Jingan Wang

Orcid: 0000-0002-0104-0755

According to our database1, Jingan Wang authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
A Computer-Vision-Based Nondestructive Hairiness Measurement System on Yarn Cone Transportation Line.
IEEE Trans. Instrum. Meas., 2024

The effect of student self-efficacy on learning outcomes in a business simulation mobile game: a quasi-experimental study.
Libr. Hi Tech, 2024

E-YQP: A self-adaptive end-to-end framework for quality prediction in yarn spinning manufacturing.
Adv. Eng. Informatics, 2024

2023
The effect of mobile business simulation games in entrepreneurship education: a quasi-experiment.
Libr. Hi Tech, 2023

2022
The self-increasing expansion method for knowledge space based on deep learning algorithm.
Int. J. Inf. Commun. Technol., 2022

2021
Characterization of Touch Sensory Attributes of Fabrics by a Simultaneous-Mechanical Measurement Method.
IEEE Trans. Instrum. Meas., 2021

A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern.
J. Intell. Manuf., 2021

Performance Evaluation of High-tech Innovation Enterprises Based on Grey Chaotic Neural Network.
Proceedings of the AIAM 2021: 3rd International Conference on Artificial Intelligence and Advanced Manufacture, Manchester, United Kingdom, October 23, 2021

2020
Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment.
IEEE Access, 2020

Automatic Assessment of Fabric Smoothness Appearance Based on a Compact Convolutional Neural Network With Label Smoothing.
IEEE Access, 2020

2019
Decoloration of Multi-Color Fabric Images for Fabric Appearance Smoothness Evaluation by Supervised Image-to-Image Translation.
IEEE Access, 2019

An Objective Fabric Smoothness Assessment Method Based on a Multi-Scale Spatial Masking Model.
IEEE Access, 2019

Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks.
IEEE Access, 2019


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