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RISS 인기검색어
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- 저자 : Chang Faliang (검색결과 6 건)
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Crowd escape event detection based on Direction-Collectiveness Model
( Mengdi Wang ),( Faliang Chang ),( Youmei Zhang ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.9
Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the ‘escape event’ occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.
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Qinyu Jiang,Faliang Chang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.4
Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.
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Jiang Qinyu,Chang Faliang,Liu Chunsheng 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.4
Rotating machines are one of the most common equipment in modern industry, the health condition of the equipment is closely linked to safety of workers and production eff ectiveness. Thus accurate and robust fault diagnostic approaches are vital to safety production. In practice, diagnostic accuracy is seriously aff ected by noises, especially in low signal-to-noise (SNR) ratio conditions, and the quality of fault features is positively link to the diagnosing accuracy. In consideration of distinguishable feature expression can improve diagnosing result and robust to wider range of experimental conditions, this paper presents a novel spectrogram based local fl uctuation feature (SLFF) for low SNR conditions. Firstly, signals are transformed into spectrograms. Then a feature extracting window bank is established on spectrograms for SLFF. At last, a support vector machine (SVM) is applied as a fault classifi er for evaluating the proposed feature. The proposed SLFF represents the basic spectral shape and variation which leads to robust and well distinguishable feature expression, the feature reveals the diff erences of spectral local variation trends between normal and fault types that can improve the discrimination under the infl uence of strong noises. The eff ectiveness of the proposed method has been proved experimentally in this paper.
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Qinyu Jiang,Faliang Chang 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.9
Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APOAIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.
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Topology Learning of Non-overlapping Multi-camera Network
Xiaolin Li,Wenhui Dong,Faliang Chang,Peishu Qu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.11
We focus on the issue of learning the topology of the non-overlapping multi-camera network, which includes recovering the nodes (entry and exit zones), transition time distribution and links. Firstly, the nodes associated with each camera view are identified using clustering method. Then, transition time distribution is modeled as a Gaussian distribution and is computed by accumulated cross correlation and Gaussian fitting. Finally, the mutual information is used to refine the possible links and the topology is recovered. Experimental results on simulated data and real scene demonstrate the effectiveness of the proposed method.
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