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公开(公告)号:US11649517B2
公开(公告)日:2023-05-16
申请号:US16083713
申请日:2017-09-20
Inventor: Changheui Jang , Hyunmyung Kim , Gokul Obulan Subramanian , Jin Woo Heo , Ho Jung Lee , Sunghoon Hong , Chaewon Kim
IPC: C21D9/46 , C21D6/00 , C21D8/02 , C22C38/02 , C22C38/04 , C22C38/06 , C22C38/48 , C22C38/50 , C22C38/18
CPC classification number: C21D9/46 , C21D6/004 , C21D8/0205 , C21D8/0226 , C21D8/0236 , C21D8/0263 , C22C38/02 , C22C38/04 , C22C38/06 , C22C38/18 , C22C38/48 , C22C38/50 , C21D2211/001 , C21D2211/004 , C21D2211/005
Abstract: The present disclosure relates to a high-strength Fe—Cr—Al—Ni multiplex stainless steel and a manufacturing method therefor. The multiplex stainless steel comprises 35 to 67 wt % of iron (Fe), 13 to 30 wt % of chrome (Cr), 15 to 30 wt % of nickel (Ni), and 5 to 15 wt % of aluminum (Al) and has a multiplex structure in which an austenite phase accounting for high ductility, a ferrite phase accounting for high strength, and an NiAl(B2) phase providing both strength and high-temperature steam oxidation resistance, exist in combination. The multiplex stainless steel can secure necessary fabricability and mechanical strength even if for/in a thin state, can maintain integrity as a structural member in a normal operation condition of a light-water reactor thanks to the formation of a chrome oxide layer thereon, and can form a stable oxide layer including alumina under a high-temperature steam environment, which is plausible in a high-temperature nuclear accident, thereby providing exceptionally improved resistance to serious accidents.
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公开(公告)号:US11663796B2
公开(公告)日:2023-05-30
申请号:US17085349
申请日:2020-10-30
Inventor: Inwoo Ha , Changho Jo , Jaeyoon Kim , Sungeui Yoon , Sunghoon Hong
IPC: G06K9/46 , G06K9/00 , G06K9/20 , G06T7/70 , G06T7/50 , G06T19/00 , G06N3/08 , G06N3/04 , G06V10/60 , G06V10/22 , G06V20/00
CPC classification number: G06V10/60 , G06N3/04 , G06N3/08 , G06T7/50 , G06T7/70 , G06T19/006 , G06V10/22 , G06V20/00 , G06T2200/24 , G06T2207/20081 , G06T2207/20084
Abstract: A processor-implemented light source information output method includes: receiving an input image; detecting, using a trained neural network, at least one object in the input image; estimating, using the trained neural network, light source information of a light source corresponding to the at least one object; and outputting the light source information.
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