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

Multivariate time series classification method based on deep learning

A multivariate time series and deep learning technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of insufficient data support, high variance, high volatility, etc., to achieve enhanced global timing characteristics, Significant clinical significance and practical application value, the effect of reducing the amount of parameters

Pending Publication Date: 2022-03-08
SUN YAT SEN UNIV
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is believed that the simple extraction and concatenation process may lose the feature relationship between variables
[0010] 2) High variance and high volatility
[0011] 3) Limited data
Therefore, the amount of data is not enough to support an overly complex model

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
  • Multivariate time series classification method based on deep learning
  • Multivariate time series classification method based on deep learning
  • Multivariate time series classification method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0052] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0053] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0054] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] For the diagnosis of anterior cruciate ligament injury, in order to fully mine the individual kinematics characteristics contained in the six-degree-of-freedom data set of the legs obtained from the Opti-Knee test, the present invention designs a multivariate time series classification method based on deep learning. This method can effectively capture the local and global correlation fe...

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 provides a multivariate time sequence classification method based on deep learning, and the method employs a BaseCNN to capture the interaction between variables, also employs an LSTM with a long-distance dependence capability to construct a sub-network, employs the long-term memory characteristic of the sub-network, strengthens the global time sequence characteristics of a model, and helps the model to better capture the global time sequence characteristics. Besides, a three-step training mode is ingeniously designed, the functions of Center Loss and Triplet Loss are effectively played, the characteristics of volatility and specificity of the data set are processed, and good characteristic embedding is provided for a final classification network. The artificial intelligent anterior cruciate ligament auxiliary diagnosis method greatly promotes the research of the existing artificial intelligent anterior cruciate ligament auxiliary diagnosis, and has great clinical significance and practical application value.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, and more specifically, to a multivariate time series classification method based on deep learning. Background technique [0002] As one of the most complex weight-bearing joints in the human body, the knee joint bears more load during walking and sports and plays an important role in shock absorption. At the same time, knee joint injuries are also very common. Among them, anterior cruciate ligament deficiency (ACL-D) is one of the most common sports injuries. With the development of the economy and the improvement of living standards, people's demand for sports has gradually increased, followed by an increase in sports injuries. The anterior cruciate ligament (ACL) not only limits the anterior translation of the tibia, but also controls the axial rotation of the knee joint and the movement of varicose veins, and has a high value in controlling the stability of the human knee joint....

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
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/045G06F18/214G06F18/241
Inventor 王梓曼印鉴刘威陈仲朱怀杰邱爽
Owner SUN YAT SEN 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