Image-Based Process Monitoring Using Deep Belief Networks

Lyu, Yuting; Chen, Junghui; Song, Zhihuan

doi:10.1016/j.ifacol.2018.09.285

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…One of the applications in which these networks perform particularly well is the detection of anomalies in the time series of different signals. 32–36 In the study, we utilised three types of DRNNs. They are as follows: LSTM recurrent neural network, one-dimensional (1D) convolutional neural network LSTM (CNN-LSTM) and 1D convolutional LSTM (ConvLSTM).…”

Section: Methodsmentioning

confidence: 99%

“…For example, Abdurakipov et al 11 studied the possibility of monitoring combustion regimes using flame images of a gas burner. Lyu et al 12 applied a deep belief network for the detection of abnormal conditions in the experimental combustion system. Wang et al 13 proposed a method of identifying the burning state (including oil fire, powder burning and normal burning) and measuring the heat release rate.…”

Section: Introductionmentioning

confidence: 99%

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Combustion process monitoring based on flame intensity time series

Omiotek

Smolarz

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The pulverised coal and its mixture with biomass are one of the most popular fuels in industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the other hand, maximum energy production efficiency, it is necessary to monitor the combustion process of these fuels. One way to do this is to monitor the flame intensity. This is an optical, non-invasive solution, and information on the status of the process is obtained with minimal delay. The article proposes a method for identifying undesired combustion states for which the excess air coefficient is greater or smaller than the value ensuring total combustion. Three deep recurrent neural network architectures for classifying the flame intensity time series were explored. The best results were obtained using the convolutional long short-term memory model, which offered the accuracy of 86.5%–99.8%, depending on the current thermal power. The prediction time of a single data sequence was about 0.6 ms. High accuracy and low time consumption of the proposed method create an opportunity for its use in industrial combustion systems of pulverised coal and its mixture with biomass.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combustion process monitoring based on flame intensity time series

Omiotek

Smolarz

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…These parameters were automatically calculated using other supporting parameters such as true negative, true positive, false negative, as well as false positive, which were used to compute the metrics automatically using the hybrid method. The predicted results were compared with other methods such as the Scratch Model, AlexNet and ResNet50 (Lyu et al, 2018). Compared to the previous models, the proposed model produced better accuracy (Acc), sensitivity (Sen) and specificity (Spc).…”

Section: Datasets and Performance Parametersmentioning

confidence: 99%

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

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“…It has been widely adopted for multiple tasks such as pattern recognition, handwriting recognition, speech recognition and many other tasks [113], [114]. It is also used for classification task [115], soft sensor [116], and imagebased monitoring [117]. DBN is an unsupervised method which works on unlabeled data.…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Image-Based Process Monitoring Using Deep Belief Networks

Cited by 5 publications

References 18 publications

Combustion process monitoring based on flame intensity time series

Combustion process monitoring based on flame intensity time series

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

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