Convolutional neural network based combustion mode classification for condition monitoring in the supersonic combustor

Zhu, Xiaobin; Cai, Zun; Wu, Jianjun; Cheng, Yuqiang; Huang, Qiang

doi:10.1016/j.actaastro.2019.03.072

Cited by 45 publications

(9 citation statements)

References 43 publications

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“…Song et al [ 17 ] find that word vector (WV) is trained by a word embedding model, which can represent the context and semantic information of words. Zhu et al [ 18 ] proposed that the part-of-speech vector (POSV) was trained after combining words and parts of speech, and different vectors were used to represent different parts of speech of the same word, thus avoiding ambiguity of some words. Sentiment word vector SWV is trained with sentiment words and text sentiment tags.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Zhang

2021

Computational Intelligence and Neuroscience

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Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers’ attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation of sample data. Firstly, this paper analyzes the model structure of a typical convolutional neural network model to increase the network depth and width in order to improve its performance, analyzes the network structure that further improves the model performance by using the attention mechanism, and then summarizes and analyzes the current special model structure. In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of text features and language knowledge are proposed. The text features and language knowledge are integrated into the language processing model, and the accuracy of the text language processing model is improved by parameter optimization. Experimental results on data sets show that the accuracy of the proposed model reaches 93.0%, which is better than the reference model in the literature.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Zhang

2021

Computational Intelligence and Neuroscience

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“…Adam is an extension of stochastic gradient descent (SGD) algorithm. The method is computationally efficient and straightforward to implement and has been proved to have good performance in processing large amounts of data and parameters [35,36]. The training of the network was conducted using the TensorFlow (www.tensorflow.org) library.…”

Section: Deep Learning Architecturesmentioning

confidence: 99%

Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion

Luo

et al. 2020

International Journal of Hydrogen Energy

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The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related research, design and optimization. In this study, we propose a framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the deep learning solver, named CFDNN. CFDNN is then trained on simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from the CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration are achieved by using the CFDNN solver compared to a conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets.

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“…In recent years, various classification algorithms such as artificial neural networks (ANNs) and support vector machine (SVM), have widely been applied to predict the combustion states through flame imaging. The ANN usually coupled a CNN fully connected layer [23] and the network can predict the combustion states based on the labeled images by fine-tuning. However, the ANN suffers weaknesses such as difficult to determine its architecture and unreliable to deal with small sample cases [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of combustion state through a semi-supervised learning model and flame imaging

Han

Zhang

et al. 2021

Fuel

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Convolutional neural network based combustion mode classification for condition monitoring in the supersonic combustor

Cited by 45 publications

References 43 publications

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

[Retracted] Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion

Prediction of combustion state through a semi-supervised learning model and flame imaging

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