scholarly journals Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network

2021 ◽  
Vol 34 (1) ◽  
pp. 79-90
Author(s):  
Xingjie ZHOU ◽  
Xuyun FU ◽  
Minghang ZHAO ◽  
Shisheng ZHONG
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Bp Neural Network ◽  
Domain Adaptation ◽  
Aero Engine ◽  
Parameter Deviation

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

Machining Parameter Optimization of Aero-Engine Blade in Electrochemical Machining Based on BP Neural Network

2010 ◽  
Vol 121-122 ◽  
pp. 893-899 ◽  
Author(s):  
Zhi Yong Li ◽  
Hua Ji ◽  
Hong Li Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electrochemical Machining ◽  
Machining Accuracy ◽  
Machining Parameters ◽  
Parameter Combination ◽  
Aero Engine ◽  
Engine Blade ◽  
Blade Machining ◽  
Machining Parameter

Because the process of blade in electrochemical machining(EMC) can be effected by many factors, such as blade shapes, machining electrical field, electrolyte fluid field and anode electrochemical dissolution, different ECM machining parameters maybe result in great affections on blade machining accuracy. Regard some type of aero-engine blade as research object, five main machining parameters, applied voltage, initial machining gap, cathode feed rate, electrolyte temperature and pressure difference between electrolyte inlet and outlet, have been evaluated and optimized based on BP neural network technique. From 3125 possible machining parameter combinations, 657 optimized parameter combinations are discovered. To verify the validity of the optimized ECM parameter combination, a serial of machining experiments have been conducted on an industrial scale ECM machine, and the experiment results demonstrates that the optimized ECM parameter combination not only can satisfy the manufacturing requirements of blade fully but has excellent ECM process stability.

scholarly journals Prediction Method of Concentricity and Perpendicularity of Aero Engine Multistage Rotors Based on PSO-BP Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 132271-132278 ◽  
Author(s):  
Chuanzhi Sun ◽  
Chengtian Li ◽  
Yongmeng Liu ◽  
Zewei Liu ◽  
Xiaoming Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Aero Engine

scholarly journals Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 695
Author(s):  
Xiaoxiao Zhu ◽  
Yongli Zhou ◽  
Yongjun Yang ◽  
Huping Hou ◽  
Shaoliang Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Spatial Structure ◽  
Regression Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Mine Dumps ◽  
Restoration Age ◽  
Bp Neural Network Model ◽  
Semi Arid

Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an R2 of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an R2 of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.

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