International Journal of Electrical and Computer Engineering (IJECE)
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Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines
Get PDFThis paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method
Integration of ultra-wideband elliptical antenna with frequency selective surfaces array for performance improvement in wireless communication
Get PDFThe integration of frequency selective surfaces (FSS) with antennas has gained significant attention due to its ability to enhance key radio frequency (RF) performance parameters such as gain, directivity, and bandwidth, making it highly beneficial for modern wireless communication systems. In this work, we propose and investigate an ultra-wideband (UWB) elliptical antenna operating within the 5.2 to 10 GHz frequency range. To further improve its performance, we integrate the antenna with a 13×13 FSS array. The impact of the FSS on the antenna’s characteristics is analyzed, showing a remarkable gain enhancement from 2.6 dBi (without FSS) to 10.05 dBi (with FSS). These results confirm the effectiveness of FSS integration in optimizing UWB antenna performance, making it a promising approach for advanced wireless communication applications
Low-power and reduced delay in inverter and universal logic gates using Hvt-FinFET technology
Get PDFThe rapid scaling of conventional complementary metal–oxide– semiconductor (CMOS) metal–oxide–semiconductor field-effect transistors (MOSFETs) led to significantly increasing power dissipation, delay, and short channel effects (SCEs). Fin field-effect transistor (FinFET) technology is a better alternative to MOSFETs with superior electrostatic control, low power, and reduced leakage current. FinFETs have been chosen for their efficiency in overcoming these issues. This work focuses on the design of high-threshold voltage fin field-effect transistor (Hvt-FinFET) 18 nm technology-based inverter with optimized parameters and implementing universal gates NAND and NOR in Cadence Virtuoso tool. These three gates are basic building blocks for any complex digital system design. The results demonstrate significant improvement in power and reduced propagation delay in comparison with conventional CMOS technology. The Hvt-FinFET inverter obtained power dissipation and delay reduction of 13.63% and 33.33%, respectively. Power and delay optimization of 29.10% and 11.8% have been obtained in the NAND gate and 31.28% and 29.08% in the NOR gate when compared to conventional CMOS circuits. The results demonstrate significant improvements in power savings, reduced propagation delay, and superior energy efficiency, validating the effectiveness of Hvt-FinFET technology for next-generation very large scale integration (VLSI) applications
Synergetic synthesis of a neural network controller for an adaptive control of a nonlinear dynamic plant
Get PDFThe paper considered issues the development of a self-organizing controller (SC) based on a neuro-fuzzy network that can approximate a nonlinear function with arbitrary accuracy. The SC in the form of neuro-fuzzy networks, possesses the nonlinear property that allows for an increased range of control over the plant, which imparts adaptive properties to the control systems. To reduce the dimensionality of the plant, it is proposed to split the model of the system into sub models with smaller dimensionality, due to which the duration of training of the neuro-fuzzy network is reduced and asymptotic stability is ensured as a whole. The proposed approach is also applicable to multidimensional control systems of the nonlinear dynamic plants. The simulation results showed that the synthesized SC provides good tracking characteristics, the tracking efficiency is no more than 10%, which meets the requirement of the control system
Exploring feature selection method for microarray classification
Get PDFEffectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data
AI-MG-LEACH: investigation of MG-LEACH in wireless sensor networks energy efficiency applied the advanced algorithm
Get PDFWireless sensor networks (WSNs) play a crucial role in data collection across various fields like environmental monitoring and industrial automation. The energy efficiency of these networks, powered by limited-capacity batteries, is key to their performance. Clustering protocols such as low- energy adaptive clustering hierarchy (LEACH) are widely used to optimize energy consumption. To enhance LEACH’s performance, MG-LEACH was introduced, improving cluster head selection to extend network lifespan. This study compares MG-LEACH with AI-MG-LEACH, which incorporates artificial intelligence (AI) to further improve energy efficiency by selecting cluster heads based on factors like residual energy. Simulations show AI-MG-LEACH reduces energy consumption, extends network life, and enhances data reliability, outperforming MG-LEACH
Machine learning-based classification of local muscle fatigue using electromyography signals for enhanced rehabilitation outcomes
