Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Peixin Hou"'
The Impacts of the COVID-19 Pandemic on Fertility Intentions of Women with Childbearing Age in China
Publikováno v:
Behavioral Sciences, Vol 12, Iss 9, p 335 (2022)
On 31 May 2021, the Political Bureau of the Central Committee of the Communist Party of China proposed the policy that a couple can have three children, and rolled out more supportive measures to further optimize the fertility policies. However, whil
Externí odkaz:
https://doaj.org/article/dd08296e74dc4661a5b303e3d7d758e3
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 11, Iss 1 (2018)
This study addresses the construction of Takagi-Sugeno-Kang (TSK) fuzzy models by means of clustering. A contribution-factor based fuzzy min-max neural network (CFMN) is developed based on Simpson’s well-known fuzzy min-max neural network (FMNN) fo
Externí odkaz:
https://doaj.org/article/a6c34ddf3be9459fa21ee4764330cb21
Publikováno v:
Biodemography & Social Biology; Oct-Dec2023, Vol. 68 Issue 4, p166-195, 30p
Publikováno v:
International Journal of Approximate Reasoning. 95:194-212
Possibilistic c-means (PCM) based clustering algorithms are widely used in the literature. Recently, adaptive PCM (APCM) is proposed to adapt the bandwidth at each iteration and the cluster merge is automatically achieved. The cluster elimination abi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32d957981df441db9b2b9d2b9678558f
https://doi.org/10.1016/j.ijar.2018.02.006
https://doi.org/10.1016/j.ijar.2018.02.006
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 11, Iss 1 (2018)
This study addresses the construction of Takagi-Sugeno-Kang (TSK) fuzzy models by means of clustering. A contribution-factor based fuzzy min-max neural network (CFMN) is developed based on Simpson’s well-known fuzzy min-max neural network (FMNN) fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ea495fafb967b9a143eaa1e4bc08aa3
https://www.atlantis-press.com/article/25892525/view
https://www.atlantis-press.com/article/25892525/view
Publikováno v:
FUZZ-IEEE
Possibilistic c-means (PCM) based clustering algorithms are widely used in the literature. In this paper, we develop a noise level based PCM (NPCM) clustering algorithm. The advantage of NPCM is that strong prior information of the dataset is not req
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d40e8f3b2297b3e64d3f710a13e47f11
https://doi.org/10.1109/fuzz-ieee.2017.8015379
https://doi.org/10.1109/fuzz-ieee.2017.8015379
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 39:562-570
The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d64c69aff76ccde3fdd53967b8c57c93
https://doi.org/10.1109/36.911114
https://doi.org/10.1109/36.911114
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 38:515-524
The authors propose an improved version of JPEG coding for compressing remote sensing images obtained by optical sensors onboard microsatellites. The approach involves expanding cloud features to include their cloud-land transitions, thereby simplify
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fb248e48f9c3e0cc4d4c71392371b67
https://doi.org/10.1109/36.823946
https://doi.org/10.1109/36.823946
Publikováno v:
ICPR
In this paper we propose two improvements to standard JPEG coding that can improve the compression ratio achieved for compressing remote sensing images obtained by sensors on-board microsatellites by more than 39-60%. The first improvement consists i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::948d0362b675c5faa208e2d7af3abe2d
https://doi.org/10.1109/icpr.1998.712031
https://doi.org/10.1109/icpr.1998.712031