Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automat...Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.展开更多
As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions...As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions,such as two-phase flow,the measurement accuracy is greatly decreased due to various factors including improper signal processing methods.In this study,three digital signal processing methods—the quadrature demodulation(QD)method,Hilbert method,and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition.Based on the analysis,specific improvements are applied to each method to deal with the signals under two-phase flow condition.For simulation,sensor signals under single-and two-phase flow conditions are established using a random walk model.The phase difference tracking performances of these three methods are evaluated in the simulation.Based on the digital signal processor,a converter program is implemented on its evaluation board.The converter program is tested under single-and two-phase flow conditions.The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity.The QD algorithm has the best performance under the single-phase flow condition.Under the two-phase flow condition,the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160,250,and 420 kg/h flow points,whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.展开更多
文摘Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.
基金Project supported by the Scientific Research Project of Shanghai Municipal Bureau of Quality,China and the Technical Supervision Foundation of China(No.2018-05)。
文摘As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions,such as two-phase flow,the measurement accuracy is greatly decreased due to various factors including improper signal processing methods.In this study,three digital signal processing methods—the quadrature demodulation(QD)method,Hilbert method,and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition.Based on the analysis,specific improvements are applied to each method to deal with the signals under two-phase flow condition.For simulation,sensor signals under single-and two-phase flow conditions are established using a random walk model.The phase difference tracking performances of these three methods are evaluated in the simulation.Based on the digital signal processor,a converter program is implemented on its evaluation board.The converter program is tested under single-and two-phase flow conditions.The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity.The QD algorithm has the best performance under the single-phase flow condition.Under the two-phase flow condition,the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160,250,and 420 kg/h flow points,whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.
