A chip-level space-time equalization receiver scheme is proposed for multiple-input multiple-output high-speed downlink packet access (MIMO HSDPA) systems to jointly combat the co-channel interference and the inter-co...A chip-level space-time equalization receiver scheme is proposed for multiple-input multiple-output high-speed downlink packet access (MIMO HSDPA) systems to jointly combat the co-channel interference and the inter-code interference. A fractional sample equalizer is also derived to further improve the performance of the receiver. Performance analysis and the calculation of the output signal to interference ratio (SINR) at each receiver antenna are presented to help direct the design of equalization weight in a more optimal manner. System simulations demonstrate the significant performance gain over conventional Rake receiver and high potential of MIMO HSDPA for high-data-rate packet transmission.展开更多
Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
Upper ocean heat content is a factor critical to the intensity change of tropical cyclones(TCs). Because of the inhomogeneity of in situ observations in the North Indian Ocean,gridded temperature/salinity(T/S) profile...Upper ocean heat content is a factor critical to the intensity change of tropical cyclones(TCs). Because of the inhomogeneity of in situ observations in the North Indian Ocean,gridded temperature/salinity(T/S) profiles were derived from satellite data for 1993–2012 using a linear regression method. The satellite derived T/S dataset covered the region of 10°S–32°N,25°–100°E with daily temporal resolution,0.25°×0.25° spatial resolution,and 26 vertical layers from the sea surface to a depth of 1 000 m at standard layers. Independent Global Temperature Salinity Profile Project data were used to validate the satellite derived T/S fields. The analysis confirmed that the satellite derived temperature field represented the characteristics and vertical structure of the temperature field well. The results demonstrated that the vertically averaged root mean square error of the temperature was 0.83 in the upper 1 000 m and the corresponding correlation coefficient was 0.87,which accounted for 76% of the observed variance. After verification of the satellite derived T/S dataset,the TC heat potential(TCHP) was verified. The results show that the satellite derived values were coherent with observed TCHP data with a correlation coefficient of 0.86 and statistical significance at the 99% confidence level. The intensity change of TC Gonu during a period of rapid intensification was studied using satellite derived TCHP data. A delayed effect of the TCHP was found in relation to the intensity change of Gonu,suggesting a lag feature in the response of the inner core of the TC to the ocean.展开更多
Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacoki...Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.展开更多
This paper investigates a multi-period mean-variance portfolio selection with regime switching and uncertain exit time. The returns of assets all depend on the states of the stochastic market which are assumed to foll...This paper investigates a multi-period mean-variance portfolio selection with regime switching and uncertain exit time. The returns of assets all depend on the states of the stochastic market which are assumed to follow a discrete-time Markov chain. The authors derive the optimal strategy and the efficient frontier of the model in closed-form. Some results in the existing literature are obtained as special cases of our results.展开更多
文摘A chip-level space-time equalization receiver scheme is proposed for multiple-input multiple-output high-speed downlink packet access (MIMO HSDPA) systems to jointly combat the co-channel interference and the inter-code interference. A fractional sample equalizer is also derived to further improve the performance of the receiver. Performance analysis and the calculation of the output signal to interference ratio (SINR) at each receiver antenna are presented to help direct the design of equalization weight in a more optimal manner. System simulations demonstrate the significant performance gain over conventional Rake receiver and high potential of MIMO HSDPA for high-data-rate packet transmission.
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
基金Supported by the National Basic Research Program of China(973 Program)(No.2013CB430304)the National Natural Science Foundation of China(Nos.41030854,41106005,41176003,41206178,41376015,41376013,41306006)the National High-Tech R&D Program of China(No.2013AA09A505)
文摘Upper ocean heat content is a factor critical to the intensity change of tropical cyclones(TCs). Because of the inhomogeneity of in situ observations in the North Indian Ocean,gridded temperature/salinity(T/S) profiles were derived from satellite data for 1993–2012 using a linear regression method. The satellite derived T/S dataset covered the region of 10°S–32°N,25°–100°E with daily temporal resolution,0.25°×0.25° spatial resolution,and 26 vertical layers from the sea surface to a depth of 1 000 m at standard layers. Independent Global Temperature Salinity Profile Project data were used to validate the satellite derived T/S fields. The analysis confirmed that the satellite derived temperature field represented the characteristics and vertical structure of the temperature field well. The results demonstrated that the vertically averaged root mean square error of the temperature was 0.83 in the upper 1 000 m and the corresponding correlation coefficient was 0.87,which accounted for 76% of the observed variance. After verification of the satellite derived T/S dataset,the TC heat potential(TCHP) was verified. The results show that the satellite derived values were coherent with observed TCHP data with a correlation coefficient of 0.86 and statistical significance at the 99% confidence level. The intensity change of TC Gonu during a period of rapid intensification was studied using satellite derived TCHP data. A delayed effect of the TCHP was found in relation to the intensity change of Gonu,suggesting a lag feature in the response of the inner core of the TC to the ocean.
基金Project(31200748)supported by the National Natural Science Foundation of China
文摘Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.
基金supported in part by the National Basic Research Program (2007CB814906)the National Natural Science Foundation of China (10471103 and 10771158)+2 种基金Social Science Foundation of the Ministry of Education of China (Numerical methods for convertible bonds, 06JA630047)Tianjin Natural Science Foundation (07JCYBJC14300)the National Science Foundation under Grant No. EAR-0934747
文摘This article summarizes our recent work on uniform error estimates for various finite elementmethods for time-dependent advection-diffusion equations.
基金This research is supported by the National Science Foundation for Distinguished Young Scholars under Grant No. 70825002, the National Natural Science Foundation of China under Grant No. 70518001, and the National Basic Research Program of China 973 Program, under Grant No. 2007CB814902.
文摘This paper investigates a multi-period mean-variance portfolio selection with regime switching and uncertain exit time. The returns of assets all depend on the states of the stochastic market which are assumed to follow a discrete-time Markov chain. The authors derive the optimal strategy and the efficient frontier of the model in closed-form. Some results in the existing literature are obtained as special cases of our results.
