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14. Study on high-low cycle fretting fatigue life of Inconel718 tenon structure

Author

YANG Xiaolin, MU Qinqin, CHEN Yonghui, and YAN Qun

Subjects
inconel718, high-low cycle load, high-low cycle frequency, fretting fatigue, fatigue life, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

The turbine blades of aero-engines are subjected to cyclic high-low cycle loads in actual service environments，which is a typical high-low cycle micro motion fatigue condition. The frequent occurrence of fatigue fracture in turbine blades due to fretting fatigue seriously affects the safe operation of aero-engines. In order to study the high-low cycle fretting fatigue problems of hot end component materials，this paper designs an Inconel718 tenon connection simulation component and conducts high-low cycle composite fretting fatigue tests on it. Three different load/frequency experiment conditions were designed to compare the life distribution of the simulated parts under different conditions. The results show that high/low cycle load has significant influence on fretting fatigue life. With the increase of high/low cycle load or frequency，the fretting fatigue life of joints decreases. The test results have proven that the high-low cycle fretting fatigue test of the tenon joint structure effectively reproduces the actual service condition of turbine blades. By displaying the explicit finite element analysis，the predicted crack initiation location shows less dispersion compared to the experimental results.

Published
2024
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21. DNNobfus: a study on obfuscation-based edge-side model protection framework

Author

SONG Feiyang, ZHAO Xinmiao, YAN Fei, CHENG Binlin, ZHANG Liqiang, YANG Xiaolin, and WANG Yang

Subjects
artificial intelligence safety, code obfuscation, reverse engineering, model protection, Electronic computers. Computer science, QA75.5-76.95
Abstract

The proliferation of artificial intelligence models has rendered them vulnerable to a myriad of security threats. The extensive integration of deep learning models into edge devices has introduced novel security challenges. Given the analogous structural characteristics of deep neural networks, adversaries can employ decompilation tactics to extract model structural details and parameters, facilitating the reconstruction of these models. Such actions can compromise the intellectual property rights of the model and increase the risk of white-box attacks. To mitigate the capability of model decompilers to locate and identify model operators, acquire parameters, and parse network topologies, an obfuscation framework was proposed. This framework was embedded within the model compilation process to safeguard against model extraction attacks. During the frontend optimization phase of deep learning compilers, three obfuscation techniques were developed and integrated: operator obfuscation, parameter obfuscation, and network topology obfuscation. The framework introduced opaque predicates, incorporated fake control flows, and embedded redundant memory access to thwart the reverse engineering efforts of model decompilers. The experimental findings demonstrate that the obfuscation framework, named DNNobfus, significantly diminishes the accuracy of state-of-the-art model decompilation tools in identifying model operator types and network connections to 21.63% and 48.24%, respectively. Additionally, DNNobfus achieves an average time efficiency of 67.93% and an average space efficiency of 88.37%, surpassing the performance of the obfuscation tool Obfuscator-LLVM in both respects.

Published
2024
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34. AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus

Author

Zhou, Xi, Ye, Qinghao, Yang, Xiaolin, Chen, Jiakuan, Ma, Haiqin, Xia, Jun, Del Ser, Javier, and Yang, Guang

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), Intraclass Correlation Coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2$\pm$2.6 respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0$\pm$3.8 respectively. The whole process takes 3.4$\pm$0.3 seconds. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0$\pm$0.6 respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9$\pm$3.8 respectively. The whole process took 1.9$\pm$0.1 seconds. We have established a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles., Comment: 26 pages, 4 figure, accepted by Neural Computing and Applications journal

Published
2022

37. Optimal Covariate Balancing Conditions in Propensity Score Estimation

Author

Fan, Jianqing, Imai, Kosuke, Lee, Inbeom, Liu, Han, Ning, Yang, and Yang, Xiaolin

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Applications
Abstract

Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model. To address this problem, researchers have proposed to estimate propensity score by directly optimizing the balance of pre-treatment covariates. While these methods appear to empirically perform well, little is known about how the choice of balancing conditions affects their theoretical properties. To fill this gap, we first characterize the asymptotic bias and efficiency of the IPTW estimator based on the Covariate Balancing Propensity Score (CBPS) methodology under local model misspecification. Based on this analysis, we show how to optimally choose the covariate balancing functions and propose an optimal CBPS-based IPTW estimator. This estimator is doubly robust; it is consistent for the ATE if either the propensity score model or the outcome model is correct. In addition, the proposed estimator is locally semiparametric efficient when both models are correctly specified. To further relax the parametric assumptions, we extend our method by using a sieve estimation approach. We show that the resulting estimator is globally efficient under a set of much weaker assumptions and has a smaller asymptotic bias than the existing estimators. Finally, we evaluate the finite sample performance of the proposed estimators via simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

Published
2021

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