Fault area estimation using traveling wave for wide area protection

As the increasing number of Phasor Measurement Units(PMUs) are deployed, wide area protection in power systems has been gaining interest. In particular, fault detection, fault classification and fault area estimation are essential to reduce the damage of faults, and even prevent catastrophic cascades of failures. In this paper, we present a scheme for fault area estimation using PMUs and traveling wave theory. The purpose of this paper is to formulate a scheme for fault area estimation by calculating the approximate fault location based on traveling wave theory.This research has targeted at reliable operation of wide transmission system through the estimation of fault area.To verify the suggested scheme, the various simulations are performed in practical Korean power transmission system.The simulation results show that the proposed scheme has a good performance with high accuracy for estimating fault area.

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information,which is essential to a wide range of applications over large geographical regions.Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products:MODIS land cover(MODISLC)at 500-m resolution in 2010,GlobCover at 300-m resolution in 2009,FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data.Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products,which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation(MDev)and RMSE,but were less effective on GlobCover product.We found that,in the USA,the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage,while FROM-GLC-agg gave the least deviation from the survey at the state level.Correlation between land cover map estimates and survey estimates is significant,but stronger at the state level than at the county level.In regions where most mismatches happen at the county level,MODIS tends to underestimate,whereas MERIS and Landsat images incline to overestimate.Those uncertainties should be taken into consideration in relevant applications.Excluding interannual and seasonal effects,R 2 of the FROM-GLC regression model increased from 0.1 to 0.4,and the slope is much closer to one.Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.

Combining multiple imperfect data sources for small area estimation:a Bayesian model of provincial fertility rates in Cambodia

Demographic estimation becomes a problem of small area estimation when detaileddisaggregation leads to small cell counts.The usual difficulties of small area estimation are compounded when the available data sources contain measurement errors.We present a Bayesianapproach to the problem of small area estimation with imperfect data sources.The overall modelcontains separate submodels for underlying demographic processes and for measurement processes.All unknown quantities in the model,including coverage ratios and demographic rates,are estimated jointly via Markov chain Monte Carlo methods.The approach is illustrated usingthe example of provincial fertility rates in Cambodia.

Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling

To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.

Small area estimation with subgroup analysis

In this article,a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation(SAE).Instead of assuming common regression coefficients for all small domains in the traditional model,the new estimator is based on a subgroup regression model which allows different regression coefficients in different groups.The alternating direction method of multipliers(ADMM)algorithm is used to find subgroups with different regression coefficients.We also consider pairwise spatial weights for spatial areal data.In the simulation study,we compare the performances of the new estimator with the traditional small area estimator.We also apply the new estimator to urban area estimation using data from the National Resources Inventory survey in Iowa.

Mitogenomic phylogeny,biogeography,and cryptic divergence of the genus Silurus(Siluriformes:Siluridae)

The genus Silurus,an important group of catfish,exhibits heterogeneous distribution in Eurasian freshwater systems.This group includes economically important and endangered species,thereby attracting considerable scientific interest.Despite this interest,the lack of a comprehensive phylogenetic framework impedes our understanding of the mechanisms underlying the extensive diversity found within this genus.Herein,we analyzed 89 newly sequenced and 20 previously published mitochondrial genomes(mitogenomes)from 13 morphological species to reconstruct the phylogenetic relationships,biogeographic history,and species diversity of Silurus.Our phylogenetic reconstructions identified eight clades,supported by both maximum-likelihood and Bayesian inference.Sequence-based species delimitation analyses yielded multiple molecular operational taxonomic units(MOTUs)in several taxa,including the Silurus asotus complex(four MOTUs)and Silurus microdorsalis(two MOTUs),suggesting that species diversity is underestimated in the genus.A reconstructed time-calibrated tree of Silurus species provided an age estimate of the most recent common ancestor of approximately 37.61 million years ago(Ma),with divergences among clades within the genus occurring between 11.56 Ma and 29.44 Ma,and divergences among MOTUs within species occurring between 3.71 Ma and 11.56 Ma.Biogeographic reconstructions suggested that the ancestral area for the genus likely encompassed China and the Korean Peninsula,with multiple inferred dispersal events to Europe and Central and Western Asia between 21.78 Ma and 26.67 Ma and to Japan between 2.51 Ma and 18.42 Ma.Key factors such as the Eocene-Oligocene extinction event,onset and intensification of the monsoon system,and glacial cycles associated with sea-level fluctuations have likely played significant roles in shaping the evolutionary history of the genus Silurus.

