Nondestructive testing in apple variety identification has become a necessary prerequisite for the industrialization of apple production and increased international competitiveness. According to the characteristics of apple images, this paper designs and implements an apple variety identification algorithm (ACRMV) based on multiview technology. The method comprises two main steps, namely, discriminatory image block selection and a multiview classification algorithm. In the phase of image block selection, local features that occur frequently in one category but seldom in other categories are selected. In the multiview classification stage, a robust multiview classification fusion algorithm is designed based on image block features generated by different descriptors for each view. The experimental results show that ACRMV with the strategy of multifeature fusion and joint training is superior to its corresponding single-view method and to other multiview methods. The discriminative image block selection algorithm uses image blocks with greater discrimination as training data to reduce the influence of redundant data. The proposed method makes full use of the consistency and complementarity among different views to achieve the purpose of merging multiple views and jointly improving recognition performance.
In this paper, based on the fact that is still a small peasant economy in China and there are many small plots, this article studies small and medium sprayers. In this sprayer, the subdivision precision spraying control system, designed for precision agriculture applications, was simulated by the LabVIEW software, while an experimental setup was able to measure and record during laboratory experiments. The main pipeline of sprayer chose the A and B. And the two different pipelines were set in the different target spray volume. When the theoretical spray value was kept being unchanged, the flow rate was verified with the field sprayer speed which were set by the pulse generator. With that the pressure stability test was completed. Based on the analysis and experimental results, the flow control precision is 97.03%, and the pressure stability precision is 97.88%, the relative average of the pulse generator is 0.05. Finally, the subdivision system could control the flow of the two branches and was better than the traditional spray method in China, while it could achieve more precise control of spray.
Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU’s hidden states at different times is proposed to improve the GRU’s feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity.
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.