To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robu...To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.展开更多
Soil spatial variability is difficult to evaluate due to insufficient test data.An alternative option is estimation by indirect methods such as inverse analysis.In this paper,two examples are presented to demonstrate ...Soil spatial variability is difficult to evaluate due to insufficient test data.An alternative option is estimation by indirect methods such as inverse analysis.In this paper,two examples are presented to demonstrate the capability and accuracy of the probabilistic estimation method to characterize soil spatial variability with displacement responses.The first example is a soil slope subject to a surcharge load,in which the spatially varied field of the elastic modulus is estimated with displacements.The results show that estimations based on horizontal displacements were more accurate than those based on vertical displacements.The accuracy of the estimated field was substantially reduced by increasing variance of elastic modulus.However,the estimation was generally acceptable as the error was not more than 10%,even for the high variance case(COV^l.5).The accuracy of estimation was also affected by the type of covariance function and the correlation length.When the correlation length decreased,the accuracy of estimation was reduced.The second example is a validation of laboratory model tests where a horizontal load was applied on a layered ground.The estimated thicknesses of soil layers were close to those in the real situation,which demonstrates the capacity of the estimation method.展开更多
This paper generalizes a method of generating shift sequences in the interleaved construction proposed by Gong.With the new shift sequences,some new families of p-ary sequences with desired properties can be obtained....This paper generalizes a method of generating shift sequences in the interleaved construction proposed by Gong.With the new shift sequences,some new families of p-ary sequences with desired properties can be obtained.A lower bound on the number of new families of binary sequences is also established.展开更多
基金The National Key Research and Development Program of China(No.2018YFB1802400)the National Natural Science Foundation of China(No.61571123)the Research Fund of National M obile Communications Research Laboratory,Southeast University(No.2020A03)
文摘To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.
基金the National Natural Science Foundation of China(Nos.51979158,51639008,51679135,and 51422905)the Program of Shanghai Academic Research Leader(No.19XD1421900),China。
文摘Soil spatial variability is difficult to evaluate due to insufficient test data.An alternative option is estimation by indirect methods such as inverse analysis.In this paper,two examples are presented to demonstrate the capability and accuracy of the probabilistic estimation method to characterize soil spatial variability with displacement responses.The first example is a soil slope subject to a surcharge load,in which the spatially varied field of the elastic modulus is estimated with displacements.The results show that estimations based on horizontal displacements were more accurate than those based on vertical displacements.The accuracy of the estimated field was substantially reduced by increasing variance of elastic modulus.However,the estimation was generally acceptable as the error was not more than 10%,even for the high variance case(COV^l.5).The accuracy of estimation was also affected by the type of covariance function and the correlation length.When the correlation length decreased,the accuracy of estimation was reduced.The second example is a validation of laboratory model tests where a horizontal load was applied on a layered ground.The estimated thicknesses of soil layers were close to those in the real situation,which demonstrates the capacity of the estimation method.
基金supported by the National Natural Science Foundation of China under Grant No.61170257supported by the National Key Basic Research Program of China under Grant No.2013CB834203+1 种基金the National Science Foundation of China under Grant Nos.10990011 and 61070172the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA06010702
文摘This paper generalizes a method of generating shift sequences in the interleaved construction proposed by Gong.With the new shift sequences,some new families of p-ary sequences with desired properties can be obtained.A lower bound on the number of new families of binary sequences is also established.
