A knowledge and data-driven optimal planning scheme for multi-modal vision transmission systems

Jia Yong; Kai Liu; Jia Yong; Kai Liu

doi:10.3934/mbe.2023530

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 11939-11956. doi: 10.3934/mbe.2023530

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A knowledge and data-driven optimal planning scheme for multi-modal vision transmission systems

Jia Yong ¹,
Kai Liu ^{1,2
,
,}

1.
Liuzhou Vocational and Technical College, Liuzhou, Guangxi 545006, China
2.
Guangxi University of Science and Technology, Liuzhou, Guangxi 545000, China

Academic Editor: Keping Yu

Received: 04 February 2023 Revised: 21 April 2023 Accepted: 03 May 2023 Published: 11 May 2023

Vision transmission systems (VTS) manages to achieve the optimal information propagation effect given reasonable strategies. How to automatically generate the optimal planning strategies for VTS under specific conditions is always facing challenges. Currently, related research studies have dealt with this problem with assistance of single-modal vision features. However, there are also some other information from different modalities that can make contributions to this issue. Thus, in the paper, we propose a data-driven optimal planning scheme for multimodal VTS. For one thing, the vision features are employed as the basic mechanism foundation for mathematical modeling. For another, the data from other modalities, such as numerical and semantic information, are also introduced to improve robustness for the modeling process. On such basis, optimal planning strategies can be generated, so that proper communication effect can be obtained. Finally, some simulation experiments are conducted on real-world VTS scenes in simulative platforms, and the observed simulation results can well prove efficiency and proactivity of the proposal.
- knowledge mechanism,
- vision transmission systems,
- optimal planning,
- multi-mmodal information integration
Citation: Jia Yong, Kai Liu. A knowledge and data-driven optimal planning scheme for multi-modal vision transmission systems[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 11939-11956. doi: 10.3934/mbe.2023530

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Abstract

Vision transmission systems (VTS) manages to achieve the optimal information propagation effect given reasonable strategies. How to automatically generate the optimal planning strategies for VTS under specific conditions is always facing challenges. Currently, related research studies have dealt with this problem with assistance of single-modal vision features. However, there are also some other information from different modalities that can make contributions to this issue. Thus, in the paper, we propose a data-driven optimal planning scheme for multimodal VTS. For one thing, the vision features are employed as the basic mechanism foundation for mathematical modeling. For another, the data from other modalities, such as numerical and semantic information, are also introduced to improve robustness for the modeling process. On such basis, optimal planning strategies can be generated, so that proper communication effect can be obtained. Finally, some simulation experiments are conducted on real-world VTS scenes in simulative platforms, and the observed simulation results can well prove efficiency and proactivity of the proposal.

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