The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this ...
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ISBN:
(数字)9781728126296
ISBN:
(纸本)9781728126302
The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this paper, a dynamic network model construction method is proposed. The basic idea of the proposed dynamic emotion analysis method is to construct different network modes based on the channel correlation mode and band power distribution method, and use the sliding window method to estimate the dynamic correlation mode and dynamic power distribution changes, Then the multivariate features of network-level brain dynamics are extracted and classified. We conducted experiments on the SEED database to study network-level dynamic transformations in the process of positive, neutral, and negative emotions. The results verify the feasibility of the emotional assessment method based on dynamic functional connection, and provide a new way to establishing brain dynamic model under different emotional states. The results show that emotion analysis based on dynamic networks is more accurate than static networks.
With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis d...
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Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-...
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River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation ...
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Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking,combined with the CN(Color Name) feature,we proposed a new feature(denotes CNH,Color Name Histogram) based on col...
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Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking,combined with the CN(Color Name) feature,we proposed a new feature(denotes CNH,Color Name Histogram) based on color ***,the method projected the original RGB image to CN space to obtain robust 11 feature ***,we counted the each pixel numbers of feature ***,normalizing the amount of pixels in each *** addition,we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient).In order to prove validity of the proposed algorithm,we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled *** contrast with the reference algorithms,the success of our algorithm increases by 1.5% and the precision increases by 1.7%.The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation,but also enhances the robustness to illumination change.
Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generali...
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To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm, this paper proposed a GDT-NMS (Generalized Intersection over Union Dual Threshold NMS, ...
To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm, this paper proposed a GDT-NMS (Generalized Intersection over Union Dual Threshold NMS, GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union). Using the GIoU indicator computing the similarity between objects, can better describe the relative position and overlap between the objects. And we proposed the dual-threshold NMS algorithm, which can balance the relationship between the object missed detection problem and the object false detection problem, reduce 'false positive example' problem. By nonlinearly processing the weight function, the object is better distinguished. The algorithm uses Faster R-CNN as the detector. The experimental results show that the improved algorithm has outstanding performance.
With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an indust...
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ISBN:
(数字)9781728126296
ISBN:
(纸本)9781728126302
With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods.
This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strat...
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Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on ...
Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.
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