Hash signature saving in distributed video coding

Chen, Xinhao; Zhu, Xingguo; Shen, Xiaolin; Yu, Lu

doi:10.1631/jzus.c1000008

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…The existing classifiers mainly include: artificial neural network (Haddad et al, 2018), Support Vector Machine (SVM) (Yong et al, 2015), decision tree (Huang et al, 2015;Long et al, 2018), expert system (Sai et al, 2015) and Bayesian classifier (Zhou et al, 2011), etc. For step 2): SVM has a high classification accuracy, but the amount of calculation in the process of parameter optimization is relatively large, and the real-time performance is not good.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Liu

Deng-ke

et al. 2021

Front. Energy Res.

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The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

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Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Liu

Deng-ke

et al. 2021

Front. Energy Res.

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“…The similar structures of HupA and HupB make it much more difficult to separate the two alkaloids simultaneously [9]. Traditional steps like liquid-liquid extraction, silica gel column chromatography, and crystallization methods were frequently used for separating HupA, yet the HupB was always discarded [2,10,11]. Moreover, all these traditional processes required large amount of organic solvents and multiple time-consuming processes, with poor yield and recovery of HupA and HupB.…”

Section: Introductionmentioning

confidence: 99%