Get PDFMuscle fatigue is a key factor affecting rehabilitation progress, safety, and patient engagement. Accurate detection of fatigue during physical activity remains a challenge, particularly in clinical and remote settings. This study presents the development of an Internet of things-based system for classifying local muscle fatigue using surface electromyography (EMG) signals and machine learning. A wearable device was used to collect real-time EMG data and subjective fatigue ratings from 10 healthy participants during sustained isometric grip exercises. Feature extraction was performed on-device, and the data were transmitted wirelessly for analysis. Machine learning models including logistic regression, decision tree (DT), random forest, and extreme gradient boosting (XGBoost) were trained to classify fatigue states. The DT model achieved the highest accuracy of 90.7%, with a precision of 90.7% and a recall of 90.9%. SHAP analysis revealed time under load, smoking, and alcohol use as the most influential factors in fatigue classification. These results show that wearable EMG devices combined with smart algorithms are effective for real-time fatigue monitoring during rehabilitation
New approximations for the numerical radius of an n×n operator matrix
Get PDFMany mathematicians have been interested in establishing more stringent bounds on the numerical radius of operators on a Hilbert space. Studying the numerical radii of operator matrices has provided valuable insights using operator matrices. In this paper, we present new, sharper bounds for the numerical radius 1/4‖|A|2+|A*|2‖≤w2(A)≤1/2‖|A|2+|A*|2‖, that found by Kittaneh. Specifically, we develop a new bound for the numerical radius w(T) of block operators. Moreover, we show that these bounds not only improve upon but also generalize some of the current lower and upper bounds. The concept of finding and understanding these bounds in matrices and linear operators is revisited throughout this research. Furthermore, the study emphasizes the importance of these bounds in mathematics and their potential applications in various mathematical fields
Design strategies for solar photovoltaic integration in rural areas
Get PDFThis study explores the optimization of photovoltaic (PV) systems in the Sungai Tiang Camp region, Malaysia, with a focus on determining the ideal tilt angles to maximize energy generation in a tropical environment while incorporating a cost analysis. While existing studies optimize tilt angles for energy maximization in temperate regions, this study addresses the unique climatic and socio-economic conditions of rural Malaysia. Unlike fixed-tilt assumptions common in prior work, this research explores cost-effective, manually adjustable systems tailored for local weather patterns and rural affordability. To address this, the study examines the relationship between tilt angle, solar irradiance, temperature and output power. The results are analyzed to identify optimal configurations. Results reveal that tilt angles between 5° and 10° deliver the highest energy output, with slight seasonal adjustments for efficiency improvement. These findings align with Malaysia's tropical solar profile, offering practical insights for micro-scale solar deployments in similar climates. By addressing the unique needs of remote areas, this research contributes to bridging the gap in localized PV studies. Its outcomes not only enhance the understanding of solar PV performance in tropical conditions but also provide valuable guidelines for rural electrification and sustainable energy solutions in equatorial regions worldwide
Applications of satellite information for rainwater estimation and usage: a comprehensive review
Get PDFGlobal climate change introduces significant uncertainty in water resource availability, making precipitation studies essential for societal sustainability. Satellite precipitation products (SPPs) have emerged as a vital alternative and complement to traditional meteorological station data for hydrological and climate research. This review examines scientific literature on SPP applications for daily, monthly, and annual rainfall estimations globally. Eleven widely used SPPs were identified, with the tropical rainfall measuring mission (TRMM) and climate hazards group infrared precipitation with station data (CHIRPS) standing out due to their frequent usage, high resolution, and extensive data records. A growing trend in research utilizes SPPs for hydrological studies and validates their estimates by contrasting satellite information with ground station measurements using continuous and categorical statistics. TRMM and CHIRPS, in particular, show precipitation accuracies closer to station data, influenced by local topography and climatology. Furthermore, SPP data, combined with geographic information systems (GIS), proves useful for identifying potential rainwater harvesting sites, offering an alternative information source to address water availability crises in drought-prone areas