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RISS 인기검색어
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- 저자 : Zihui Yang (검색결과 5 건)
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Di Zhang,Guomin Sun,Zihui Yang,Jie Yu Korean Nuclear Society 2024 Nuclear Engineering and Technology Vol.56 No.2
During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.
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Zhou Wen,Sun Guomin,Miwa Shuichiro,Yang Zihui,Li Zhuang,Zhang Di,Wang Jianye 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.9
To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium selfsufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.
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Few-shot transfer learning with attention for intelligent fault diagnosis of bearing
Yao Hu,Qingyu Xiong,Qiwu Zhu,Zhengyi Yang,Zhiyuan Zhang,Dan Wu,Zihui Wu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
The bearing is one of the key components in modern industrial equipment. In the past few years, many studies have been carried out on bearing diagnosis through datadriven methods. However, there are two practical problems. First, under actual working conditions, the lack of fault samples is a major factor that hinders the application of these methods in industrial environments. Second, there is a lack of full utilization of a priori knowledge in the current stage of methods using relational networks for fault diagnosis. It is manifested by the incompleteness of the relational network structure. To address these problems, we present a new diagnosis method based on few-shot learning, which is suitable for the environment where the data is scarce. In this method, we train the model with the data generated by the artificial damaged bearings instead of the data from the real bearing. We experimentally validate the performance improvement of the complete relational network structure. It is able to perform the few-shot learning task better. In addition, we also reduce the global feature discrepancy by introducing an attention mechanism to improve the performance of the model. And the impact of the number of layers of the attention mechanism on the model is also discussed in detail. In this paper, our model performs better under the same experimental conditions compared with other transfer learning models.
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Herb Howard C. Hernandez,Eng Hui Ong,Louise Heyzer,Cai Ning Tan,Faezah Ghazali,Daphne Zihui Yang,Hee-Won Jung,Noor Hafizah Ismail,Wee Shiong Lim 대한노인병학회 2022 Annals of geriatric medicine and research Vol.26 No.2
Background: The Short Physical Performance Battery (SPPB) is a well-established functional assessment tool used for the screening and assessment of frailty and sarcopenia. However, the SPPB requires trained staff experienced in conducting the standardized protocol, which may limit its widespread use in clinical settings. The automated SPPB (eSPPB) was developed to address this potential barrier; however, its validity among frail older adults remains to be established. Therefore, this exploratory study compared the eSPPB and manual SPPB in patients attending a tertiary fall clinic in relation to their construct validity, reliability, and agreement.Methods: We studied 37 community-dwelling older adults (mean age, 78.5±6.8 years; mean FRAIL score, 1.2±1.0; 65% pre-frail) attending a tertiary falls clinic. The participants used the mSPPB and eSPPB simultaneously. We evaluated the convergent validity, discriminatory ability, reliability, and agreement using partial correlation adjusted for age and sex, an SPPB cutoff of ≤8 to denote sarcopenia, intraclass correlation coefficients (ICC), and Bland-Altman plots, respectively.Results: The eSPPB showed strong correlations with the mSPPB (r=0.933, p<0.01) and Berg Balance Scale (r=0.869, p<0.01), good discriminatory ability for frailty and balance, and good to excellent reliability (ICC=0.94; 95% confidence interval, 0.88–0.97). The Bland-Altman plots indicated good agreement with the mSPPB (mean difference, -0.2; 95% confidence interval, -3.2–2.9) without evidence of systematic or proportional biases.Conclusion: The results of our exploratory study corroborated the construct validity, reliability, and agreement of the eSPPB with the mSPPB in a small sample of predominantly pre-frail older adults with increased fall risk. Future studies should examine the scalability and feasibility of the widespread use of the eSPPB for frailty and sarcopenia assessment.
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