Visibility Characteristics over the South China Sea during 1980–2018 Based on Gridded Data Generated by Artificial Neural Network

Li, Lifeng; Shan, Yulong; Zhao, Shuang

doi:10.1007/s13351-021-0194-z

Cited by 1 publication

(1 citation statement)

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“…As for the field of spatio-temporal information processing, because it involves multiple disciplines and has the attribute of interdisciplinary integration, the research in this field has been widely concerned. As a technology based on spatial information, it covers advanced technology [1][2] in different fields, including information processing technology in computer field, information transmission technology in network communication field, data acquisition technology in remote sensing field, communication technology in global positioning system, data interaction technology in wireless sensor network field, and specific information recognition technology in GIS. Through the integration of the above multiple technology systems, the acquisition, processing, analysis and application of spatial information can be realized in an integrated way, and a relatively complete information technology architecture [3][4] can be built.…”

Section: Introductionmentioning

confidence: 99%

Unified method of power grid spatio-temporal data based on Beidou grid code

Peng,

Chen,

Huang

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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In the process of unified data processing, due to the lack of analysis of its spatio-temporal attributes, the fitting effect of the data corresponding to the same household is poor. Therefore, a unified method for spatio-temporal data of power grid based on Beidou grid code is proposed. After the sphere corresponding to the coverage area of the grid is divided proportionally, the mutual conversion relationship between the coordinates of the grid spatiotemporal data and the longitude and latitude of the grid positioning corner is established, and the ontology grid spatiotemporal data in the grid grid module is divided into different classes. In the data unification stage, based on the control points (spatiotemporal data of the target power grid), the nonlinear interpolation method is used to realize the unification of multivariate data, and the spatiotemporal data set of the target power grid is interpolated to obtain the coverage surface of the power grid data, so as to realize the data unification on the basis of elastic interpolation. In the test results, the unified data fitting statistical parameters and Ljung Box Q (18) parameters under the design method are at a high level, with good fitting effect.

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Section: Introductionmentioning

confidence: 99%