Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging(GPI)diagnostic has been developed and operated on EAST since 2012,and the time-delay estimation(TDE)method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data.However,with the TDE method it is difficult to analyze the data with fast transient events,such as edge-localized mode(ELM).Consequently,a method called the spatial displacement estimation(SDE)algorithm is developed to estimate the turbulence velocity with high temporal resolution.Based on the SDE algorithm,we make some improvements,including an adaptive median filter and super-resolution technology.After the development of the algorithm,a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm,and the calculated speed agrees well with preset speed.This SDE algorithm is applied to the EAST GPI data analysis,and the derived propagation velocity of turbulence is consistent with that from the TDE method,but with much higher temporal resolution.

Spatial Decay Estimates for the Solutions to Stokes Equations in Four Kinds of Semi-Infinite Cylinders

This paper investigates the spatial behavior of the solutions of the Stokes equations in a semi-infinite cylinder.We consider four kinds of semi-infinite cylinders with boundary conditions of Dirichlet type.For each type of cylinder we obtain the spatial decay estimates for the solutions.To make the attenuation meaningful,we derive the explicit bound for the total energy in terms of the initial boundary data.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

Kriging Geostatistical Methods for Travel Mode Choice: A Spatial Data Analysis to Travel Demand Forecasting

This paper aims to compare the results of two techniques of Kriging (Ordinary Kriging and Indicator Kriging) that are applied to estimate the Private Motorized (PM) travel mode use (car or motorcycle) in several geographical coordinates of non-sampled values of the concerning variable. The data used was from the Origin/Destination and Public Transportation Opinion Survey, carried out in 2007/2008 at S?o Carlos (SP, Brazil). The techniques were applied in the region with 110 sample points (households). Initially, Decision Tree was applied to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be used in Ordinary Kriging. For application of Indicator Kriging it was used the variable “main travel mode” in a discrete manner, where “1” represented the use of PM travel mode and “0” characterized others travel modes. The results obtained by the two spatial estimation techniques were similar (Kriging maps and cross-validation procedure). However, the Indicator Kriging (KI) obtained the highest number of hit rates. In addition, with the KI it was possible to use the variable in its original form, avoiding error propagation. Finally, it was concluded that spatial statistics was thriving in travel demand forecasting issues, giving rise, for the both Kriging methods, to a travel mode choice surface on a confirmatory way.

Co-channel interference rejection for MIMO-OFDM systems

A method based on the maximum a posteriori probability （MAP） criterion is proposed to estimate the channel frequency response （CFR） matrix and interference- plus-noise spatial covariance matrix （SCM） for multiple input and multiple output orthogonal frequency division multiplexing （MIMO-OFDM） systems. An iterative solution is proposed to solve the MAP-based problem and an interference rejection combining （IRC） receiver is derived to suppress co-channel interference （CCI） based on the estimated CFR and SCM. Furthermore, considering the property of SCM, i. e., Hermitian and semi-definite, two schemes are proposed to improve the accuracy of SCM estimation. The first scheme is proposed to parameterize the SCM via a sum of a series of matrices in the time domain. The second scheme measures the SCM on each subcarrier as a low-rank model while the model order can be chosen through the penalized-likelihood approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Towards combining multiple geophysical datasets to determine earthquake source parameters in China

1.Difficulties of conventional seismic studies on earthquake source parameters Earthquake source parameters,including magnitude,location,focal mechanism,rupture process are key factors for understanding seismogenic environment,mitigating seismic hazards,estimating earthquake triggering,and tectonic analysis.Traditionally,source parameters are determined by seismological methods.For example,Fang L H et al.（2014）relocated the 2012 Ms6.6 Xinjiang Xinyuan earthquake sequence using local seismograms based on the double difference method,

Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed,northwest China

Precipitation patterns are vital to water resource management and hydrological research,especially in the upper reaches of inland rivers in arid and semiarid areas.However,estimating spatiotemporal precipitation patterns at a basin scale is challenging due to limited observations.In this study,spatiotemporal patterns of precipitation amount,frequency,duration,and intensity at different time scales from 2014 to 2019 are estimated using the Bayesian maximum entropy method in the Tianlaochi catchment of the Heihe River watershed,northwest China.The study's results show that the annual average precipitation amount was 535.9 mm from 2014 to 2019,with precipitation amount between May and September accounting for 85.9%of the annual precipitation amount.For daily precipitation,the average frequency rate of light precipitation is highest at 59.55%,however,the average contribution rate of moderate precipitation is highest at 50.33%.The spatial distribution of precipitation is characterized by high-value areas concentrated in the central valley and low-value areas located at the catchment's outlet.The most important driving factors of precipitation patterns are elevation,relative humidity,and wind direction.These outcomes can be used to establish accurate hydrological models in the catchment and provide support for water resource management in the Heihe River watershed.