Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shanhao Li"'
Autor:
Miaomiao Liu, Ziwei Huang, Yukun Guo, Zhengwei Zhang, Liqiang Zhang, Hongmei Zhang, Jiang Zhong, Shanhao Li, Wei Deng, Di Wang, Wei Li, Ying Huangfu, Xiangdong Yang, Xidong Duan
Publikováno v:
InfoMat, Vol 5, Iss 11, Pp n/a-n/a (2023)
Abstract The controllable synthesis of complicated nanostructures in advanced two‐dimensional (2D) semiconductors, such as periodic regular hole arrays, is essential and remains immature. Here, we report a green, facile, highly controlled synthetic
Externí odkaz:
https://doaj.org/article/b7ceb06c1f3b4b018d56282e6ff88c67
Publikováno v:
IEEE Access, Vol 10, Pp 31751-31759 (2022)
A portion of the data in video captioning datasets are noisy and unsuitable for models to learn at early stages, e.g., there could be a generic 4-word-long caption lacking distinctive details of video content and a 19-word-long description with rare
Externí odkaz:
https://doaj.org/article/e84dd86aca16444cb0f09e9db904e42e
Publikováno v:
IEEE Access, Vol 6, Pp 46801-46825 (2018)
Query and prediction have been proved to be one of the most important operations for uncertain spatiotemporal data and deserve further study. In this paper, we propose an approach to predict uncertain spatiotemporal data, which is intended to integra
Externí odkaz:
https://doaj.org/article/188cb7bae8fe481a9fd77783b094a8fd
Publikováno v:
Ceramics International. 49:21658-21666
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::392e2573739dc208aaca73c5480c7e12
https://doi.org/10.1016/j.ceramint.2023.03.303
https://doi.org/10.1016/j.ceramint.2023.03.303
Publikováno v:
2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e42364d0a529f241c5f5ff4b4efe1be
https://doi.org/10.1109/icaibd55127.2022.9820499
https://doi.org/10.1109/icaibd55127.2022.9820499
Publikováno v:
Dyes and Pigments. 206:110678
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::592fed66552e3dd839a7e806d01e83ec
https://doi.org/10.1016/j.dyepig.2022.110678
https://doi.org/10.1016/j.dyepig.2022.110678
Publikováno v:
IEEE Access, Vol 6, Pp 46801-46825 (2018)
Query and prediction have been proved to be one of the most important operations for uncertain spatiotemporal data and deserve further study. In this paper, we propose an approach to predict uncertain spatiotemporal data, which is intended to integra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbc424da22370b31f9b09c67336c2ff7
https://doi.org/10.1109/access.2018.2866408
https://doi.org/10.1109/access.2018.2866408