Research on Robust Design and Optimization of Embedded Network Electronic Information System

Pan, Yang; Xin-liang, Zhou; Jia, Jianhao

doi:10.1088/1742-6596/1550/3/032119

Cited by 1 publication

(1 citation statement)

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“…With the continuous rise of China's network technology level, the built-in information systems for electronic networks have entered a rapid development, and it also makes its role in application construction more prominent. [1]. Vigorously promoting informatization is a strategic measure of China's overall modernization drive and an urgent need and inevitable choice for building an innovative country [2].…”

Section: Introductionmentioning

confidence: 99%

Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation

Mei¹,

Wang

2022

Journal of Sensors

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In the research process of robustness refinement solution of built-in information systems for electronic networks, there are too many factors related to robustness in the current system robustness design, and different factors have different influences on robustness. In stochastic programming problems, uncertain variables usually obey a certain probability distribution, but in real decision-making, these determined distributions are often unknown or we only know part of the information of the distributions, and distributed robust refinement solution is just an effective solution to solve uncertain problems. The robustness measurement solution of information systems for electronic networks is analyzed. The robust refinement of information system is deeply studied by nonparametric density estimation solution, which is based on the strict robustness requirements put forward by users. Based on the research results of interdependent network theory and aiming at “improving the robustness of electronic information system,” this paper makes an in-depth study on the robustness refinement strategy of power information system. The comparison between the companies that adopted the robust refinement of built-in information systems for electronic networks based on nonparametric density estimation and the companies that did not adopt it shows that the refinement rate of the companies that adopted it in the first three years was 82%, while that of the companies that did not adopt it was only 57%, and the overall misjudgment rate was 43%. Therefore, it is proved that using the proposed reliability refinement solution to optimize, the embedded system can improve the service life, modeling accuracy, and availability of the system and has certain practicability.

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Section: Introductionmentioning

confidence: 99%