Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF

A TE-ANN-AWF, error compensation technology, applied in the field of telemetry error compensation based on TE-ANN-AWF mobile pollution source, can solve the problem of remote sensing detection method being easily interfered by the external environment, etc., to improve the applicability and anti-interference ability , to ensure the effect of long-term stability

Active Publication Date: 2018-09-28
HANGZHOU DIANZI UNIV
View PDF9 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the problem that the remote sensing detection method of mobile pollution sources is easily disturbed by the external environment. In this paper, a new error compensation model TE-ANN- AWF

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF
  • Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF
  • Mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] In order to make the technological innovation realized by the present invention easy to understand, the following is combined figure 1 To further describe the implementation of the present invention in detail, the specific steps are as follows:

[0043] Step 1: Obtain measurement samples under different interference effects through environmental simulation experiments, and then preprocess and normalize the measurement samples. Based on the experimental measurement samples, the interference correlation analysis is carried out through TE transfer entropy, so as to determine the source of measurement error and measure the degree of imbalance between multiple interferences. And use the directionality of TE transfer entropy to derive the quantitative standard and judgment method of non-significant causality.

[0044] Suppose Xn and Yn are two environmental disturbance change sequences and remote sensing measurement observation sequences with discrete states of xn and yn at time n...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a mobile pollution source remote-measurement error compensation method based on TE-ANN-AWF. The TE transfer entropy is used to perform a causal correlation analysis on interference and measure results, an error source is determined and a multi-interference imbalance degree is measured, and the directivity of the TE transfer entropy is used for leading out a quantification standard and a determination method having a non-substantial causal correlation. A virtual observation method is provided for realizing multiple deconstruction of a unit observation sequence, an ANN error prediction model is used to realize compensation of a single interference channel virtual observation sequence, and a multiple adaptive weight fusion method is employed for fusion reconstruction on a multiple virtual observation sequence. By aiming at a weight convergence problem in a fusion algorithm, an index forgetting method is introduced in a model to combine the good weight pre-estimatedcapability of TE and the weight adaptive adjustment of AWF, and the dynamic performance of a error compensation process is improved.

Description

Technical field [0001] The present invention designs a TE-ANN-AWF-based mobile pollution source remote sensing detection error compensation method, which belongs to the technical field of error compensation for mobile pollution source remote sensing detection instruments, and aims to correct the measurement results of remote sensing detection instruments under external environmental interference According to the entropy estimation theory, adaptive fusion and neural network related theories, compensation and estimation are carried out to solve the problem of remote sensing detection method being vulnerable to external environmental interference. Background technique [0002] Mobile pollution sources refer to sources that do not emit air pollutants through stationary equipment, such as motor vehicles, mobile construction machinery, ships and airplanes that emit exhaust gas during movement. Mobile source pollution, especially heavy-duty diesel trucks, commercial gasoline vehicles, o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/17G06F17/18G06N3/08
CPCG01N21/17G01N2021/1795G06F17/18G06N3/08
Inventor 蒋鹏华通席旭刚佘青山甘海涛
Owner HANGZHOU DIANZI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com