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        An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network

        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.

      • SCIESCOPUSKCI등재

        A high-density gamma white spots-Gaussian mixture noise removal method for neutron images denoising based on Swin Transformer UNet and Monte Carlo calculation

        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.

      • A Multi-cultural Application of the Consensual Assessment Technique

        Beth A. Hennessey,Gia Kim,Zheng Guomin,Sun Weiwei 대한사고개발학회 2008 The International Journal of Creativity & Problem Vol.18 No.2

        The Consensual Assessment Technique for Product Creativity (CAT) (Amabile, 1982, 1983a; Hennessey & Amabile, 1999) is based on the assumption that a panel of independent judges, persons with expertise in a particular field who have not had the opportunity to confer with one another or the researcher and who have never been trained as to how to make their ratings, are best able to make judgments of product creativity and related dimensions. Over 25 years of research carried out in the West have clearly established that the creativity of products can, in fact, be reliably and validly assessed based upon the consensus of experts. The purpose of the present paper is to explore the applicability of the CAT in investigations involving products and raters in non-Western societies. Elementary school teacher-judges were recruited in the US, Saudi Arabia, China and South Korea and were asked to make assessments of collages and stories created by children living in their local area. Across all four cultural contexts, judges’ ratings of product creativity were found to be highly reliable. The argument is made that because investigative paradigms employing the CAT focus on the production of real-world, tangible products that are rated by persons who share the cultural background of the individuals doing the creating, a multiplicity of practical and theoretical problems and biases typically associated with cross-national investigations are avoided. Researchers are encouraged to explore the utility of the CAT for multi-cultural investigations.

      • Defect-Free Encapsulation of Fe<sup>0</sup> in 2D Fused Organic Networks as a Durable Oxygen Reduction Electrocatalyst

        Kim, Seok-Jin,Mahmood, Javeed,Kim, Changmin,Han, Gao-Feng,Kim, Seong-Wook,Jung, Sun-Min,Zhu, Guomin,De Yoreo, James J.,Kim, Guntae,Baek, Jong-Beom American Chemical Society 2018 JOURNAL OF THE AMERICAN CHEMICAL SOCIETY - Vol.140 No.5

        <P>Because they provide lower cost but comparable activity to precious platinum (Pt)-based catalysts, nonprecious iron (Fe)-based materials, such as Fe/Fe<SUB>3</SUB>C and Fe–N–C, have gained considerable attention as electrocatalysts for the oxygen reduction reaction (ORR). However, their practical application is hindered by their poor stability, which is attributed to the defective protection of extremely unstable Fe nanoparticles. Here, we introduce a synthesis strategy for a stable Fe-based electrocatalyst, which was realized by defect-free encapsulation of Fe nanoparticles using a two-dimensional (2D) phenazine-based fused aromatic porous organic network (Aza-PON). The resulting Fe@Aza-PON catalyst showed electrocatalytic activity (half-wave potential, 0.839 V; Tafel slope, 60 mV decade<SUP>–1</SUP>) comparable to commercial Pt on activated carbon (Pt/C, 0.826 V and 90 mV decade<SUP>–1</SUP>). More importantly, the Fe@Aza-PON displayed outstanding stability (zero current loss even after 100 000 cycles) and tolerance against contamination (methanol and CO poisoning). In a hybrid Li–air battery test, the Fe@Aza-PON demonstrated performance superior to Pt/C.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jacsat/2018/jacsat.2018.140.issue-5/jacs.7b10663/production/images/medium/ja-2017-10663c_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ja7b10663'>ACS Electronic Supporting Info</A></P>

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