An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model
Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the principal component analysis (PCA), and the hidd...
Main Authors: | Tian Qiu, Minjian Liu, Guiping Zhou, Li Wang, Kai Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-07-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/12/13/2585 |
Similar Items
-
A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation
by: Kun Zhao, et al.
Published: (2021-09-01) -
Unsupervised Phonocardiogram Analysis With Distribution Density Based Variational Auto-Encoders
by: Shengchen Li, et al.
Published: (2021-08-01) -
Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach
by: Michael W. Hopwood, et al.
Published: (2022-07-01) -
Unsupervised statistical image segmentation using bi-dimensional hidden Markov chains model with application to mammography images
by: Abdelali Joumad, et al.
Published: (2023-10-01) -
Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition
by: Najoua Rahal, et al.
Published: (2021-01-01)