A Constant-Power (CP) double-sided inductor-capacitor-capacitor (DS-LCC) wireless battery charger without any switch-controlled capacitor (SCC) is proposed in this paper. This CP wireless charger incorporates a semi-active rectifier (SAR) in the secondary side, and a control strategy is developed to achieve CP charging. This design eliminates the need for additional SCCs, reducing cost and system complexity. With the DS-LCC compensation topology, this wireless charger has no safety problem when misalignment. The feasibility of the proposed charger is validated through simulation, demonstrating that efficiency is maintained between 93.0% and 94.0%, which surpasses that of the state-of-the-art DS-LCC CP charger.
Floating waste detection is a critical task for environmental monitoring and preservation, particularly in water bodies polluted by plastic waste and other debris. Traditional detection models like YOLOv5s struggle with detecting small, occluded, and poorly illuminated floating objects due to suboptimal feature extraction and limited datasets. This study proposes an enhanced model, AAYOLO (Atrous-Attention YOLO), which integrates atrous convolutions and enhanced channel attention (ECA) mechanisms to improve feature extraction across various scales. Atrous convolutions are applied at strategic levels of the network (P1/2, P3/8, and P5/32) to expand the receptive field, while ECA effectively adjusts feature weights to enhance object detection under varying lighting conditions. The proposed model is evaluated on the FloW-Img dataset, a benchmark specifically designed for floating waste detection, consisting of high-resolution images of plastic bottles found in aquatic environments. Experimental results demonstrate that AAYOLO outperforms the baseline YOLOv5s model, achieving a higher mean Average Precision (mAP@0.5) of 0.892 compared to 0.872, along with improvements in precision (0.878 vs. 0.858) and recall (0.836 vs. 0.820). This approach presents a promising solution for real-time, accurate detection of floating debris, contributing to improved waterway monitoring and environmental protection.
Tropical thunderstorms, characterized by intense convective activity, pose significant risks in equatorial regions like Malaysia, where they often produce severe wind gusts and frequent lightning. The relationship between lightning activity and windstorm dynamics remains underexplored, particularly in tropical mesoscale convective systems (MCS). This research investigates the spatial distribution of lightning flash types, which are cloud flashes (IC), negative cloud-to-ground flashes (-CG), and positive cloud-to-ground flashes (+CG), across radial zones (5 km, 10 km, 15 km) during a windstorm event in Johor Bahru on 30 November 2022. Quantum Geographic Information System (QGIS) software was used to map and analyze the spatial distribution of lightning flashes. IC flashes dominated near the thunderstorm core (15.76% within 5 km), while CG flashes were most prevalent (84.24% within 5 km), indicating strong updrafts and charge separation. In contrast, +CG flashes were rare near the core but increased slightly at greater distances (2.78% at 10 km), suggesting charge redistribution during thunderstorm decay. The findings demonstrate that fine-scale radial zoning of lightning data can improve storm structure analysis and early warning systems in tropical regions.
Passive acoustic classification acts as a major function in automated ship identification, where deep learning models are used to recognize ship types from radiated noise. A major challenge lies in embedding domain-specific knowledge into these models to improve feature discrimination. This study proposes a classifier that integrates Coherently Averaged Power Spectral Estimation (CAPSE) method with a ResNet50-based classifier. Initially, ships' frequency spectrums are processed using CAPSE analysis, enabling the extraction of key machinery characteristics, and are transformed into LOFAR grams. These time-frequency representations are subsequently processed by a ResNet50 network, which leverages deep convolutional architectures to capture hierarchical feature representations. Taking advantage of transfer learning with ResNet50 deep feature extraction capabilities, the proposed model effectively learns complex patterns within the data, leading to improved classification performance. Evaluation on the standard DeepShip dataset showed that the proposed methodology attained an average classification accuracy and F1-score of 89.93%, while maintaining good generalization and robustness. tSNE feature visualization demonstrated improved class separation by the trained model, with most samples correctly clustered. This study improves underwater acoustic classification by showing that combining deep learning with better feature extraction can lead to highly accurate results.
Hybrid renewable energy system (HRE) sizing is vital for maximizing both economic and environmental performance in grid-connected systems. This study develops a comparative optimization framework for an HRE serving a camp in the southeastern region of Libya, integrating photovoltaic (PV) arrays, wind turbines (WT), battery storage systems (BT), fuel cells (FC), hydrogen tanks (HT) and an Electrolyzer (EL). Three objective functions, Levelized Cost of Energy (LCOE), Grid Reliance Fraction (GRF) and Pollutant Emission Coefficient (PEC), are employed to quantify techno-economic efficiency and environmental impact. To this end, three widely used metaheuristic algorithms; Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO), are employed under identical settings to identify optimal system configurations. Each algorithm is run for a fixed number of iterations, and their diversity and convergence behaviors are monitored to ensure a fair benchmark. The comparative results show that PSO yields the most cost-effective design, achieving an LCOE of $0.1448/kWh, a reduction of 13.5% and 1.3% relative to GA and GWO, respectively and a GRF of 15.73%. These findings demonstrate that PSO strikes the best balance between cost, reliability and computational efficiency for this HRE case study. The framework and insights presented here can guide planners and engineers in selecting the most suitable optimization method for sustainable energy-system design.
