Calibration coefficients validation is the foundation for ascertaining the sensor performance and carrying out the quantitative application.Based on the analysis of the differences between the calibration and validati...Calibration coefficients validation is the foundation for ascertaining the sensor performance and carrying out the quantitative application.Based on the analysis of the differences between the calibration and validation,two calibration coefficients validation methods were introduced in this paper.Taking the HJ-1A satellite CCD1 camera as an example,the uncertainties of calibration coefficients validation were analyzed.The calibration coefficients validation errors were simulated based on the measured data at an Inner Mongolia test site.The result showed that in the large view angle,the ground directional reflectance variation and the atmospheric path variation were the main error sources in calibration coefficients validation.The ground directional reflectance correction and atmospheric observation angle normalization should be carried out to improve the validation accuracy of calibration coefficients.展开更多
The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However...The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.展开更多
基金supported by the International Science and Technology Cooperation Program of China(Grant No.2008DFA21540)the National Hi-Tech Research and Development Program of China(Grant No.2006AA12Z113)+1 种基金the Chinese Defense Advance Research Program of Science and Technologythe Young Talents Filed Special Project of Institute of Remote Sensing and Application of Chinese Academy of Sciences
文摘Calibration coefficients validation is the foundation for ascertaining the sensor performance and carrying out the quantitative application.Based on the analysis of the differences between the calibration and validation,two calibration coefficients validation methods were introduced in this paper.Taking the HJ-1A satellite CCD1 camera as an example,the uncertainties of calibration coefficients validation were analyzed.The calibration coefficients validation errors were simulated based on the measured data at an Inner Mongolia test site.The result showed that in the large view angle,the ground directional reflectance variation and the atmospheric path variation were the main error sources in calibration coefficients validation.The ground directional reflectance correction and atmospheric observation angle normalization should be carried out to improve the validation accuracy of calibration coefficients.
基金supported by the National Natural Science Foundation of China(Grant No.51176030)Jiangsu Science and Technology Department(Grant No.BY2015070-17)
文摘The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.
