International Journal of Electrical and Computer Engineering (IJECE)
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Performance evaluation of a high-gain 50 W DC-DC flyback boost converter for variable input voltage low-power photovoltaic applications
Get PDFDC-DC boost converters are essential for stabilizing the voltage output of photovoltaic (PV) modules. This paper analyzes a unique 50 W high-gain DC-DC flyback boost converter for various input voltage PV applications. Scientific analysis was employed to determine suitable parameters for critical circuit components. Simulations were conducted to evaluate the proposed high-gain DC-DC boost converter's performance. Subsequently, a prototype of the high-gain DC boost converter was fabricated with a printed circuit board (PCB) size of 100×100 mm. The proposed prototype's performance is compared to that of conventional boost converters based on criteria such as input voltage, output voltage, component count, voltage stress, voltage gain, efficiency, and rated power. The results indicate that the proposed converter can achieve a 300 V output voltage with a 50 W power rating from variable input voltages ranging between 12 V and 36 V. The highest gain achieved was 25 with a 12 V input voltage, though at a lower power rating of 15 W. A peak efficiency of 84.30% was measured with a 24 V DC input voltage. The proposed converter's features, particularly its high step-up voltage gain, make it suitable for industrial and renewable energy applications
Characteristics of partial discharge on high-density polyethylene insulation under AC and DC voltages
Get PDFThe majority of insulation system failures in electrical grids are caused by partial discharge (PD) activity. Continuous PD activity gradually degrades the quality of insulation, potentially resulting in total breakdown. This study investigates PD activity in high-density polyethylene (HDPE) insulation, detected through the observation and measurement of PD charge using the CIGRE Method II electrode system. The objective is to analyze PD behavior in HDPE cable insulation containing cavity-type defects under alternating current (AC) and direct current (DC). The samples consist of three layers of HDPE sheets, each 1 mm thick, with an artificial circular cavity of 1 cm in diameter embedded in the middle layer. This configuration enables detailed analysis of insulation damage and degradation. The results show that HDPE performs better under DC voltage compared to AC. This is evidenced by the average PD inception voltage (Vin) under DC conditions reaching 15.5 kV, higher than the 11.8 kV observed under AC, as well as a significantly longer PD inception time (Tin) under DC conditions. Although the PD charge magnitude is nearly the same under both voltage types, the higher voltage required to trigger PD under DC indicates that HDPE exhibits superior insulation resistance to DC voltage
Privacy and confidentiality in internet of things: a literature review
Get PDFThe internet of things (IoT) is a scalable network of interconnected smart devices that aims to improve quality of life, business growth, and efficiency across multiple sectors. Since the IoT is an expanding network, a large amount of data is generated, collected, and exchanged. However, most of this data is personal data that contains private or sensitive information, which makes it a target for several cyber threats due to poor encryption, weak authentication mechanisms, and insecure communications. Therefore, ensuring the privacy and confidentiality of sensitive information remains a critical challenge. This paper presents a comprehensive literature review focusing on privacy and confidentiality issues within the IoT ecosystem. It categorizes existing research into privacy-preserving techniques, authentication and trust mechanisms, and machine learning-based solutions. Beginning by detailing the review methodology employed to gather and analyze relevant research. The review then explores recent research work related to privacy concerns and authentication and trust mechanisms, emphasizing various approaches and solutions developed to address these challenges. The paper further delves into machine learning-based solutions that offer innovative methods for enhancing privacy and confidentiality
Chaotic red-tailed hawk algorithm to optimize parameter power system stabilizer
Get PDFThis article introduces a recently created adaptation of the red-tailed hawk (RTH) algorithm. The proposed approach is a modified version of the original RTH algorithm, incorporating chaotic elements to enhance its integrity and performance. The RTH algorithm emulates the hunting behavior of the red-tailed hawk. This article demonstrates the adjustment of the power system stabilizer using the suggested technique in a case study involving a single-machine system. The suggested method was validated by benchmarking against known functions and evaluating its performance on a single-machine system in terms of transient responsiveness. The essay employs the original RTH algorithm as a means of comparison. The simulation results demonstrate that the proposed technique exhibits promising performance
Hybrid artificial intelligence approach to counterfeit currency detection
Get PDFThe use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions
Design and experimental validation of a microstrip Vivaldi antenna-based system for breast tumor detection
