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PublicationFuzzy logic controller-proportional-integral for motor velocity control of electric rail train using DC-DC Converter(Semarak Ilmu Publishing, 2025-12)DC motor utilization is obtained in various of industrial activities and non- industrial activities. The utilization of DC motors in various purposes, it is necessary to control a velocity of the DC motor in accordance with a required velocity and this control can be conducted through a power converter. In this study, the utilization of DC chopper for velocity control of DC motor through Fuzzy logic controller proportional integral (FLC-PI) method for electric rail train is developed. The FLC-PI supports to achieve an adaptive controller. It has been adapted to achieve a good performance for auto tuning PI controller. The development process is conducted through simulation work with Matlab software. This study compares the performance between the system with PI control and FLC-PI control. The results of this study obtained the comparison between the PI and FLC-PI performances for the system without load are 1.06% and 0% overshoots respectively. In other to the comparison between the PI and FLC-PI performances for the system with load of 100 Nm are 0.5% and 0% overshoots respectively. And when the comparison between the PI and FLC-PI performances for the system with load of 200 Nm are 0.27% and 0% overshoots respectively. Thus, FLC-PI has shown more better performance than PI control.
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PublicationKnowledge management and the Fourth Industrial Revolution (4IR): a recent systematic review(Semarak Ilmu Publishing, 2025-09)Concerning the Fourth Industrial Revolution (4IR) era, associated with the rapid integration related to advanced technologies like Artificial Intelligence (AI), blockchain, and the IoT (Internet of Things) into society, effective Knowledge Management (KM) is crucial for organizations in this dynamic landscape. This systematic review explores the evolving relationship between KM and 4IR, addressing how organizations adapt their KM practices to leverage 4IR technologies and address associated challenges. Our review examines recent research in KM and 4IR, highlighting key themes, trends, and findings. Using a rigorous systematic review methodology, final primary data (n=28) published between 2021 and 2023 through advanced searches in Scopus and Econbiz databases were analyzed. The results reveal a dynamic landscape where organizations embrace 4IR technologies to enhance knowledge creation, storage, sharing, and application. The synthesis of existing research underscores the need for a strategic and adaptable KM approach in the 4IR era, emphasizing the critical roles of organizational culture, leadership, and technological infrastructure in shaping successful KM practices in this transformative context. This systematic review serves as a guide for scholars, professionals, and decision-makers, aiding them in navigating the complexities of 4IR and emphasizing the value of knowledge as a key resource in this evolving field.
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PublicationThe advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature(Semarak Ilmu Publishing, 2025-08)The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.
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PublicationElectrically small spiral PIFA for deep implantable devices(IEEE, 2020)n this paper, a miniaturized implantable circularly polarized spiral Planar Inverted-F Antenna (SPIFA) in the UHF (600-800 MHz) band is presented. This antenna is intended for deep implantable devices such as leadless pacemakers and deep brain stimulation (DBS), which facilitates the reception of RF power from an external transmitter. The antenna is electrically small, with a volume of π× 5 mm × 5 mm × 3.2 mm and a diameter of 0.022λ. The performance of the proposed antenna in terms of reflection coefficient, realized gain and axial ratio are assessed when accounting for the effects of operating in different types of human body tissues, different biocompatible materials and different thicknesses and depths of the implanted antenna. Finally, the antenna is prototyped and measured in free space, a phantom model, in a cow’s fat and muscle tissues to validate the simulation results, indicating good agreements. A realized gain around −20 dBm is achieved when operating in 50 mm depth in cow’s muscle tissue while having electrically very small dimensions compared to implantable antennas reported in the literature.
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PublicationAnalysis of the effectiveness of Metaheuristic methods on Bayesian optimization in the classification of visual field defects(MDPI, 2023)Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model’s performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.
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