The urban land area Change in China from 1820 to 1999

According to the length of city perimeter and the administration systems recorded in the historical literatures of the Qing Dynasty, a set of methods is developed to convert the historical records into the area of urban land use, by which a set of preliminary estimated urban land use data of the 18 provinces during the Emperor Jiaqing (1820AD) in the Qing Dynasty, is achieved. Based on the above achievements, the regional differences of urban land use are analyzed, and the comparison in urban land use between the Qing Dynasty and present (1999) is made.

Body Surface Area Prediction in Odorrana grahami

Body surface area（BSA）was regarded as a more readily quantifiable parameter relative to body mass in the normalization of comparative biochemistry and physiology.The BSA prediction has attracted unceasing research back more than a century on animals,especially on humans and rats.Few studies in this area for anurans were reported,and the equation for body surface area（S）and body mass（W）：S=9.9 W 0.56,which was concluded from toads of four species in 1969,was generally adopted to estimate the body surface areas for anurans until recent years.However,this equation was not applicable to Odorrana grahami.The relationship between body surface area and body mass for this species was established as：S=15.4 W 0.579.Our current results suggest estimation equations should be used cautiously across different species and body surface area predictions on more species need to be conducted.

A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment

Simulation (stochastic) methods are based on obtaining random samples θ5 from the desired distribution p(θ) and estimating the expectation of any function h(θ). Simulation methods can be used for high-dimensional distributions, and there are general algorithms which work for a wide variety of models. Markov chain Monte Carlo (MCMC) methods have been important in making Bayesian inference practical for generic hierarchical models in small area estimation. Small area estimation is a method for producing reliable estimates for small areas. Model based Bayesian small area estimation methods are becoming popular for their ability to combine information from several sources as well as taking account of spatial prediction of spatial data. In this study, detailed simulation algorithm is given and the performance of a non-trivial extension of hierarchical Bayesian model for binary data under spatial misalignment is assessed. Both areal level and unit level latent processes were considered in modeling. The process models generated from the predictors were used to construct the basis so as to alleviate the problem of collinearity between the true predictor variables and the spatial random process. The performance of the proposed model was assessed using MCMC simulation studies. The performance was evaluated with respect to root mean square error (RMSE), Mean absolute error (MAE) and coverage probability of corresponding 95% CI of the estimate. The estimates from the proposed model perform better than the direct estimate.

Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys

Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large complex surveys. Frequentist approaches, such as bootstrapping, and Monte Carlo (MC) simulation, are also applied but not evaluated in terms of the interval magnitude, width, and the computational time consumed. The 2013 Florida Behavioral Risk Factor Surveillance System data was used as a case study. County-level estimated prevalence of three health-related outcomes was obtained through a GLMM;and their 95% confidence intervals (CIs) were generated from bootstrapping and MC simulation. The intervals were compared to 95% credential intervals through a hierarchial Bayesian model. The results showed that 95% CIs for county-level estimates of each outcome by using MC simulation were similar to the 95% credible intervals generated by Bayesian estimation and were the most computationally efficient. It could be a viable option for constructing statistical intervals for small area estimation in public health practice.

BLUP Estimation of Linear Mixed-effects Models with Measurement Errors and Its Applications to the Estimation of Small Areas

The linear mixed-effects model （LMM） is a very useful tool for analyzing cluster data. In practice, however, the exact values of the variables are often difficult to observe. In this paper, we consider the LMM with measurement errors in the covariates. The empirical BLUP estimator of the linear combination of the fixed and random effects and its approximate conditional MSE are derived. The application to the estimation of small area is provided. Simulation study shows good performance of the proposed estimators.

Summaries of three keynote lectures at the SAE–2018

Three keynote lectures are presented at the conference of Small Area Estimation and OtherTopics of Current Interest in Surveys, Official Statistics and General Statistics (SAE 2018), an international conference held between June 16 and 18 at East China Normal University, Shanghai,China. The speakers of these lectures are world famous statistics professors, James O. Berger,J. N. K. Rao and Malay Ghosh. The lectures mainly review the previous studies and present thepioneering results covering Bayesian statistics, small area estimation, shrinkage priors, etc.

Interview with Professor Danny Pfeffermann

An interview with Professor Danny Pfeffermann is conducted during the conference of SmallArea Estimation 2018 (SAE 2018), an international conference held between June 16 and 18at East China Normal University, Shanghai, China. SAE 2018 is also a celebration of ProfessorPfeffermann’s 75th birthday. Our interview consists of eight questions, which focus on Professor Pfeffermann’s personal education background, research motivations, contributions to thedevelopment of statistics, opinions on big data and data science, and his future plan. Professor Pfeffemann used interesting examples to express his opinions on the future development ofstatistics.