Sensing Model and Performance of the Surface Defect Photonic Crystal with Porous Silicon

Ying, 陈颖 Chen; Huiqing, 范卉青 Fan; Wenyue, 王文跃 Wang; Qiguang, 朱奇光 Zhu; Weidong, 陈卫东 Chen

doi:10.3788/aos201535.0523001

Cited by 3 publications

(3 citation statements)

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“…As required, fuzzy processing the information received by sensors, the output signal is {near, middle and far}, in order to facilitate the processing of information and make subsequent planning. Its membership function [3] (Fig. 3):near=ND(near distance) ；middle=MD(…”

Section: Obstacle Avoidance Methods Improving and Process Analysismentioning

confidence: 99%

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

Wang

Zhao

Chen

2013

AMM

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Proposing an obstacle avoidance method for mobile robot under unknown environment, which makes use of multiple ultrasonic sensors coordinating with each other and collects position information of obstacles followed by information fuzzy processing, applies artificial potential field method with improved potential function to project the travel path of the robot. It has solved the "deadlock" problem of the traditional artificial potential field method and achieved obstacle avoidance of mobile robot under unknown environment. By simulation analysis, robot obstacle avoidance can be implemented flexibly using this method.

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Section: Obstacle Avoidance Methods Improving and Process Analysismentioning

confidence: 99%

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

Wang

Zhao

Chen

2013

AMM

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“…Lightweight networks have the signi cant advantages of small size and few parameters and can achieve classi cation accuracy comparable to traditional CNN. Being able to ensure the accuracy and e ciency of grain recognition despite limited resources provides strong support for the intelligent development of the agricultural eld [13]. Gilanie et al [14] collected a dataset of seven rice seeds from Pakistan under natural light and tested it with a lightweight CNN model RiceNet.…”

Section: Introductionmentioning

confidence: 99%

Wheat variety classification based on McaxseNet lightweight model

Xue,

Liu,

Han

et al. 2024

Preprint

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In the context of traditional wheat cultivation, issues such as a lack of seedlings and the prolonged monopoly of seedlings are frequently encountered. These phenomena have a considerable impact on both grain yield and the income of farmers. The traditional methods of identifying wheat seedling varieties rely on manual observation and measurement. However, these methods are time-consuming, labor-intensive, and susceptible to subjective influences, resulting in poor timeliness and robustness. The detection accuracy and speed of wheat seedling variety identification and classification can be improved by using deep learning models. However, there is still relatively little research on this subject. In this study, a McaxseNet lightweight model wheat variety identification classification method is proposed. The method is based on the MobileVit-XS network model, which efficiently identifies global feature information. The introduction of the CBAM attention mechanism in the MV2 module enables the MV2 module to be more focused and accurate when processing features. It is proposed that the XSE module incorporate the SE attention mechanism in the improved Xception module, followed by residual linking, to address the gradient vanishing problem and enhance the feature extraction capability of the model, while simultaneously improving its robustness. The McaxseNet lightweight model was trained on 30 datasets in a wheat test field, comprising a total of 29,673 images of wheat seedlings from 30 wheat varieties. The average accuracy of the dataset is 98.27%, which represents a 5.94% improvement over that of the MobileViT model. Furthermore, the model's number of parameters is only 10.51MB, and the execution time for processing a single wheat seedling image is 24.1ms. In comparison to other convolutional neural network models, McaxseNet exhibits a higher degree of accuracy while maintaining a relatively low number of parameters. In comparison to other convolutional neural network models, McaxseNet exhibits a higher degree of accuracy while maintaining a relatively low number of parameters.

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“…Genetic algorithm is random search algorithm based on natural selection and genetic principles, and the algorithm searches the optimal result in overall situation by multi-point search [2] . Artificial Neural Networks method simulates the human brain, and this method has the high parallel efficiency and learning ability, and can convergence the optimal path [3] .Fuzzy algorithms uses approximate natural language and better processes the uncertainty and imprecision of data [4] . Particle Swarm Optimization algorithm belong to the evolution algorithm and adopts the iteration to search the optimal result and starts the random solution.…”

Section: Introductionmentioning

confidence: 99%

Study on Path Planning Method for Mobile Robot Based on Fruit Fly Optimization Algorithm

Jiang

Jian-zhong

2014

AMM

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A path planning method based on fruit fly optimization algorithm was proposed. An optimization algorithm by the foraging process of fruit fly was presented, and the mathematical model of fitness function was established. The algorithm steps employing the LabVIEW platform were achieved. The experiments of path planning were carried out. The experimental results show that the optimization algorithm can achieve the path planning and avoidance of mobile robot, and thus to verify the feasibility.

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Sensing Model and Performance of the Surface Defect Photonic Crystal with Porous Silicon

Cited by 3 publications

References 0 publications

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

Wheat variety classification based on McaxseNet lightweight model

Study on Path Planning Method for Mobile Robot Based on Fruit Fly Optimization Algorithm

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