Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Zhesen Cui"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract In many applications, there is a need for algorithms that can align partially overlapping point clouds while remaining invariant to corresponding transformations. This research presents a method that achieves these goals by minimizing a bina
Externí odkaz:
https://doaj.org/article/2e0e1951ae9a43d292505abf759beba0
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabi
Externí odkaz:
https://doaj.org/article/590335f69cc34f26883c995285492dbd
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
The power dispatching network forms the backbone of efforts to automate and modernize power grid dispatching, rendering it an indispensable infrastructure element within the power system. However, accurately forecasting future flows remains a formida
Externí odkaz:
https://doaj.org/article/c34785227c554b2a87a76fff37908288
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 1887-1895 (2023)
Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this
Externí odkaz:
https://doaj.org/article/351e408d398842638f098de073aef862
Publikováno v:
IEEE Access, Vol 9, Pp 166750-166761 (2021)
Point set registration aims to find a spatial transformation that best aligns two point sets. Algorithms which can handle partial overlap and are invariant to the corresponding transformations are particularly desirable. To this end, we first reduce
Externí odkaz:
https://doaj.org/article/dc8e8cdcb89a4807a58b69c4ca302ba1
Publikováno v:
Neural Computing and Applications. 33:11339-11353
Time series modelling is gaining spectacular popularity in the prediction process of decision making, with applications including real-world management and engineering. However, for short time series, prediction has to face unavoidable limitation for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3ce9aa33c864025df85208492797a37
https://doi.org/10.1007/s00521-020-05658-0
https://doi.org/10.1007/s00521-020-05658-0
Publikováno v:
Expert Systems with Applications. 213:119246
Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd1cea1b980f1d268bc10a8dacfcdee9
https://doi.org/10.1016/j.eswa.2022.119246
https://doi.org/10.1016/j.eswa.2022.119246
Publikováno v:
Energy. 166:598-609
Short-term electrical load forecasting is an important part in the management of electrical power because electrical load is an extreme, complex non-linear system. To obtain parameter values that provide better performances with high precision, this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d9f78df5a6bcdf9fae4e9c7b1227f5d
https://doi.org/10.1016/j.energy.2018.10.076
https://doi.org/10.1016/j.energy.2018.10.076
Publikováno v:
CISP-BMEI
The slime mould algorithm (SMA) is a recently developed meta-heuristic optimization algorithm which is based on the oscillation mode of slime mould in nature. However, the SMA is often trapped in local optima for global continuous optimization proble
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e52d01d23f30666d0cfbbde71d154d9
https://doi.org/10.1109/cisp-bmei51763.2020.9263669
https://doi.org/10.1109/cisp-bmei51763.2020.9263669
Autor:
Cui, Zhesen1 (AUTHOR), Ding, Zhe2 (AUTHOR) zhe.ding@hdr.qut.edu.au, Xu, Jing3 (AUTHOR), Zhang, Shaotong4 (AUTHOR), Wu, Jinran5 (AUTHOR), Lian, Wei1 (AUTHOR)
Publikováno v:
Scientific Reports. 6/13/2024, Vol. 14 Issue 1, p1-16. 16p.