This paper proposes a novel low distortion high linearity CMOS bootstrapped switch, and the proposed switch can alleviate the nonlinear distortion of the on-resistance by eliminating first order signal-dependent varia...This paper proposes a novel low distortion high linearity CMOS bootstrapped switch, and the proposed switch can alleviate the nonlinear distortion of the on-resistance by eliminating first order signal-dependent variation of the overdrive voltage. Based on a 0.18 mm standard CMOS process, the simulation results show that at 100 MHz sampling clock frequency and 49 MHz input signal with 2Vpp, the proposed switch in differential mode has a Spurious-Free Dynamic Range(SFDR) of 90.1 dB.展开更多
The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a m...The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.61404043)Doctoral Program Foundation of Institutions of Higher Education of China(No.201201111-20008)Key Laboratory of Infrared Imaging Materials and Detectors,Shanghai Institute of Technical Physics,Chinese Academy of Sciences(No.IIMDKFJJ-13-06)
文摘This paper proposes a novel low distortion high linearity CMOS bootstrapped switch, and the proposed switch can alleviate the nonlinear distortion of the on-resistance by eliminating first order signal-dependent variation of the overdrive voltage. Based on a 0.18 mm standard CMOS process, the simulation results show that at 100 MHz sampling clock frequency and 49 MHz input signal with 2Vpp, the proposed switch in differential mode has a Spurious-Free Dynamic Range(SFDR) of 90.1 dB.
文摘The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.
