The present work uses the Thermosphere Ionosphere Electrodynamics General Circulation Model(TIEGCM),under geomagnetically disturbed conditions that are closely related to the southward interplanetary magnetic field(IM...The present work uses the Thermosphere Ionosphere Electrodynamics General Circulation Model(TIEGCM),under geomagnetically disturbed conditions that are closely related to the southward interplanetary magnetic field(IMF),to investigate how the nighttime poleward wind(30°–50°magnetic latitude and 19–22 magnetic local time)responds to subauroral polarization streams(SAPS)that commence at different universal times(UTs).The SAPS effects on the poleward winds show a remarkable UT variation,with weaker magnitudes at 00 and 12 UT than at 06 and 18 UT.The strongest poleward wind emerges when SAPS commence at 06 UT,and the weakest poleward wind develops when SAPS occur at 00 UT.A diagnostic analysis of model results shows that the pressure gradient is more prominent for the developing of the poleward wind at 00 and 12 UT.Meanwhile,the effect of ion drag is important in the modulation of the poleward wind velocity at 06 and 18 UT.This is caused by the misalignment of the geomagnetic and geographic coordinate systems,resulting in a large component of ion drag in the geographically northward(southward)direction due to channel orientation of the SAPS at 06 and 18 UT(00 and 12 UT).The Coriolis force effect induced by westward winds maximizes(minimizes)when SAPS commence at 12 UT(00 UT).The centrifugal force due to the accelerated westward winds shows similar UT variations as the Coriolis force,but with an opposite effect.展开更多
It has been primarily confirmed that before the occurrence of a middle or major earthquake, anomalies in the residuals of universal time and latitude measurements obtained by astrometric observations may appear. We in...It has been primarily confirmed that before the occurrence of a middle or major earthquake, anomalies in the residuals of universal time and latitude measurements obtained by astrometric observations may appear. We investigate the relation between the residual anomalies and the three key factors of an earthquake. To build a network of observational sites so as to obtain data of residuals of universal time and latitude from multi-instruments would be of certain significance for determining three key factors of an earthquake, especially for positioning epicenter. The data from multi-instruments would also be valuable for studies of the variation of the vertical. It is proposed to manufacture potable and high-accuracy astrometric instruments and to build observational network to obtain anomalies of universal time and latitude in the regions with high earthquake uprising possibilities.展开更多
Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and control...Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.展开更多
基金the National Science Foundation.The work is supported by the National Nature Science Foundation of China(No.s 41974182,41674153,41521063,41431073,41521062 and 42004135)the US NSF award AGS-1762141 and the US AFOSR MURI award FA9559-16-1-0364+1 种基金the NSF grant AGS-1452309The Spark Project at Wuhan University(2042020gf0024)also sponsors this work.
文摘The present work uses the Thermosphere Ionosphere Electrodynamics General Circulation Model(TIEGCM),under geomagnetically disturbed conditions that are closely related to the southward interplanetary magnetic field(IMF),to investigate how the nighttime poleward wind(30°–50°magnetic latitude and 19–22 magnetic local time)responds to subauroral polarization streams(SAPS)that commence at different universal times(UTs).The SAPS effects on the poleward winds show a remarkable UT variation,with weaker magnitudes at 00 and 12 UT than at 06 and 18 UT.The strongest poleward wind emerges when SAPS commence at 06 UT,and the weakest poleward wind develops when SAPS occur at 00 UT.A diagnostic analysis of model results shows that the pressure gradient is more prominent for the developing of the poleward wind at 00 and 12 UT.Meanwhile,the effect of ion drag is important in the modulation of the poleward wind velocity at 06 and 18 UT.This is caused by the misalignment of the geomagnetic and geographic coordinate systems,resulting in a large component of ion drag in the geographically northward(southward)direction due to channel orientation of the SAPS at 06 and 18 UT(00 and 12 UT).The Coriolis force effect induced by westward winds maximizes(minimizes)when SAPS commence at 12 UT(00 UT).The centrifugal force due to the accelerated westward winds shows similar UT variations as the Coriolis force,but with an opposite effect.
基金State Natural Science Foundation of China (19973011).
文摘It has been primarily confirmed that before the occurrence of a middle or major earthquake, anomalies in the residuals of universal time and latitude measurements obtained by astrometric observations may appear. We investigate the relation between the residual anomalies and the three key factors of an earthquake. To build a network of observational sites so as to obtain data of residuals of universal time and latitude from multi-instruments would be of certain significance for determining three key factors of an earthquake, especially for positioning epicenter. The data from multi-instruments would also be valuable for studies of the variation of the vertical. It is proposed to manufacture potable and high-accuracy astrometric instruments and to build observational network to obtain anomalies of universal time and latitude in the regions with high earthquake uprising possibilities.
文摘Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.
