scholarly journals An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3689 ◽  
Author(s):  
Siyu Ye ◽  
Yi Zhang ◽  
Wen Yao ◽  
Quan Chen ◽  
Xiaoqian Chen
Keyword(s):  
Prediction Accuracy ◽  
Traffic Volume ◽  
Engineering Problem ◽  
Modeling Method ◽  
Distribution Data ◽  
Communication Link ◽  
Ensemble Modeling ◽  
Business Services ◽  
Table Lookup ◽  
Data Table

The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.

A Novel Modeling Method Based on Multi-Dimensional Taylor Network and its Application in Time Series Prediction

2014 ◽  
Vol 940 ◽  
pp. 480-484 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Dynamic Characteristics ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Modeling Method ◽  
Input Output ◽  
Output Data ◽  
Bridge Tower

A novel modeling method based on multi-dimensional Taylor network is proposed. The structure and the principle of the multi-dimensional Taylor network are introduced. Based on this, the method is applied in the nonlinear time series prediction based on multi-dimensional Taylor network. It provides a new method to predict the time series, which can describe the dynamic characteristics without prior knowledge and can realize the prediction of the nonlinear time series just with input-output data. An example of predicting the stress data of a large span bridge tower induced by strong typhoon is taken at last in this paper. Results indicate the validity and the better prediction accuracy of this method in nonlinear time series prediction.

scholarly journals Supplemental Material: A test of the efficacy of sand saltation for silt production: Implications for the interpretation of loess

2020 ◽  
Author(s):  
Steven Adams ◽  
Gerilyn Soreghan
Keyword(s):  
Grain Size ◽  
Mass Loss ◽  
Size Distribution ◽  
Control Sample ◽  
Distribution Data ◽  
Detailed Calculation ◽  
Point Counting ◽  
Data Table ◽  
Counting Data ◽  
Scaling Methods

Table S1 (GPS coordinates of sample locations), Table S2 (grain-size distribution data; https://doi.pangaea.de/10.1594/pangaea.914301), Table S3 (point-counting data), Table S4 (automated surface observing stations [ASOS] GPS and wind data), Table S5 (scaling methods and detailed calculation), Table S6 (law of wall calculation and plot), and Table S7 (control sample mass loss).<br>

scholarly journals Polynomial Response Surface based on basis function selection by multitask optimization and ensemble modeling

2021 ◽  
Author(s):  
Yong Zhao ◽  
Siyu Ye ◽  
Xianqi Chen ◽  
Yufeng Xia ◽  
Xiaohu Zheng
Keyword(s):  
Basis Function ◽  
Information Exchange ◽  
Polynomial Regression ◽  
Information Criterion ◽  
Negative Influence ◽  
Engineering Problem ◽  
Basis Functions ◽  
Ensemble Modeling ◽  
Polynomial Response Surface ◽  
Function Selection

AbstractPolynomial Regression Surface (PRS) is a commonly used surrogate model for its simplicity, good interpretability, and computational efficiency. The performance of PRS is largely dependent on its basis functions. With limited samples, how to correctly select basis functions remains a challenging problem. To improve prediction accuracy, a PRS modeling approach based on multitask optimization and ensemble modeling (PRS-MOEM) is proposed for rational basis function selection with robustness. First, the training set is partitioned into multiple subsets by the cross validation method, and for each subset a sub-model is independently constructed by optimization. To effectively solve these multiple optimization tasks, an improved evolutionary algorithm with transfer migration is developed, which can enhance the optimization efficiency and robustness by useful information exchange between these similar optimization tasks. Second, a novel ensemble method is proposed to integrate the multiple sub-models into the final model. The significance of each basis function is scored according to the error estimation of the sub-models and the occurrence frequency of the basis functions in all the sub-models. Then the basis functions are ranked and selected based on the bias-corrected Akaike’s information criterion. PRS-MOEM can effectively mitigate the negative influence from the sub-models with large prediction error, and alleviate the uncertain impact resulting from the randomness of training subsets. Thus the basis function selection accuracy and robustness can be enhanced. Seven numerical examples and an engineering problem are utilized to test and verify the effectiveness of PRS-MOEM.

Prediction of Traffic Volume in Highway Tunnel Group Region Based on Grey Markov Model

2013 ◽  
Vol 712-715 ◽  
pp. 2981-2985 ◽  
Author(s):  
Wei Zhan ◽  
Qing Lu ◽  
Yue Quan Shang
Keyword(s):  
Markov Model ◽  
Early Warning ◽  
Prediction Accuracy ◽  
Development Trend ◽  
Traffic Volume ◽  
Grey Model ◽  
Prediction Methods ◽  
Highway Tunnel ◽  
The Development Trend ◽  
Highway Management

Based on the investigation and analysis of the traffic volume in highway tunnel group region, the development trend of traffic volume is analyzed by Grey model. Then the prediction accuracy is improved by Markov optimization. The method in this paper has a better prediction accuracy and practicality in a period than other common prediction methods. It can be used for the prediction analysis of traffic volume and for early warning by highway management.

scholarly journals Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1369
Author(s):  
Luyue Xia ◽  
Shanshan Liu ◽  
Haitian Pan
Keyword(s):  
Ionic Liquids ◽  
Co2 Capture ◽  
Entropy Method ◽  
Modeling Method ◽  
Ensemble Model ◽  
Ensemble Modeling ◽  
Co2 Solubility ◽  
Fully Integrated ◽  
Imidazolium Ionic Liquids ◽  
Selective Ensemble

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)

2011 ◽  
Vol 66-68 ◽  
pp. 563-568
Author(s):  
Yi Sheng An ◽  
Hua Cui ◽  
Shan Guan Wei ◽  
Xiang Mo Zhao
Keyword(s):  
Prediction Accuracy ◽  
Influence Factors ◽  
Traffic Volume ◽  
Forecasting Model ◽  
Traffic Data ◽  
Grey Forecasting ◽  
The Poor ◽  
Simulation Results ◽  
Volume Models ◽  
Real Traffic

To circumvent the poor prediction accuracy of traffic volume models available due to the lack of traffic data and inaccurate judgments on the traffic influence factors, in this paper we established a traffic volume prediction model using grey forecasting model GM(1,1) based on the real traffic data from the highway toll database. The GM(1,1) method has advantage of the strong adaptiveness to Complex system, thus getting a great advantage over other methods for modeling such a complex nonlinear traffic volume system with many uncertain influence factors. Simulation results show that our GM(1,1) model has mean relative prediction error of 3.9%, which accomplishes our intended prediction accuracy.

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