A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments

Hu, Hongping; Yang, Yu

doi:10.2991/caai-17.2017.124

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…CNN has excellent feature extraction ability in image data [8] , so the original vibration signal is generally processed into time-domain diagram, frequency-domain diagram and time-frequency diagram as the input of CNN.…”

Section: Cnn Different Data Processing Methodsmentioning

confidence: 99%

Impact of different data processing methods on model diagnostic performance

Fan

Zhao

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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During fault diagnosis of rolling bearings of rotating machinery in nuclear power plant, there are different data processing methods for original vibration signals, and different data processing methods have different diagnostic accuracy under the same diagnosis model. [Purpose] When the performance of the model is limited, in order to improve the diagnostic accuracy of the model, it is necessary to study the data processing method. [Methods] Therefore, the original vibration signal is processed into five ways: time-domain diagram(TD), time grayscale diagram(TGS), fast Fourier transform frequency-domain diagram(FFT), short-time Fourier transform time-frequency diagram(STFT) and continuous wavelet transform time-frequency diagram(CWT) for the initial feature extraction of the original vibration signal, and the five methods are compared and analyzed in the original data, data with added noise and data with less sample size. [Result] The experimental results show that the FFT data processing methods has a more obvious test accuracy under the noisy data and small sample size, and its accuracy is 1.9% and 6.7%(data under 50 sample size) higher than the suboptimal methods in both cases. [Conclusions] Therefore, in the case of noisy data and small sample size, the diagnostic performance of the model can be further improved by adopting the FFT data processing.

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Section: Cnn Different Data Processing Methodsmentioning

confidence: 99%

Impact of different data processing methods on model diagnostic performance

Fan

Zhao

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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“…Convolutional neural network [6] (CNN) is a very effective deep learning network model, which is widely used in face recognition technology. CNN is a deep feedforward neural network with features such as local connection and weight sharing [7] .…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Survey of Research on Face Recognition Methods Based on Depth Learning

Zhou,

Zhu

2024

J. Phys.: Conf. Ser.

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The field of face recognition has recently become a quite popular area of research, which is of great significance to the development of technology. It introduced the definition of face recognition technology and its development process, as well as the technical advantages and application scenarios of the technology. with the development of deep learning, face recognition technology based on deep learning is gradually realized. firstly, the limitations of traditional face recognition technology are pointed out. Then, several popular face recognition methods based on depth learning are analyzed. Finally, this paper introduces the application of deep learning technology in face recognition, summarizes a new deep learning model based on big data, and summarizes and prospects the development of face recognition technology in the future.

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“…GRU [8] is a variant of LSTM network, it inherits the advantages of LSTM network and has a simple structure, LSTM has three gates are forget gate, input gate and output gate, while GRU has only two gates are update gate and reset gate, for memory information transfer, LSTM is passed to the next unit through the output gate, GRU is directly transferred to the next unit without control. t moment GRU unit structure of the equation is shown below, r t , z t , h ~tare reset gate, update gate and memory transfer information respectively.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning in Product Manufacturing Record System

Wang

Zhao

2021

International Journal of Advanced Network, Monitoring and Controls

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Deep learning based data analysis techniques are investigated in the context of product production record systems, using CNN, STACK LSTM, GRU, INCEPTION, ConvLSTM and CasualLSTM techniques to design network models and to study the processing of temporal data. Three network models are proposed for the problem of predicting the pass rate of upcoming product inspection records, namely CNN-STACK LSTM, INCEPTION-GRU and INCEPTION-Casual LSTM, and the structure of each network model follows the learning of local-global features. The experimental results show that the INCEPTION-GRU network model works best among the three models. Based on the prediction results, it is possible to correct in advance the operation of the shop technicians who do not regulate the debugging of the product, so that the initial production efficiency of the product can be improved.

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A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments

Cited by 3 publications

References 15 publications

Impact of different data processing methods on model diagnostic performance

Impact of different data processing methods on model diagnostic performance

Survey of Research on Face Recognition Methods Based on Depth Learning

Deep Learning in Product Manufacturing Record System

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