Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Longcan Chen"'
Autor:
Dongsheng Jia, Pengcheng Feng, Liangdi Wang, Longcan Chen, Jun Wang, Jihong Zhu, Yingjie Xu, Weihong Zhang
Publikováno v:
Defence Technology, Vol 31, Iss , Pp 447-456 (2024)
In this paper, the design, manufacture and testing of an origami protective shield with a supporting frame structure are presented. It consists of an origami shield surface and a deployable supporting frame structure that needs to be portable and suf
Externí odkaz:
https://doaj.org/article/4f5f393a0edd4f61804d55a003b1a36a
Autor:
Dongsheng Jia, Pengcheng Feng, Liangdi Wang, Longcan Chen, Jun Wang, Jihong Zhu, Yingjie Xu, Weihong Zhang
Publikováno v:
Defence Technology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::092982616029000b84d1058b118fb913
https://doi.org/10.1016/j.dt.2023.02.022
https://doi.org/10.1016/j.dt.2023.02.022
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031147203
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20e983a5849a833016ee9c99d6307add
https://doi.org/10.1007/978-3-031-14721-0_17
https://doi.org/10.1007/978-3-031-14721-0_17
Publikováno v:
2021 IEEE Symposium Series on Computational Intelligence (SSCI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3214c673219a25d0eac17485841aab77
https://doi.org/10.1109/ssci50451.2021.9659851
https://doi.org/10.1109/ssci50451.2021.9659851
Fault Detection and Diagnosis Based on the Machine Learning Method of Lifting Scheme Wavelet and PCA
Publikováno v:
2021 2nd Asia Symposium on Signal Processing (ASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ea336782c2484b17d1aefa0627d0a83
https://doi.org/10.1109/assp54407.2021.00023
https://doi.org/10.1109/assp54407.2021.00023
Publikováno v:
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e191fec362c357cca60e26018ebdb78c
https://doi.org/10.1109/smc52423.2021.9659260
https://doi.org/10.1109/smc52423.2021.9659260
Publikováno v:
CEC
In the evolutionary multi-objective optimization (EMO) community, an unbounded external archive has been used in some studies for evaluating the performance of EMO algorithms. Those studies show that the unbounded external archive often includes bett
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f2d4097ca31b58e1718c0cee01ca31c
https://doi.org/10.1109/cec45853.2021.9504831
https://doi.org/10.1109/cec45853.2021.9504831
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030720612
EMO
EMO
R2HCA-EMOA is a recently proposed hypervolume-based evolutionary multi-objective optimization (EMO) algorithm. It uses an R2 indicator variant to approximate the hypervolume contribution of each solution. Meanwhile, it uses a utility tensor structure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8739e569417a9350dc52523b81d34721
https://doi.org/10.1007/978-3-030-72062-9_10
https://doi.org/10.1007/978-3-030-72062-9_10
Publikováno v:
SSCI
Recently, a number of many-objective evolutionary algorithms have been proposed in the literature. Those algorithms are often evaluated using the frequently-used DTLZ and WFG test problems. One feature of those test problems is the use of the same di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58b108d67e5a2400ff4f38670061b5f8
https://doi.org/10.1109/ssci47803.2020.9308558
https://doi.org/10.1109/ssci47803.2020.9308558
Autor:
Longcan Chen, Xiaojie Chao
Publikováno v:
Journal of Physics: Conference Series. 2235:012033
In the current industrial field control, most of the servo control systems communicate with the motion controller and the servo driver, and if the industrial site needs to modify the parameters of the servo system in time according to the requirement
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c9db4f5f6620d0c2838b59429a8ebed
https://doi.org/10.1088/1742-6596/2235/1/012033
https://doi.org/10.1088/1742-6596/2235/1/012033