It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of th...It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of the data,or the conclusions reached.The authors would like to apologize for any inconvenience caused.展开更多
A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error ...A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.展开更多
The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from whi...The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.展开更多
A single column model (SCM) is constructed by extracting the physical subroutines from the NCAR Community Climate Model version 1 (CCM1).Simulated data are generated by CCM1 and used to validate the SCM and to study t...A single column model (SCM) is constructed by extracting the physical subroutines from the NCAR Community Climate Model version 1 (CCM1).Simulated data are generated by CCM1 and used to validate the SCM and to study the sensitivity of the SCM to errors in its input data.It is found that the SCM temperature predictions are moderately sensitive to errors in the input horizontal temperature flux convergence and moisture flux convergence.Two types of error are concerned in this study,random errors due to insufficient data resolution,and errors due to insufficient data area coverage.While the first type of error can be reduced by filtering and/or increasing the data resolution,it is shown that the second type of error can be reduced by enlarging the data area coverage and using a suitable method to compute the input flux convergence terms.展开更多
Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the availa...Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.展开更多
The data processing technique and the method determining the optimal number of measured points are studied aiming at the sphericity error measured on a coordinate measurement machine （CMM）. The consummate criterion ...The data processing technique and the method determining the optimal number of measured points are studied aiming at the sphericity error measured on a coordinate measurement machine （CMM）. The consummate criterion for the minimum zone of spherical surface is analyzed first, and then an approximation technique searching for the minimum sphericity error from the form data is studied. In order to obtain the minimum zone of spherical surface, the radial separation is reduced gradually by moving the center of the concentric spheres along certain directions with certain steps. Therefore the algorithm is precise and efficient. After the appropriate mathematical model for the approximation technique is created, a data processing program is developed accordingly. By processing the metrical data with the developed program, the spherical errors are evaluated when different numbers of measured points are taken from the same sample, and then the corresponding scatter diagram and fit curve for the sample are graphically represented. The optimal number of measured points is determined through regression analysis. Experiment shows that both the data processing technique and the method for determining the optimal number of measured points are effective. On average, the obtained sphericity error is 5.78 μm smaller than the least square solution, whose accuracy is increased by 8.63%; The obtained optimal number of measured points is half of the number usually measured.展开更多
Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is con...Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is consequentlyof vital importance to safe and economic operation of generating units. NSH CALIBRATOR could complete, not only theon-line performance and parameter tests of safety valves within two to five seconds with opening pressure of safetyvalves and steam medium pressure automatically recorded, but also could complete the on-line adjustment of safetyvalves verified unqualified. It saves energy consumption, decreases noise pollution and improves accuracy and efficiencyof safety valve calibration.[展开更多
In this paper we discuss the edge-preserving regularization method in the reconstruction of physical parameters from geophysical data such as seismic and ground-penetrating radar data. In the regularization method a p...In this paper we discuss the edge-preserving regularization method in the reconstruction of physical parameters from geophysical data such as seismic and ground-penetrating radar data. In the regularization method a potential function of model parameters and its corresponding functions are introduced. This method is stable and able to preserve boundaries, and protect resolution. The effect of regularization depends to a great extent on the suitable choice of regularization parameters. The influence of the edge-preserving parameters on the reconstruction results is investigated and the relationship between the regularization parameters and the error of data is described.展开更多
The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods.Due to the inevitable measurement error,the analytics on the error data is critical to evaluate localization ...The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods.Due to the inevitable measurement error,the analytics on the error data is critical to evaluate localization methods and to find the effective ones.For indoor localization,Received Signal Strength(RSS)is a convenient and low-cost measurement that has been adopted in many localization approaches.However,using RSS data for localization needs to solve a fundamental problem,that is,how accurate are these methods?The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data.In this proposed work,we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cram′er-Rao Lower Bound(CRLB).Through mathematical techniques,the key factors that affect the accuracy of RSS-based localization methods are revealed,and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived.The significance of our discovery has two folds:First,we present a general expression for localization error data analytics,which can explain and predict the accuracy of range-based localization algorithms;second,the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.展开更多
The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and ...The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and reliability of the existing schemes are vulnerable to high resistance and data errors.To improve the insufficiencies,this paper proposes a pilot protection scheme by using the random matrix for DC lines in the symmetrical bipolar MMC high-voltage direct current(HVDC)grid.Firstly,the 1-mode voltage time-domain characteristics of the line end,DC bus,and adjacent line end are analyzed by the inverse Laplace transform to find indicators of fault direction.To combine the actual model with the data-driven method,the methods to construct the data expansion matrix and to calculate additional noise are proposed.Then,the mean spectral radiuses of two random matrices are used to detect fault directions,and a novel pilot protection criterion is proposed.The protection scheme only needs to transmit logic signals,decreasing the communication burden.It performs well in high-resistance faults,abnormal data errors,measurement errors,parameters errors,and different topology conditions.Numerous simulations in PSCAD/EMTDC confirm the effectiveness and reliability of the proposed protection scheme.展开更多
文摘It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of the data,or the conclusions reached.The authors would like to apologize for any inconvenience caused.
