A novel convolutional neural network based approach to predictions of process dynamic time delay sequences

2021 ◽

Author(s):

Shi-bo Pan ◽

Di-lin Pan ◽

Nan Pan ◽

Xiao Ye ◽

Miaohan Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Dynamic Time Warping ◽

Large Scale ◽

Line Graph ◽

High Accuracy ◽

Time Warping ◽

Large Numbers ◽

Dynamic Time

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

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A Dynamic Time Delay Neural Network for Ultrasonic Motor Identification and Control

Advances in Neural Networks - ISNN 2006 - Lecture Notes in Computer Science ◽

10.1007/11760023_159 ◽

2006 ◽

pp. 1084-1089

Author(s):

Yanchun Liang ◽

Jie Zhang ◽

Xu Xu ◽

Xiaowei Yang ◽

Zhifeng Hao

Keyword(s):

Neural Network ◽

Time Delay ◽

Ultrasonic Motor ◽

Dynamic Time ◽

And Control

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FPGA-implementation of dynamic time delay neural network for power amplifier behavioral modeling

Analog Integrated Circuits and Signal Processing ◽

10.1007/s10470-012-9857-0 ◽

2012 ◽

Vol 73 (3) ◽

pp. 819-828 ◽

Cited By ~ 4

Author(s):

Mohammed Bahoura ◽

Chan-Wang Park

Keyword(s):

Neural Network ◽

Time Delay ◽

Power Amplifier ◽

Fpga Implementation ◽

Behavioral Modeling ◽

Dynamic Time

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Surface Electrocardiogram Reconstruction From Intracardiac Electrograms Using a Dynamic Time Delay Artificial Neural Network

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2012.2225428 ◽

2013 ◽

Vol 60 (1) ◽

pp. 106-114 ◽

Cited By ~ 3

Author(s):

Fabienne Porée ◽

Amar Kachenoura ◽

Guy Carrault ◽

Renzo Dal Molin ◽

Philippe Mabo ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

Artificial Neural ◽

Dynamic Time ◽

Surface Electrocardiogram

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A Wavelet Transform-Assisted Convolutional Neural Network Multi-Model Framework for Monitoring Large-Scale Fluorochemical Engineering Processes

Processes ◽

10.3390/pr8111480 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1480

Author(s):

Xintong Li ◽

Kun Zhou ◽

Feng Xue ◽

Zhibing Chen ◽

Zhiqiang Ge ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Wavelet Transform ◽

Time Delay ◽

Convolutional Neural Network ◽

Dynamic Monitoring ◽

Model Framework ◽

Learning Methods ◽

Monitoring Method ◽

Useful Signal

The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis performance. The application on a refrigerant-producing process located in East China showed that not only regular faults but also hard to diagnose faults were successfully detected and diagnosed. More importantly, the unique online queue assembly updating strategy proposed remarkably reduced the inherent time delay of the deep-learning methods. Additionally, the application of it on the widely used Tennessee Eastman process benchmark strongly proved the superiority of it in fault detection and diagnosis over other deep-learning methods.

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