Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

High Accuracy Detection Technique for Information Data Under the Influence of Multiple Factors based on Fractional Partial Differential Equations

Version 1 : Received: 19 February 2023 / Approved: 21 February 2023 / Online: 21 February 2023 (03:04:58 CET)

A peer-reviewed article of this Preprint also exists.

zuo, yanhong; cheng, hua High Accuracy Detection Technique for Information Data under the Influence of Multiple Factors Based on Fractional Partial Differential Equations. Measurement Science and Technology 2023, doi:10.1088/1361-6501/ace6c4. zuo, yanhong; cheng, hua High Accuracy Detection Technique for Information Data under the Influence of Multiple Factors Based on Fractional Partial Differential Equations. Measurement Science and Technology 2023, doi:10.1088/1361-6501/ace6c4.

Abstract

In engineering practice, various types of information data are affected by many factors during the collection process. For example, information data measurement errors are caused by equipment performance and the working environment. During the transmission of detection information, the signal distortion caused by energy loss and signal interference causes unpredictable detection errors in the collected information data. Through the study of fractional calculus theory, it was found that it is suitable for studying nonlinear, non-causal, and non-stationary signals, and has the dual functions of improving detection information and enhancing signal strength. Therefore, under the influence of many factors, we applied the fractional difference algorithm to the field of information data processing. Multi-sensor detection data fusion algorithm based on the fractional partial differential equation was adopted to establish its online detection data. A multi-sensor detection data fusion algorithm based on a fractional partial differential equation is established, which effectively fuses the information data detection errors caused by various influencing factors and greatly improves the detection accuracy of information data. The effectiveness of this method is proved through its application in an experiment.

Keywords

multiple influencing factors; data detection error; fractional partial differential; high accuracy detection

Subject

Computer Science and Mathematics, Applied Mathematics

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