Study of Cascading Failure in Multisubnet Composite Complex Networks

Sun, Gengxin; Chen, Chih-Cheng; Bin, Sheng

doi:10.3390/sym13030523

Cited by 44 publications

(35 citation statements)

References 24 publications

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“…The moving object detector based on Wronskian function [16] is a vector image model. It is often adopted to detect the pixel variation between the same position of two adjacent frames.…”

Section: Improved Vibe Algorithmmentioning

confidence: 99%

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Yuan-guo¹,

Wu²

2021

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The effect of motion posture recognition hinges on the accurate description of motion postures with effective feature information. This study introduces Wronskian function to improve the denoising ability of visual background extractor (ViBe) algorithm, and thus acquires relatively clear motion targets. Then, a multi-feature fusion motion posture feature model was developed based on genetic algorithm (GA). Specifically, GA was called to optimize and fuse the extracted feature information, while a fitness function was constructed based on the mean variance ratio, and used to select the feature information with high inter-class discriminability. Taking support vector machine (SVM) as the classifier, a multi-class classifier was designed by one-to-one method for the classification and recognition of motion postures. Through experiments, our model was proved highly accurate in motion posture recognition.

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“…The moving object detector based on Wronskian function [16] is a vector image model. It is often adopted to detect the pixel variation between the same position of two adjacent frames.…”

Section: Improved Vibe Algorithmmentioning

confidence: 99%

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Yuan-guo¹,

Wu²

2021

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“…In static images, the countenance features can be extracted by Gabor filter, local binary pattern (LBP), scaleinvariant feature transform (SIFT), and principal component analysis (PCA) [19][20][21]. In dynamic images, these features can be extracted by model approach, geometric approach, and optical flow approach [22][23][24]. With histogram of oriented gradients (HOG), Wang et al [25] described the local contour of human faces, and constructed a bottom-up hierarchical DT, which can correctly classify the emotions of any image not included in the training set.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Classroom Learning Behaviors Based on Internet of Things and Image Processing

Yu¹,

Xiao-jing²

2021

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The quick and accurate identification of classroom emotions helps teachers perceive the learning state of their students. This paper designs a bimodal identification system for classroom emotions based on electroencephalogram (EEG) signals and countenances. The system relies on the Internet of things (IoT) technology to collect EEG signals, and extracts the signal features with fractal dimension and multiscale entropy algorithm. After that, the support vector machine (SVM) was adopted to classify the classroom emotions. Then, the features of countenances were extracted by local binary pattern (LBP). Experimental results show that our system accurately identified 85.7% of classroom emotions. Compared with the traditional countenance-based emotion identification method, the bimodal approach could extract rich information on classroom emotions, and achieve a good effect on emotion identification.

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“…The Poisson equation can be solved by many methods, such as the Green function, fast Fourier transform, central difference method, and multigrid method [21]. In this paper, the second Green function method, which is based on Dirichlet boundary condition, is adopted to solve the Poisson equation of formula (13).…”

Section: Solving Poisson Equation With Green Functionmentioning

confidence: 99%

An Image Sharpness Enhancement Algorithm Based on Green Function

Huang¹

2021

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The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.

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Study of Cascading Failure in Multisubnet Composite Complex Networks

Cited by 44 publications

References 24 publications

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Analysis of Classroom Learning Behaviors Based on Internet of Things and Image Processing

An Image Sharpness Enhancement Algorithm Based on Green Function

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