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Analysis of Ice and Snow Path Planning System Based on MNN Algorithm

Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow...

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Detalles Bibliográficos
Autores principales: Jin, YinZhe, Li, Bai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920659/
https://www.ncbi.nlm.nih.gov/pubmed/35295272
http://dx.doi.org/10.1155/2022/1586006
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author Jin, YinZhe
Li, Bai
author_facet Jin, YinZhe
Li, Bai
author_sort Jin, YinZhe
collection PubMed
description Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow sports lovers are eager to solve this problem. This paper designs a path planning method based on three-dimensional ice and snow model. The path planning method of moving snow and ice based on MNN (Multiclass Neural Networks) algorithm is studied from many aspects. MNN algorithm is used for comprehensive analysis and evaluation. The mobile phone provides data information on key nodes, air resistance, momentum change, ice and snow movement track, and so on. The results show that the ice and snow path planning system based on MNN algorithm designed in this paper has the advantages of high feasibility, high data accuracy, and good prediction effect and can effectively improve the efficiency of ice and snow path planning.
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spelling pubmed-89206592022-03-15 Analysis of Ice and Snow Path Planning System Based on MNN Algorithm Jin, YinZhe Li, Bai Comput Intell Neurosci Research Article Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow sports lovers are eager to solve this problem. This paper designs a path planning method based on three-dimensional ice and snow model. The path planning method of moving snow and ice based on MNN (Multiclass Neural Networks) algorithm is studied from many aspects. MNN algorithm is used for comprehensive analysis and evaluation. The mobile phone provides data information on key nodes, air resistance, momentum change, ice and snow movement track, and so on. The results show that the ice and snow path planning system based on MNN algorithm designed in this paper has the advantages of high feasibility, high data accuracy, and good prediction effect and can effectively improve the efficiency of ice and snow path planning. Hindawi 2022-03-07 /pmc/articles/PMC8920659/ /pubmed/35295272 http://dx.doi.org/10.1155/2022/1586006 Text en Copyright © 2022 YinZhe Jin and Bai Li. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jin, YinZhe
Li, Bai
Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title_full Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title_fullStr Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title_full_unstemmed Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title_short Analysis of Ice and Snow Path Planning System Based on MNN Algorithm
title_sort analysis of ice and snow path planning system based on mnn algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920659/
https://www.ncbi.nlm.nih.gov/pubmed/35295272
http://dx.doi.org/10.1155/2022/1586006
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