Probing deformation behavior of a refractory high-entropy alloy using neutron diffraction

Zhou, Y.*; Song, W.*; Zhang, F.*; Wu, Y.*; Lei, Z.*; Jiao, M.*; Zhang, X.*; Dong, J.*; Zhang, Y.*; Yang, M.*; et al.

Journal of Alloys and Compounds, 971, p.172635_1 - 172635_7, 2024/01

https://doi.org/10.1016/j.jallcom.2023.172635

A Study of tungsten spectra using Large Helical Device and Compact Electron Beam Ion Trap in NIFS

Morita, Shigeru*; Dong, C. F.*; Goto, Motoshi*; Kato, Daiji*; Murakami, Izumi*; Sakaue, Hiroyuki*; Hasuo, Masahiro*; Koike, Fumihiro*; Nakamura, Nobuyuki*; Oishi, Tetsutaro*; et al.

AIP Conference Proceedings 1545, p.143 - 152, 2013/07

https://doi.org/10.1063/1.4815848

Tungsten spectra have been observed from Large Helical Device (LHD) and Compact electron Beam Ion Trap (CoBIT) in wavelength ranges of visible to EUV. The tungsten spectra from LHD are well analyzed based on the knowledge from CoBIT tungsten spectra. The C-R model code has been developed to explain the UTA spectra in details. Radial profiles of EUV spectra from highly ionized tungsten ions have been measured and analyzed by impurity transport simulation code with ADPAK atomic database code to examine the ionization balance determined by ionization and recombination rate coefficients. The ablation cloud of the impurity pellet is directly measured with visible spectroscopy.

Anomaly detection and identification of equipment and plants using deep learning

Demachi, Kazuyuki*; Dong, F.*; Abe, Toru*; Chen, S.*; Takaya, Shigeru; Seki, Akiyuki; Yoshikawa, Masanori; Miki, Daisuke*

no journal, , 

Integrating deep learning-based object detection and optical character recognition for automatic extraction of link information from piping and instrumentation diagrams

Dong, F.*; Chen, S.*; Demachi, Kazuyuki*; Hashidate, Ryuta; Takaya, Shigeru

no journal, , 

Piping and Instrumentation Diagrams contain information about the piping and process equipment together with the instrumentation and control devices, which is essential to the design and management of Nuclear Power Plants. There are abundant complex objects on P&IDs, with imbalanced distribution of these objects and their linked information across different diagrams. Therefore, the content of P&IDs is generally extracted and analyzed manually, which is time consuming and error prone. To efficiently address these issues, we integrate state-of-the-art deep learning-based object detection and Optical Character Recognition models to automatically extract link information from P&IDs. Besides, we propose a novel image pre-processing approach using sliding windows to detect low resolution small objects. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate it capable to extract link information from P&IDs of NPPs.