Tensor-based vehicle networking data loss multiple estimation new method

A technology of missing data and Internet of Vehicles, applied in the field of Internet of Vehicles, can solve the problems of data errors and reducing overall performance.

Active Publication Date: 2019-08-23
TIANJIN UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When there is serious data loss, this method can often stably show the applicability better than the first method, but there are also some disadvantages, such as data errors generated during repair will also reduce the overall performance

Method used

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  • Tensor-based vehicle networking data loss multiple estimation new method
  • Tensor-based vehicle networking data loss multiple estimation new method
  • Tensor-based vehicle networking data loss multiple estimation new method

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Experimental program
Comparison scheme
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Embodiment Construction

[0037] Step 1, model building:

[0038] The basic idea of ​​the tensor model:

[0039] figure 2 The third-order tensor cp decomposition model is given. The main idea of ​​CP decomposition is: a high-order tensor can be regarded as composed of several one-dimensional factor matrices, then the decomposed factor matrix can be used for calculation .

[0040] Create a tensor of order k where n l Represents the dimension along the l-th direction (l∈{1,2,...,k}). For the built tensor T, the indices of the elements are given by express. According to the basic idea of ​​CP decomposition, the construction tensor can be approximated by a low-rank structure, as follows:

[0041]

[0042] in is the l factorization factor matrix The j-th column vector of , the symbol ο represents the vector outer product, and r is the CP rank of the tensor T. If analyzed from the perspective of each element, formula (1) is equivalent to

[0043]

[0044] in is the mth factor matrix a...

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PUM

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Abstract

The invention provides a tensor-based vehicle network data loss multiple estimation method for the problem of data loss of the Internet of Vehicles, which is integrated Bayesian tensor decomposition (IBTD), and belongs to the field of Internet of Vehicles. According to the algorithm, in a data model construction stage, a random sampling principle is utilized, missing data is randomly extracted togenerate a data subset, and an optimized Bayesian tensor decomposition algorithm is used for interpolation. An integration idea is introduced, multiple interpolated error results are analyzed and sorted, space-time complexity is considered, and an optimal result is obtained through preferred average. the performance of the proposed model is carried out by an average absolute percentage error (MAPE) and a root-mean-square error (RMSE). Experimental results show that the new method provided by the invention can effectively carry out interpolation on traffic data sets with different missing amounts, and can obtain a very good interpolation result.

Description

technical field [0001] The invention belongs to the field of the Internet of Vehicles, and in particular relates to a tensor-based new method for estimating missing data of the Internet of Vehicles. Background technique [0002] The Internet of Vehicles actually aims to build an intelligent transportation network. With the rapid development of modern sensing technology, communication technology, computer technology and information technology, the intelligent transport system (Intelligent Transport System, referred to as ITS) is gradually promoted, and the traffic information collection system is an important part of ITS. , Real-time traffic information can grasp the urban road traffic conditions and changes, and provide scientific basis for urban traffic planning and decision-making. [0003] The data required for the application of the Internet of Vehicles should have high spatial and temporal resolutions in order to achieve the purposes of modeling, traffic management, fo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16G06F17/18H04L29/08
CPCG06F17/16G06F17/18H04L67/12
Inventor 张德干张婷吴昊高瑾馨颜浩然
Owner TIANJIN UNIVERSITY OF TECHNOLOGY
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