In allusion to the character of monocular image sequences, a method based on extended Kalman filtering to predict the aircraft pose of image sequences is proposed. With α - β - γ stable state filtering technique, a...In allusion to the character of monocular image sequences, a method based on extended Kalman filtering to predict the aircraft pose of image sequences is proposed. With α - β - γ stable state filtering technique, a mathematics model is built to realize the prediction of aircraft pose of image sequences. In the model, not only the influence of noise during the image process is considered, but also the shortcoming of low precision in the constant velocity model is overcomed. The derivation of acceleration is considered as white noise. The predictive curve plotted with Matlab proves that the maximum of error of using this method is about 3’. So its precision is higher and error standard deviation is lower than those of the constant velocity model.展开更多
Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is conta...Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.展开更多
Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation me...Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.展开更多
Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods i...Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods including chemical methods,X-ray fluorescence and atomic absorption spectrometry are advanced and accurate.However,in some applications,such as on-line assaying process,high accuracy is required.In this paper,an algorithm based on Kalman Filter is presented to predict on-line XRF errors.This research has been carried out on the basis of based the industrial real data collection for evaluating the performance of the presented algorithm.The measurements and analysis for this study were conducted at the Sarcheshmeh Copper Concentrator Plant located in Iran.The quality of the obtained results was very satisfied;so that the RMS errors of prediction obtained for Cu and Mo grade assaying errors in rougher feed were less than 0.039 and 0.002 and in final flotation concentration less than 0.58 and 0.074,respectively.The results indicate that the mentioned method is quite accurate to reduce the on-line XRF errors measurement.展开更多
基金Innovation item of Chinese Academy of Science(Grant No. C04708Z)
文摘In allusion to the character of monocular image sequences, a method based on extended Kalman filtering to predict the aircraft pose of image sequences is proposed. With α - β - γ stable state filtering technique, a mathematics model is built to realize the prediction of aircraft pose of image sequences. In the model, not only the influence of noise during the image process is considered, but also the shortcoming of low precision in the constant velocity model is overcomed. The derivation of acceleration is considered as white noise. The predictive curve plotted with Matlab proves that the maximum of error of using this method is about 3’. So its precision is higher and error standard deviation is lower than those of the constant velocity model.
文摘Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.
基金the Ministry of Finance of China and China Meteorological Administration for the Special Project of Meteorological Sector (Grant No. GYHY(QX)2007-615)the National Basic Research Program of China (Grant No. 2005CB321703)
文摘Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.
基金the support of the Department of Research and Development of Sarcheshmeh Copper Plants for this research
文摘Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods including chemical methods,X-ray fluorescence and atomic absorption spectrometry are advanced and accurate.However,in some applications,such as on-line assaying process,high accuracy is required.In this paper,an algorithm based on Kalman Filter is presented to predict on-line XRF errors.This research has been carried out on the basis of based the industrial real data collection for evaluating the performance of the presented algorithm.The measurements and analysis for this study were conducted at the Sarcheshmeh Copper Concentrator Plant located in Iran.The quality of the obtained results was very satisfied;so that the RMS errors of prediction obtained for Cu and Mo grade assaying errors in rougher feed were less than 0.039 and 0.002 and in final flotation concentration less than 0.58 and 0.074,respectively.The results indicate that the mentioned method is quite accurate to reduce the on-line XRF errors measurement.
