To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bia...To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused.展开更多
Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, ...Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.展开更多
In Hungary a general agricultural census （AC） was carried out in 2000, followed in 2001 by the population and housing census. The two censuses had been designed separately. Originally the Hungarian Central Statistic...In Hungary a general agricultural census （AC） was carried out in 2000, followed in 2001 by the population and housing census. The two censuses had been designed separately. Originally the Hungarian Central Statistical Office （HCSO） did not plan the joint analysis of the data of the two censuses. Following the censuses users and researchers expressed the view that linking the data of the two databases would represent a value-added in the use of the data and the joint utilization of the databases of the two censuses was examined. The databases were matched and the aggregated handling of the information increased the potential for analysing both censuses and allowed further, more sophisticated investigations. By means of the databases of the two censuses, the first opportunity arose for matching the discrete data of the surveys. The precondition of the matching of the data was the conformity of the respective metadata of the two operations. ,,Private holding＂ and ,,dwelling-household＂ were the categories applicable as the smallest unit for the matching. The links between the private holdings and the households could be based on the identity of the persons living in the dwelling. The matching of the data required the use of individual identity codes. With the matching process used a joint database of the agricultural and population censuses was set up providing new approaches for gender disaggregated analysis. By using the linked database, the HCSO issued a series of publications on the households living in agricultural private holdings in the countryside. This presentation describes the method of matching the databases of the two censuses.展开更多
Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sam...Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.展开更多
文摘To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused.
基金Supported by National High Technology Project (863)(No. 2006AA02Z320)the National Natural Science Founda-tion of China (No.30700154, No.60874105)+1 种基金Zhejiang Natural Science Foundation (No.Y107458, RY1080422)the School Youth Found of Shanghai Jiaotong University
文摘Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.
文摘In Hungary a general agricultural census （AC） was carried out in 2000, followed in 2001 by the population and housing census. The two censuses had been designed separately. Originally the Hungarian Central Statistical Office （HCSO） did not plan the joint analysis of the data of the two censuses. Following the censuses users and researchers expressed the view that linking the data of the two databases would represent a value-added in the use of the data and the joint utilization of the databases of the two censuses was examined. The databases were matched and the aggregated handling of the information increased the potential for analysing both censuses and allowed further, more sophisticated investigations. By means of the databases of the two censuses, the first opportunity arose for matching the discrete data of the surveys. The precondition of the matching of the data was the conformity of the respective metadata of the two operations. ,,Private holding＂ and ,,dwelling-household＂ were the categories applicable as the smallest unit for the matching. The links between the private holdings and the households could be based on the identity of the persons living in the dwelling. The matching of the data required the use of individual identity codes. With the matching process used a joint database of the agricultural and population censuses was set up providing new approaches for gender disaggregated analysis. By using the linked database, the HCSO issued a series of publications on the households living in agricultural private holdings in the countryside. This presentation describes the method of matching the databases of the two censuses.
基金supported by National Natural Science Foundation of China(Grant Nos.10971122,11101274 and 11322109)Scientific and Technological Projects of Shandong Province(Grant No.2009GG10001012)Excellent Young Scientist Foundation of Shandong Province(Grant No.BS2012SF025)
文摘Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.
