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Complexity Comparison for Drinkers' and Normal People's EEG Using Wavelet Entropy
Jiufu Liu,Lei Gao,Zaihong Zhou,Haiyang Liu,Zhengqian Wang,Wenyuan Liu,Jianyong Zhou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.8
This paper investigates the influence of alcohol on brain complexity. Considering electro-encephalogram (EEG) has the nonlinear dynamics characteristic of time-varying and non-stationary, we introduce the wavelet entropy (WE) analysis. We denoise EEG signal by using wavelet decomposition, then calculate the wavelet entropy of the denoised signal and analyze the nonlinear complexity. In 64 conductive poles experiments and in different stimulus experiments for FP2 electrode's EEG, the drinkers' EEG wavelet entropy is greater than normal people's. The wavelet entropy of every conductive pole of drinkers’ or normal persons’ is inconformity.
Online Fault Diagnosis in Discrete Event Systems with Partially Observed Petri Nets
Jiufu Liu,Zaihong Zhou,Zhisheng Wang 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
This paper investigates the fault detection problem for Discrete Event Systems (DES) which can be modeled by Partially Observed Petri Nets (POPN). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, we propose an improved online fault diagnosis algorithm that integrates Generalized Mutual Exclusion Constraints (GMEC) and Integer Linear Programming (ILP).We assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. We use GMEC for elementary diagnosis of the system behavior,then the ILP problem of POPN is solved for further diagnosis. Finally, we modeled and analyzed an example of a real DES to test the new fault diagnoser. The proposed algorithm increased the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP was verified.