Error Evaluation Method of ElectronicTransformer Based on Large Dimensional Random Matrices

2019 ◽  
Author(s):  
Jie Fan ◽  
Keqi Ma ◽  
Minrui Xu ◽  
Wenguang Chen ◽  
Qiao Wu ◽  
...  
Keyword(s):  
Random Matrices ◽  
Evaluation Method ◽  
Error Evaluation

The minimum zone fitting and error evaluation for the logarithmic curve based on geometry optimization approximation algorithm

2019 ◽  
Vol 41 (15) ◽  
pp. 4380-4386
Author(s):  
Tu Xianping ◽  
Lei Xianqing ◽  
Ma Wensuo ◽  
Wang Xiaoyi ◽  
Hu Luqing ◽  
...  
Keyword(s):  
Approximation Algorithm ◽  
Evaluation Method ◽  
Geometry Optimization ◽  
Reference Points ◽  
Approximation Technique ◽  
Error Evaluation ◽  
Iterative Approximation ◽  
Logarithmic Curve ◽  
Normal Distance ◽  
Minimum Zone

The minimum zone fitting and error evaluation for the logarithmic curve has important applications. Based on geometry optimization approximation algorithm whilst considering geometric characteristics of logarithmic curves, a new fitting and error evaluation method for the logarithmic curve is presented. To this end, two feature points, to serve as reference, are chosen either from those located on the least squares logarithmic curve or from amongst measurement points. Four auxiliary points surrounding each of the two reference points are then arranged to resemble vertices of a square. Subsequently, based on these auxiliary points, a series of auxiliary logarithmic curves (16 curves) are constructed, and the normal distance and corresponding range of values between each measurement point and all auxiliary logarithmic curves are calculated. Finally, by means of an iterative approximation technique consisting of comparing, evaluating, and changing reference points; determining new auxiliary points; and constructing corresponding auxiliary logarithmic curves, minimum zone fitting and evaluation of logarithmic curve profile errors are implemented. The example results show that the logarithmic curve can be fitted, and its profile error can be evaluated effectively and precisely using the presented method.

The shape error evaluation methods of the foils for a multi-leaf foil bearing

2018 ◽  
Vol 233 (2) ◽  
pp. 504-513 ◽  
Author(s):  
Yuan Yu ◽  
Cheng Ren ◽  
Yanhua Sun
Keyword(s):  
Evaluation Method ◽  
Least Square ◽  
Shape Error ◽  
Pressing Pressure ◽  
Error Evaluation ◽  
Profile Error ◽  
Roundness Error ◽  
Manufacturing Method ◽  
Foil Bearing ◽  
Curve Profile

The geometric parameters of the elastic foil bearing are important basis for designing the foil bearing. Whether the main shape indices of the pressed foils meet the design requirements is the key to evaluate a manufacturing method. The inconsistent curvature radii of the top foils, the inconsistent bump heights of the bumps on the bump foil, the roundness error of the top foil, and the curve profile error of the bump foil may cause the pre-tightening assembly difference and different bearing capacity of each bearing shell. Aiming at the shape error evaluations of the foils for a multi-leaf foil bearing, first, the algorithm of geometric area optimization for circle roundness error is introduced and an evaluation method of arc roundness error is put forward according to the minimum zone principle in this paper. This method can be used to calculate the roundness error of the top foil for a multi-leaf foil bearing. The results show that the corresponding roundness error of the top foil decreases with the increasing of the pressing pressure. The relative roundness error is small (less than 7%), which changes a little with different pressing pressures. For the measurement of a bottom bump foil, the method of matching feature points is used in pre-location and then fine-positioning for the measured curve is implemented based on the least square method in order to eliminate the position error between the measured curve and the design curve. Thus, the curve profile error evaluation of the bottom bump foil for a multi-leaf foil bearing is implemented and the profile error of each bump can be obtained. According to the shape error evaluation values of the top foil and the bump foil, the quality control strategy and the error compensation by improving the mold structure can be further researched.

Using Machine Learning Tools for Forecasting Natural Gas Market Demand

2020 ◽  
Author(s):  
Kai Yang ◽  
Lei Hou
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Evaluation Method ◽  
Gas Pipeline ◽  
Prediction Methods ◽  
Error Evaluation ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Gas Consumption ◽  
Gas Supply

Abstract Providing reliable and accurate forecasts of natural gas consumption can keep supply and demand of natural gas pipelines in balance, which can increase profits and reduce supply risks. In order to accurately predict the short-term load demand of different gas nodes in the natural gas pipeline network, a hybrid optimization strategy of integrated genetic optimization algorithm and support vector machine are proposed. Factors such as holidays, date types and weather were taken into account to build a natural gas daily load prediction model based on GA-SVM was established. A natural gas pipeline network in China includes three gas supply nodes of different user type gas is forecasted, and a variety of error evaluation method, the GA-SVM evaluation index compared with other prediction methods, and through different data set partition is discussed in the periods of peak gas and gas resources in the GA — the applicability of the SVM prediction model, the ends of a natural gas pipeline network in China includes four gas supply nodes of different user type gas is forecasted, and a variety of error evaluation method, the GA-SVM evaluation index compared with other prediction methods, The applicability of the method is also discussed by dividing different data sets. By predicting the gas load forecast of the three nodes, the results show that GA-SVM hybrid prediction model has high prediction accuracy compared with other single models, and the three gas nodes MAPE of GA-SVM is respectively 3.66%, 5.17% and 3.43%. Through further analysis, even with the data samples reduced, the winter gas peak of gas prediction can still maintain good prediction effects. The research shows that the GA-SVM model has high accuracy and strong applicability in predicting gas consumption at different nodes of the natural gas pipeline network. This study can provide a research basis for analysis of gas supply uncertainty and further gas supply reliability evaluation of pipeline network.

scholarly journals An Error Evaluation Method for the Ship Angular Flexure Measurement Based on the Principle of Relevance

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yutong Zhang ◽  
Xianglu Ma ◽  
Shiqiao Qin ◽  
Wei Wu ◽  
Wenfeng Tan
Keyword(s):  
Measurement Error ◽  
Evaluation Method ◽  
Estimation Error ◽  
Good Prediction ◽  
Error Evaluation ◽  
Prediction Formula ◽  
Matching Method ◽  
Error Covariance ◽  
Velocity Matching ◽  
Flexure Angle

Due to the lack of true ship angular flexure data, it is difficult to evaluate its measurement error of the angular velocity matching method in practice. In this paper, the cause of the measurement error of the ship flexure angle is analyzed in theory, and an evaluation method for the ship angular flexure measurement error based on the principle of relevance is proposed. The proposed method provides a prediction formula to describe the estimation error of the static flexure angle based on the off-diagonal elements of the error covariance matrix P in Kalman filtering. In addition, the optimized coefficient F is introduced to make the prediction error range better describe the real error variation. The optimized coefficient F ensures that the proposed formula has good prediction effects in all three directions. Simulations based on the actual measured ship flexure data are carried out, and the simulation results verify the capability of the prediction formula. The proposed method can be used in the evaluation of the ship flexure measurement error.

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