Get PDFBreast cancer remains one of the leading causes of death among women worldwide, highlighting the critical need for accurate, non-invasive, and cost-effective diagnostic solutions. In light of this, microwave imaging has surfaced as a promising alternative to conventional diagnostic methods. This approach leverages its capability to differentiate between healthy and cancerous tissues by examining their dielectric properties. This study presents the design, implementation, and experimental assessment of a Vivaldi antenna-based system aimed at breast cancer detection. The antenna is designed to operate within the ultra-wideband frequency range, which facilitates high-resolution imaging and effective deep tissue penetration. Data collected from tissue-mimicking phantoms reveals the system’s proficiency in identifying anomalies, showcasing a significant contrast between malignant and normal tissue regions. We analyze various performance metrics, including signal reflection, penetration depth, and imaging resolution to substantiate the system's efficacy. The results underline the significant potential of Vivaldi antennas in improving early- stage breast cancer detection, thus contributing to advancements in microwave imaging technology
Computationally efficient pixelwise deep learning architecture for accurate depth reconstruction for single-photon LiDAR
Get PDFThis work introduces a compact deep learning architecture for depth image reconstruction from time-resolved single-photon histograms. Unlike most deep learning approaches that mainly rely on 3D convolutions, our network is implemented purely with 1D convolutions without assistance from other sensors or pre-processing. Both synthetic and real datasets were used to evaluate the accuracy of our model for challenging signal-to-background ratios (SBRs), ranging from 5:1 to 1:1. Conventional maximum likelihood (ML) and another photon-efficient optimization-based algorithm were adopted for performance comparisons. Results from synthetic data show that our model achieves lower mean absolute error (MAE). Additionally, results from real data indicate that our model exhibits better reconstruction for high-ambient effects and provides better spatial information. Unlike existing 3D deep learning models, we process pixel-wise histograms continuously, rather than splitting the point cloud and stitching them afterward, which saves memory and computational resources, thereby laying a foundation for real-world embedded applications
Enhanced matrix pencil method for robust and efficient direction of arrival estimation in sparse and multi-frequency environments
Get PDFAccurate direction of arrival (DOA) estimation is vital for applications in radar, sonar, wireless communication, and localization. This paper proposes an enhanced matrix pencil method (MPM) framework to overcome limitations of traditional methods such as noise sensitivity, computational inefficiency, and challenges with sparse arrays. The framework incorporates wavelet-based denoising for improved robustness in low signal-to-noise ratio (SNR) environments and employs particle swarm optimization (PSO) to optimize key parameters, achieving a balance between accuracy and efficiency. Extending MPM to two-dimensional (2D) DOA estimation, the method precisely determines azimuth and elevation angles. Comprehensive mathematical formulations and eigenvalue computations underlie the proposed enhancements. Simulation results validate its superiority over state-of-the-art techniques like MUSIC and ES-PRIT, achieving up to 30% improvement in root mean square error (RMSE) and reducing computational time by 20%–30%. Sensitivity analysis demonstrates robustness across varying noise levels, array geometries, and multi-frequency scenarios. This scalable and efficient framework addresses critical challenges in DOA estimation and offers promising directions for future advancements in real-time and resource-constrained environments
Flow-guided long short-term memory with adaptive directional learning for robust distributed denial of service attack detection in software-defined networking
Get PDFA software-defined networking (SDN) architecture is designed to improve network agility by decoupling the control and data planes, but while much more flexible, also makes networks more vulnerable to threats, such as distributed denial of service (DDoS) attacks. In this study we present a novel detection model, the flow-guided long short-term memory (LSTM) network with adaptive directional learning (ADL), for the mitigation of DDoS attacks in software defined networking (SDN) environments. While the methodology is based on a flow direction algorithm (FDA), which analyzes traffic patterns and detects anomalies from directional flow behavior. The proposed method integrates FDA in LSTM-based threat detection frameworks within internet of things (IoT) networks, thereby yielding enhanced detection accuracy, as well as a real-time security threat response. The experimental evaluation on two benchmark datasets, namely the InSDN dataset and a real-time dataset utilizing a Mininet and POX controller setup, shows that a detection rate of 99.85% and 99.72%, respectively, thereby showcasing the proposed model’s ability to differentiate between legitimate and malicious network traffic
Robotic product-based manipulation in simulated environment
Get PDFBefore deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts