Lightweight VGG Rolling Sound Classification Model Based on Combinatorial Features-Reference-Cited by-同舟云学术

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Lightweight VGG Rolling Sound Classification Model Based on Combinatorial Features

Published:2024-05-25 Issue: Volume: Page:2473-2478
ISSN:
Container-title:2024 36th Chinese Control and Decision Conference (CCDC)
language:
Short-container-title:

Author:

Shi Rui¹,Zhang Fei¹,Li Yanjiao¹

Affiliation:

1. University of Science and Technology Beijing,National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing,Beijing,China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

Guangxi Key Research and Development Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587298/10587321/10588313.pdf?arnumber=10588313

Reference17 articles.

1. Promoting the valorization of blast furnace gas in the steel industry with the visual monitoring of combustion and artificial intelligence

2. Transfer learning-based approach using new convolutional neural network classifier for steel surface defects classification

3. Prediction model of sintering bed temperature based on lognormal distribution function: construction and application

4. Automated clash resolution for reinforcement steel design in precast concrete wall panels via generative adversarial network and reinforcement learning

5. Incorporating support vector machine to the classification of respiratory sounds by Convolutional Neural Network

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