Crowd Behaviors Analysis and Abnormal Detection in Structured Scene

2017 ◽  
Author(s):  
Jiaqian Qi ◽  
Weibin Liu ◽  
Weiwei Xing
Keyword(s):  
Abnormal Detection ◽  
Crowd Behaviors

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

High-density crowd behaviors segmentation based on dynamical systems

2016 ◽  
Vol 23 (5) ◽  
pp. 599-606 ◽  
Author(s):  
Dongping Zhang ◽  
Jiao Xu ◽  
Min Sun ◽  
Zhiyu Xiang
Keyword(s):  
Dynamical Systems ◽  
High Density ◽  
Crowd Behaviors

Exterior Ballistic Data Processing by SVD and Wavelet Transform

2012 ◽  
Vol 562-564 ◽  
pp. 1394-1397
Author(s):  
Yu Hua Dong ◽  
Hai Chun Ning
Keyword(s):  
Wavelet Transform ◽  
Phase Space ◽  
Data Processing ◽  
Wavelet Decomposition ◽  
Hankel Matrix ◽  
Singular Value ◽  
Experimental Data Processing ◽  
Abnormal Detection ◽  
Reconstructed Phase Space ◽  
Trajectory Measurement

This paper proposes a method of wavelet transform combined with SVD (Singular Value Extracting), and the abnormal data elimination in its trajectory measurement is studied. After the wavelet decomposition of the observed data, combining the approximate component and the detail component, the phase space is reconstructed. The increment criterion of singular entropy is used for the input observed matrix of SVD, and the singular value is selected. Then the original signal is reconstructed by SVD inverse transform. This method overcomes the distortion problem of data end in phase space reconstruction by Hankel matrix. The reconstructed phase space by components of wavelet decomposition is orthogonal. So it further improves the accuracy of noise reduction and abnormal detection by SVD. The results of experimental data processing show the effectiveness of this method proposed in the paper.

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