文摘A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.
基金supported by the National Natural Science Foundation of China (Grant No. 11173038)
文摘The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.
文摘A single column model (SCM) is constructed by extracting the physical subroutines from the NCAR Community Climate Model version 1 (CCM1).Simulated data are generated by CCM1 and used to validate the SCM and to study the sensitivity of the SCM to errors in its input data.It is found that the SCM temperature predictions are moderately sensitive to errors in the input horizontal temperature flux convergence and moisture flux convergence.Two types of error are concerned in this study,random errors due to insufficient data resolution,and errors due to insufficient data area coverage.While the first type of error can be reduced by filtering and/or increasing the data resolution,it is shown that the second type of error can be reduced by enlarging the data area coverage and using a suitable method to compute the input flux convergence terms.
基金This work was supported by the National Natural Science Foundation of China(No.61672301)Jilin Provincial Science&Technology Development(20180101054JC)+1 种基金Science and Technology Innovation Guide Project of Inner Mongolia Autonomous Region of China(2017)Talent Development Fund of Jilin Province(2018).
文摘Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.
基金This project is supported by National Natural Science Foundation of China (No.50475117)Municipal Science and Technology Commission of,Tianjin China(No.0431835116).
文摘The data processing technique and the method determining the optimal number of measured points are studied aiming at the sphericity error measured on a coordinate measurement machine （CMM）. The consummate criterion for the minimum zone of spherical surface is analyzed first, and then an approximation technique searching for the minimum sphericity error from the form data is studied. In order to obtain the minimum zone of spherical surface, the radial separation is reduced gradually by moving the center of the concentric spheres along certain directions with certain steps. Therefore the algorithm is precise and efficient. After the appropriate mathematical model for the approximation technique is created, a data processing program is developed accordingly. By processing the metrical data with the developed program, the spherical errors are evaluated when different numbers of measured points are taken from the same sample, and then the corresponding scatter diagram and fit curve for the sample are graphically represented. The optimal number of measured points is determined through regression analysis. Experiment shows that both the data processing technique and the method for determining the optimal number of measured points are effective. On average, the obtained sphericity error is 5.78 μm smaller than the least square solution, whose accuracy is increased by 8.63%; The obtained optimal number of measured points is half of the number usually measured.
文摘Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is consequentlyof vital importance to safe and economic operation of generating units. NSH CALIBRATOR could complete, not only theon-line performance and parameter tests of safety valves within two to five seconds with opening pressure of safetyvalves and steam medium pressure automatically recorded, but also could complete the on-line adjustment of safetyvalves verified unqualified. It saves energy consumption, decreases noise pollution and improves accuracy and efficiencyof safety valve calibration.[
基金supported in part by the National Natural Science Foundation of China under Grant-in-Aid 40574053the Program for New Century Excellent Talents in University of China (NCET-06-0602)the National 973 Key Basic Research Development Program (No.2007CB209601)
文摘In this paper we discuss the edge-preserving regularization method in the reconstruction of physical parameters from geophysical data such as seismic and ground-penetrating radar data. In the regularization method a potential function of model parameters and its corresponding functions are introduced. This method is stable and able to preserve boundaries, and protect resolution. The effect of regularization depends to a great extent on the suitable choice of regularization parameters. The influence of the edge-preserving parameters on the reconstruction results is investigated and the relationship between the regularization parameters and the error of data is described.
基金partially supported by the National Key Research and Development Program of China(No.2016YFE0121800)
文摘The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods.Due to the inevitable measurement error,the analytics on the error data is critical to evaluate localization methods and to find the effective ones.For indoor localization,Received Signal Strength(RSS)is a convenient and low-cost measurement that has been adopted in many localization approaches.However,using RSS data for localization needs to solve a fundamental problem,that is,how accurate are these methods?The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data.In this proposed work,we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cram′er-Rao Lower Bound(CRLB).Through mathematical techniques,the key factors that affect the accuracy of RSS-based localization methods are revealed,and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived.The significance of our discovery has two folds:First,we present a general expression for localization error data analytics,which can explain and predict the accuracy of range-based localization algorithms;second,the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.
基金supported by the State Scholarship Fund of China Scholarship Council(No.202007000168).
文摘The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and reliability of the existing schemes are vulnerable to high resistance and data errors.To improve the insufficiencies,this paper proposes a pilot protection scheme by using the random matrix for DC lines in the symmetrical bipolar MMC high-voltage direct current(HVDC)grid.Firstly,the 1-mode voltage time-domain characteristics of the line end,DC bus,and adjacent line end are analyzed by the inverse Laplace transform to find indicators of fault direction.To combine the actual model with the data-driven method,the methods to construct the data expansion matrix and to calculate additional noise are proposed.Then,the mean spectral radiuses of two random matrices are used to detect fault directions,and a novel pilot protection criterion is proposed.The protection scheme only needs to transmit logic signals,decreasing the communication burden.It performs well in high-resistance faults,abnormal data errors,measurement errors,parameters errors,and different topology conditions.Numerous simulations in PSCAD/EMTDC confirm the effectiveness and reliability of the proposed protection scheme.
