We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations b...We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.展开更多
To reveal the principles of human thermal responses and find out the effects of body parts on whole-body thermal sensation,through a subjective survey,experimental investigations on human responses are carried out whe...To reveal the principles of human thermal responses and find out the effects of body parts on whole-body thermal sensation,through a subjective survey,experimental investigations on human responses are carried out when a single body part is thermally stimulated.Cooling airflow is sent to seven body parts,respectively.Totally 94 samples are tested.To eliminate the obvious multicollinearity of thermal sensation among different body parts,the principal component regression approach is adopted to obtain the principal components for the body parts under different experimental conditions.Through regression and analysis of principal components,the weighting factors of the seven body parts are obtained.A predictive model on whole-body thermal sensation is obtained based on the weighting factors.The results show that the different characteristics of trunk and limbs are clearly seen.The weighting factors of local thermal sensation are integrated values,and there is little difference among values of different body parts.展开更多
In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,th...In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter(CKF)framework.The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF.Furthermore,the improved range-parameterize(RP)strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently.Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not,whereas that of the conventional algorithms becomes distorted seriously when outliers appear.展开更多
基金supported by National major special equipment development(No.2011YQ120045)The National Natural Science Fund(No.41074050 and 41304023)
文摘We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.
基金The National Natural Science Foundation of China(No.50678030)
文摘To reveal the principles of human thermal responses and find out the effects of body parts on whole-body thermal sensation,through a subjective survey,experimental investigations on human responses are carried out when a single body part is thermally stimulated.Cooling airflow is sent to seven body parts,respectively.Totally 94 samples are tested.To eliminate the obvious multicollinearity of thermal sensation among different body parts,the principal component regression approach is adopted to obtain the principal components for the body parts under different experimental conditions.Through regression and analysis of principal components,the weighting factors of the seven body parts are obtained.A predictive model on whole-body thermal sensation is obtained based on the weighting factors.The results show that the different characteristics of trunk and limbs are clearly seen.The weighting factors of local thermal sensation are integrated values,and there is little difference among values of different body parts.
基金Projects(51377172,51577191) supported by the National Natural Science Foundation of China
文摘In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter(CKF)framework.The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF.Furthermore,the improved range-parameterize(RP)strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently.Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not,whereas that of the conventional algorithms becomes distorted seriously when outliers appear.
