Left ventricular remodeling index （LVRI） was assessed in patients with hypertensive heart disease （HHD） and coronary artery disease （CAD） by real-time three-dimensional echocardiography （RT3DE）. RT3DE data of ...Left ventricular remodeling index （LVRI） was assessed in patients with hypertensive heart disease （HHD） and coronary artery disease （CAD） by real-time three-dimensional echocardiography （RT3DE）. RT3DE data of 18 patients with HHD, 20 patients with CAD and 22 normal controis （NC） were acquired. Left ventricular end-diastolic volume （EDV） and left ventricular end-diastolic epicardial volume （EDVepi） were detected by RT3DE and two-dimensional echocardiography Simpson biplane method （2DE）. LVRI （left ventricular mass/EDV） was calculated and compared. The results showed that LVRI measurements detected by RT3DE and 2DE showed significant differences inter-groups （P〈0.01）. There was no significant difference in NC group （P〉0.05）, but significant difference in HHD and CAD intra-group （P〈0.05）. There was good positive correlations between LVRI detected by RT3DE and 2DE in NC and HHD groups （t=0.69, P〈0.01; r=0.68, P〈0.01）, but no significant correlation in CAD group （r=0.30, P〉0.05）. It was concluded that LVRI derived from RT3DE as a new index for evaluating left ventricular remodeling can provide more superiority to LVRI derived from 2DE.展开更多
To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overco...To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overcome the defect of much information loss for iDistance in one-dimensional transformation,the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector,and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively.By employing the classical B+tree,this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing.Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.展开更多
The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased si...The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.展开更多
This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficient...This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.展开更多
Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curs...Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.展开更多
文摘Left ventricular remodeling index （LVRI） was assessed in patients with hypertensive heart disease （HHD） and coronary artery disease （CAD） by real-time three-dimensional echocardiography （RT3DE）. RT3DE data of 18 patients with HHD, 20 patients with CAD and 22 normal controis （NC） were acquired. Left ventricular end-diastolic volume （EDV） and left ventricular end-diastolic epicardial volume （EDVepi） were detected by RT3DE and two-dimensional echocardiography Simpson biplane method （2DE）. LVRI （left ventricular mass/EDV） was calculated and compared. The results showed that LVRI measurements detected by RT3DE and 2DE showed significant differences inter-groups （P〈0.01）. There was no significant difference in NC group （P〉0.05）, but significant difference in HHD and CAD intra-group （P〈0.05）. There was good positive correlations between LVRI detected by RT3DE and 2DE in NC and HHD groups （t=0.69, P〈0.01; r=0.68, P〈0.01）, but no significant correlation in CAD group （r=0.30, P〉0.05）. It was concluded that LVRI derived from RT3DE as a new index for evaluating left ventricular remodeling can provide more superiority to LVRI derived from 2DE.
基金Sponsored by the National High Technology Research and Development Program of China (863 Program)(Grant No.[2005]555)
文摘To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overcome the defect of much information loss for iDistance in one-dimensional transformation,the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector,and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively.By employing the classical B+tree,this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing.Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92167106,61833014)Key Research and Development Program of Zhejiang Province(2022C01206)。
文摘The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.
文摘This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.
基金Project (No. [2005]555) supported by the Hi-Tech Research and De-velopment Program (863) of China
文摘Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.
