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Modulation and control of transformerless boosting inverters for three-phase photovoltaic systems: comprehensive analysis
Abstract This paper examines the performance of three power converter configurations for three-phase transformerless photovoltaic systems. This first configuration consists of a two-stage DC–DC–AC converter comprised of a DC–DC boost chopper and a three-phase voltage source inverter. The second and third configurations are the single-stage quasi-Z-source inverter (qZSI) and the split-source inverter (SSI). The performance of the presented topologies has been analyzed and compared in terms of topological requirements, modulation techniques, and control of output voltage, considering both ideal and parasitic cases. Moreover, the voltage and current stresses on the devices, passive elements, and efficiency are also addressed. Simulation and experimental testing were subsequently carried out to validate the analysis and evaluate the performance of the proposed topologies.
Hydrogen Controls the Heavy Atom Roaming in Transient Negative Ion
Photonic axion insulator with non-coplanar chiral hinge transport
Effect of alkaline treatment on the thermal and mechanical properties of sugar palm fibre reinforced thermoplastic polyurethane composites
Decoding Order and Disorder in Proteins by NMR Spectroscopy
Analysis of the characteristics of university common spaces that affect university students’ psychological restoration
<i>N</i>MR<sup>2</sup>-Based Drug Discovery Pipeline Presented on the Oncogenic Protein KRAS
Blood glucose reduction associated with wholewheat noodle diet in rats with type 2 diabetes mellitus
Space Charge, Modulating the Catalytic Activity of Single-Atom Metal Catalysts
Consistency regularization for few shot multivariate time series forecasting
Observation of the Assembly of the Nascent Mineral Core at the Nucleation Site of Human Mitochondrial Ferritin
Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment
Environmentally Benign and Long Cycling Mn-Ion Full Batteries Enabled by Hydrated Eutectic Electrolytes and Polycarbonyl Conjugated Organic Anodes
Hierarchical contrastive learning for multi-label text classification
After 75 Years, an Alternative to Edman Degradation: A Mechanistic and Efficiency Study of a Base-Induced Method for N-Terminal Peptide Sequencing
Association between systemic immune-inflammation index and chronic bronchitis: NHANES 2001–2018
Bidirectional Intramolecular Singlet and Triplet Energy Transfer in Tetracene-Ultrasmall Gold Nanocluster Dyads: An Evaluation of the Triplet Behavior of Gold Nanoclusters
Yolo-pest: an optimized YoloV8x for detection of small insect pests using smart traps
Abstract Fruit flies and fall-armyworm are one of the major insect pest that adversely affect fruit and crops, whereas fall-armyworm is also a highly destructive pest in maize crop but also damage other economically important field crops and vegetables. Adults of both pests can fly, making it hard to monitor them in the field. This study focuses on fine-tuning the YoloV8x model for automated monitoring and identifying insect pests, like fruit flies and fall-armyworms, in open fields and closed environments using IoT-based Smart Traps. The conventional techniques for monitoring of these insect pests involve pheromone attractants and sticky traps that require regular farm visits. We developed an IoT-based device, called Smart Trap, that attracts insect pests with pheromones and captures real-time images using cameras and IoT sensors. Its main objective is automated pest monitoring in fields or indoor grain storage houses. Images captured by smart traps are transmitted to the server, where Yolo-pest, a fine-tuned YoloV8x model with customized hyperparameters performs in real time for object detection. The performance of the smart trap was evaluated in a mango orchard (Fruit Flies) and maize field (fall Armyworm) in an arid climate, achieving a 94% average detection accuracy. The validation process on grayscale and coloured images further confirmed the model’s consistent accuracy in identifying insect pests in maze crop and mango orchards. The mobile application also enhances the practical utility as having a user-friendly interface for real time identification of insect pest. Farmers can easily acces the information and data remotely that empowering them for efficient pest maangment.