Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance con...Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.展开更多
In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of wa...In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.展开更多
The bowtie effect refers to geometry distortions for the moderate resolution imaging spectrum-radiometer （MODIS） level 1B （L1 B） data. Till now, to eliminate the bowtie effect, numerous methods are proposed. Howev...The bowtie effect refers to geometry distortions for the moderate resolution imaging spectrum-radiometer （MODIS） level 1B （L1 B） data. Till now, to eliminate the bowtie effect, numerous methods are proposed. However, most of them have limitations in computation efficiency. Through a comparative study of existing methods, this article puts forward a fast method to eliminate the bowtie effect using the ephemeris data. In this method, the rough positions of overlapping data are first detected. Because of the influence caused by the instrarnent characters and the earth＇s curvature, the positions of overlapping data need to be rectified to obtain more precise results. The optimal rectification method used in this article is selected by comparing three methods. By using the optimal method, the rectified MODIS data can be obtained. The experiments demonstrate that the bowtie effect can be eliminated in less than 1 s. In contrast, other traditional methods spend at least 3 s, thus the proposed method is faster and more effective.展开更多
基金Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)
文摘Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
基金Supported by the National Natural Science Foundation of China (No. 60774092, No. 60901003)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070294027)
文摘In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.
基金supported by the Hi-Tech Research and Development Program of China (2006AA06A205-3)
文摘The bowtie effect refers to geometry distortions for the moderate resolution imaging spectrum-radiometer （MODIS） level 1B （L1 B） data. Till now, to eliminate the bowtie effect, numerous methods are proposed. However, most of them have limitations in computation efficiency. Through a comparative study of existing methods, this article puts forward a fast method to eliminate the bowtie effect using the ephemeris data. In this method, the rough positions of overlapping data are first detected. Because of the influence caused by the instrarnent characters and the earth＇s curvature, the positions of overlapping data need to be rectified to obtain more precise results. The optimal rectification method used in this article is selected by comparing three methods. By using the optimal method, the rectified MODIS data can be obtained. The experiments demonstrate that the bowtie effect can be eliminated in less than 1 s. In contrast, other traditional methods spend at least 3 s, thus the proposed method is faster and more effective.
