When the electrolyte is layered, excessively large and prolonged charging and exhaust currents lead to the random tendency of reactions in different parts of the electrodes. This was leading to mechanical stresses and the warming of the plates of the battery. The presence of nitric and acetic acid contaminants in the electrolyte improves the oxidation of the deeper layers of positive electrodes. Because lead dioxide has a larger volume than lead, electrodes expand and curve. Positive electrodes are subject to war and growth. The negative electrode curve is mainly caused by the nearby distracted positive people. The adjustment of the distracted electrodes can only be carried out by removing the battery. The lack of sulfate and fully charged electrodes are subject to correction because they are soft and easy to adjust. The main contribution of this proposed work is to provide an innovative solution to resolve the battery draining issues in 5G devices with the help of an alternate routing model. The proposed model will provide an idea that is used to slice distracted electrodes washed with water and placed between soft, hard boards. Generally, on the top board, a load is installed, which increases the edges of the electrodes. Electrodes are banned directly or through the amplitude of a top or hammer to avoid the destruction of the active layer; hence, the draining of the battery was reduced.
To ensure a higher quality, capacity and production of rice, it is critical to diagnose disease early in order to decrease the usage of pesticides and reduce agricultural and environmental damage. Therefore, a Multi-scale YOLO v5 detection network is proposed to resolve rice crop disease in its early stage. The experiment initially starts with the rice leaf images from the RLD dataset for pre-processing, after which data set labels are created, which are then divided into train and test sets. DenseNet-201 is used for the backbone network and depth aware instance segmentation is used to segment the different regions of rice leaf. At next, the proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used for extracting the features from the segmented image and enhances the detection of disease with different scales. The feature maps are finally identified in the detection head, where the anchor boxes are then applied to the output feature maps to produce the final output vectors by the YOLO v5 network. The experiments are conducted with RLD dataset with different existing networks. The effectiveness of the network is evaluated based on various parameters in terms of average precision, accuracy, average recall, IoU and F1 score.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.