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- 저자 : Yilin Zhen (검색결과 2 건)
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Catalytic decomposition of N2O over RhOx supported on metal phosphates
YiLin,Zhen Ma,Tao Meng 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.28 No.-
RhOx/M–P–O (M = Mg, Al, Ca, Fe, Co, Zn, La) catalysts were tested in N2O decomposition. RhOx/Ca–P–Oshowed the highest activity, achieving complete N2O conversion at 300 8C. RhOx/La–P–O is the secondmost active catalyst, achieving almost complete conversion at 375 8C. RhOx/Mg–P–O, RhOx/Co–P–O, andRhOx/Al–P–O showed lower activities, whereas RhOx/Fe–P–O and RhOx/Zn–P–O were completelyinactive. The high activity of RhOx/Ca–P–O is ascribed to the presence of very small RhOx particles, morebasic sites, and the easiness of desorbing O2 at low temperatures. The inhibiting effects of co-fed O2 and/or H2O on the catalytic activity of RhOx/Ca–P–O are reversible
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Classification of Motor Imagery Tasks for Electrocorticogram Based Brain-Computer Interface
Fangzhou Xu,Weidong Zhou,Yilin Zhen,Qi Yuan 대한의용생체공학회 2014 Biomedical Engineering Letters (BMEL) Vol.4 No.2
Purpose In the present study, we propose a novel schemefor motor imagery (MI) classification of multichannelelectrocorticogram (ECoG) recordings from patients withmedically intractable focal epilepsy. Methods This scheme proposes a combination of the twofeatures which includes autoregressive (AR) model coefficientsand local binary pattern (LBP) operators. It can providespatial resolution and angular space information. Then thegradient boosting (GB) in conjunction with ordinary leastsquares (OLS) algorithm is employed as the classifier toimprove the performance of MI classification for ECoGbased Brain Computer Interface (BCI) system. Results Experimental results on the BCI Competition IIIdata set I indicate that the novel method has excellentperformance and yields a cross-validation accuracy of 88.8%and accuracy of 93%, respectively. Conclusions From the experimental results and comparativestudies, we can infer that the scheme may serve as a good MIclassification tool for a better tradeoff between the classificationaccuracy and computational complexity.
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