Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of su...Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.展开更多
Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Veg...Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.展开更多
Background: The Nanostim {trade mark, serif} Leadless Cardiac Pacemaker (LCP) has been shown to be safe and effective in human clinical trials. Since there is little information on the effect of implant location on LC...Background: The Nanostim {trade mark, serif} Leadless Cardiac Pacemaker (LCP) has been shown to be safe and effective in human clinical trials. Since there is little information on the effect of implant location on LCP performance, the aim of this study was to determine whether anatomic position affects the long-term pacing performance of the LCP. Methods: Patients who enrolled in the Leadless II IDE Clinical Trial and had finished 6 months follow up (n = 479) were selected for the study. The implanting investigators determined the LCP final position under fluoroscope, which was categorized into three groups: RV apex (RVA, n = 174), RV apical septum (RVAS, n = 101), and RV septum (RVS, n = 204) (Figure 1). Data on capture threshold (at a 0.4 ms pulse width), R-wave amplitude and impedance were analyzed at implant, hospital discharge and 2 weeks, 6 weeks, 3 months and 6 months post-implant. Results: At implant, the mean capture thresholds in the RVA, RVAS and RVS were 0.77 ± 0.45, 0.81 ± 0.61 and 0.78 ± 0.59 volts, respectively. R-wave amplitudes were 8.0 ± 3.0 mV, 7.7 ± 2.9 mV and 7.6 ± 2.9 mV, respectively. Impedance values were 727 ± 311, 765 ± 333, and 677 ± 227 respectively. There were no differences among the 3 implant locations in capture threshold or R-wave amplitudes at 6 months (P > 0.06);however, all 3 performance parameters significantly improved over time (P < 0.001). Conclusions: The LCP implant location does not affect capture thresholds or R-wave amplitudes at 6 months, and there is little effect on impedance. Although implant location does not appear to be a predictor of electrical performance, additional long-term data will help guide optimal implant location.展开更多
基金supported by the National Key R&D Pro gram (2017YFB0902901)National Nature Science Founda tion of China (51725702, 51627811, 51707064)。
文摘Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.
基金National Natural Science Foundation of China(No.41171285)Research and Development Special Fund for Public Welfare Industry(Meteorology)of China(No.GYHY201106014)
文摘Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.
文摘Background: The Nanostim {trade mark, serif} Leadless Cardiac Pacemaker (LCP) has been shown to be safe and effective in human clinical trials. Since there is little information on the effect of implant location on LCP performance, the aim of this study was to determine whether anatomic position affects the long-term pacing performance of the LCP. Methods: Patients who enrolled in the Leadless II IDE Clinical Trial and had finished 6 months follow up (n = 479) were selected for the study. The implanting investigators determined the LCP final position under fluoroscope, which was categorized into three groups: RV apex (RVA, n = 174), RV apical septum (RVAS, n = 101), and RV septum (RVS, n = 204) (Figure 1). Data on capture threshold (at a 0.4 ms pulse width), R-wave amplitude and impedance were analyzed at implant, hospital discharge and 2 weeks, 6 weeks, 3 months and 6 months post-implant. Results: At implant, the mean capture thresholds in the RVA, RVAS and RVS were 0.77 ± 0.45, 0.81 ± 0.61 and 0.78 ± 0.59 volts, respectively. R-wave amplitudes were 8.0 ± 3.0 mV, 7.7 ± 2.9 mV and 7.6 ± 2.9 mV, respectively. Impedance values were 727 ± 311, 765 ± 333, and 677 ± 227 respectively. There were no differences among the 3 implant locations in capture threshold or R-wave amplitudes at 6 months (P > 0.06);however, all 3 performance parameters significantly improved over time (P < 0.001). Conclusions: The LCP implant location does not affect capture thresholds or R-wave amplitudes at 6 months, and there is little effect on impedance. Although implant location does not appear to be a predictor of electrical performance, additional long-term data will help guide optimal implant location.
