2024.

Brushless doubly-fed reluctance machine (BDFRM) and brushless doubly-fed induction machine (BDFIM) have great potential to become third-generation electrical machines for wind turbine applications instead of doubly-fed induction machine (DFIM). However, due to their complicated operating principle, they have not yet been commercialized. In the idea of reducing the materials cost, increasing the performances of these machines, and allowing their commercial breakthrough, it is important to propose a new approach to the design and optimization of these kinds of machines. This paper contributes by combining the reluctance network (RN) model developed for those machines together with the particle swarm optimization (PSO) algorithm. Previously, a new RN model has been developed and validated by finite elements (FE) analysis for BDFRM modeling. This model simultaneously takes into account magnetic saturation, rotor motion, and stator winding distribution. It also offers an interesting trade-off between precision and computational time compared to the FE model. Therefore, the developed model is firstly extended to the BDFIM structure and then coupled to the PSO algorithm and the result is an accurate and fast BDFRM and BDFIM design optimization tool. This tool is then used to generate a 1kW machine design with optimized performances.

Y. Vahi and A. Ignise, “Designing a Multi-Agent System for Smart Grid Management,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-6, 2024.

This paper addresses the claim that by enabling intelligent, flexible, and autonomous decision-making at the local scale, a multi-agent system (MAS) can substantially strengthen the performance, reliability, and durability of smart grid operations. We discuss the design and implementation of a MAS for smart grid management through an in-depth case study, emphasizing particular design decisions, difficulties faced, and results achieved. Among MAS’s main benefits are its capacity to guarantee grid stability, reduce overload, and maximize the utilization of resources.

Z. Bin Akhtar, “Artificial Intelligence (AI) within the Realm of Cyber Security,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-11, 2024.

The integration of artificial intelligence (AI) in cybersecurity has revolutionized the field, offering advanced solutions for threat detection and mitigation. However, this integration also introduces significant vulnerabilities and threats that need to be thoroughly understood and addressed. This manuscript investigates the critical issues associated with AI in cybersecurity, identifying specific vulnerabilities and the potential exploitation of AI systems by malicious actors. The primary objective of this research exploration is to explore these challenges and provide a systematic review of existing methodologies to mitigate such risks. By addressing these problems, the manuscript contributes to a comprehensive understanding of AI’s dual-edged impact on cybersecurity and proposes strategic solutions to enhance digital security and user privacy. This research aims to bridge the gap in current knowledge, offering insights and recommendations for further developing robust AI-driven cybersecurity frameworks.