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Haichao Cai,Chunguang Xu,Qinxue Pan,Hongjuan Yan 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10
Ultrasonic guided wave detection technology has numerous advantages compared with body waves. Accurately obtaining the time-of-arrival (TOA) or time-of-flight (TOF) of the ultrasonic guided wave signal is critical for the detection and location of defect because of the frequent use of the low frequency guided wave probe in testing. Thus, guaranteeing the propagation over a long distance and increasing the resolution of defect detection become the key points. This study adds the weighted nonlinear transformation functions to the minimum entropy deconvolution (MED). By adjusting the corresponding parameters, it can enhance a weak signal of small defects and suppress the noise signal. The experimental results indicate that this method can accurately obtain TOA to enhance the resolution of defect detection and can improve the speed of convergence effectively compared with MED.
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Third Harmonic Generation of Shear Horizontal Guided Waves Propagation in Plate-like Structures
Weibin Li,Chunguang Xu,Younho Cho 한국비파괴검사학회 2016 한국비파괴검사학회지 Vol.36 No.2
The use of nonlinear ultrasonics wave has been accepted as a promising tool for monitoring material states related to microstructural changes, as it has improved sensitivity compared to conventional non-destructive testing approaches. In this paper, third harmonic generation of shear horizontal guided waves propagating in an isotropic plate is investigated using the perturbation method and modal analysis approach. An experimental procedure is proposed to detect the third harmonics of shear horizontal guided waves by electromagnetic transducers. The strongly nonlinear response of shear horizontal guided waves is measured. The accumulative growth of relative acoustic nonlinear response with an increase of propagation distance is detected in this investigation. The experimental results agree with the theoretical prediction, and thus providing another indication of the feasibility of using higher harmonic generation of electromagnetic shear horizontal guided waves for material characterization.
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Third Harmonic Generation of Shear Horizontal Guided Waves Propagation in Plate-like Structures
Li, Weibin,Xu, Chunguang,Cho, Younho The Korean Society for Nondestructive Testing 2016 한국비파괴검사학회지 Vol.36 No.2
The use of nonlinear ultrasonics wave has been accepted as a promising tool for monitoring material states related to microstructural changes, as it has improved sensitivity compared to conventional non-destructive testing approaches. In this paper, third harmonic generation of shear horizontal guided waves propagating in an isotropic plate is investigated using the perturbation method and modal analysis approach. An experimental procedure is proposed to detect the third harmonics of shear horizontal guided waves by electromagnetic transducers. The strongly nonlinear response of shear horizontal guided waves is measured. The accumulative growth of relative acoustic nonlinear response with an increase of propagation distance is detected in this investigation. The experimental results agree with the theoretical prediction, and thus providing another indication of the feasibility of using higher harmonic generation of electromagnetic shear horizontal guided waves for material characterization.
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Wang, Chenchong,Shen, Chunguang,Huo, Xiaojie,Zhang, Chi,Xu, Wei Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.5
In order to make reasonable design for the improvement of comprehensive mechanical properties of RAFM steels, the design system with both machine learning and high-throughput optimization algorithm was established. As the basis of the design system, a dataset of RAFM steels was compiled from previous literatures. Then, feature engineering guided random forests regressors were trained by the dataset and NSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution set with nine composition features and two treatment processing features. The selected optimal solutions by this design system showed prospective mechanical properties, which was also consistent with the physical metallurgy theory. This efficiency design mode could give the enlightenment for the design of other metal structural materials with the requirement of multi-properties.
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Xinyu Cao,Yin Fang,Chunguang Yang,Zhenghao Liu,Guoping Xu,Yan Jiang,Peiyan Wu,Wenbo Song,Hanshuo Xing,Xinglong Wu 대한배뇨장애요실금학회 2024 International Neurourology Journal Vol.28 No.1
Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions: This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.
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