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公开(公告)号:US20230121481A1
公开(公告)日:2023-04-20
申请号:US17967749
申请日:2022-10-17
发明人: Soon Chul Byun , Sang Mo Kim , Yong Seok Choi , Jae Min Lim , Hoon Seok , Sang Young Lee , Yong Hyeok Lee , Yi Su Jeong
IPC分类号: H01M10/056 , H01M10/052
摘要: Disclosed are a hybrid solid electrolyte sheet and a method of manufacturing the same. The hybrid solid electrolyte sheet includes a hybrid solid electrolyte layer including a gel polymer electrolyte, thereby securing flexibility and alleviating brittleness. In addition, the hybrid solid electrolyte sheet includes a porous polymer film having a plurality of pores, thus minimizing the content of the acrylate monomer in the pores thereof and providing advantages of maintaining the continuity of the solid electrolyte while minimizing a decrease in ionic conductivity.
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公开(公告)号:US20240039036A1
公开(公告)日:2024-02-01
申请号:US18085996
申请日:2022-12-21
发明人: Hoon Seok , Yeong Jun Cheon , Hong Seok Min , Sang Young Lee , Kyeong Seok Oh , Yong Hyeok Lee
IPC分类号: H01M10/056 , H01M10/44 , H01M10/0585
CPC分类号: H01M10/056 , H01M10/446 , H01M10/0585
摘要: A flexible self-supporting solid electrolyte membrane, an all-solid-state battery including the membrane, and a manufacturing method thereof are disclosed. The solid electrolyte membrane may include: a substrate including pores therein; and a solid electrolyte layer disposed on at least one surface of the substrate and including a solid electrolyte and a cured compound. At least a portion of the solid electrolyte layer may penetrate into the pores of the substrate to form a conduction path of lithium ions in a thickness direction of the substrate.
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公开(公告)号:US20240038217A1
公开(公告)日:2024-02-01
申请号:US18130794
申请日:2023-04-04
发明人: Yong Hyeok Lee
IPC分类号: G10L15/06 , G10L21/0208 , G10L15/02 , G10L15/01
CPC分类号: G10L15/063 , G10L21/0208 , G10L15/02 , G10L15/01 , G10L2015/0635
摘要: In an embodiment a system includes a training data preparation device configured to obtain a speech recognition rate of speech data for training using a target speech recognition model, a recognition rate prediction model configured to estimate an expected recognition rate of the target speech recognition model for clean speech data in which noise is removed from the speech data for training and a speech preprocessing model configured to preprocess the speech data for training to obtain the clean speech data and to update the speech preprocessing model based on a recognition rate loss corresponding to a difference between the expected recognition rate and a maximum recognition rate.
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