Zobrazeno 1 - 10
of 539
pro vyhledávání: '"data decomposition"'
Publikováno v:
Data Technologies and Applications, 2024, Vol. 58, Issue 3, pp. 472-495.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/DTA-07-2023-0377
Autor:
Vikneswari Someetheram, Muhammad Fadhil Marsani, Mohd Shareduwan Mohd Kasihmuddin, Siti Zulaikha Mohd Jamaludin, Mohd. Asyraf Mansor
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 6, Pp 2582-2594 (2024)
As global climates undergo changes, the frequency of water-related disasters rises, leading to significant economic losses and safety hazards. During flood events, river water levels exhibit unpredictable fluctuations, introducing considerable noise
Externí odkaz:
https://doaj.org/article/60f94f949afa4fe8a918f849359296b7
Autor:
Yuting Zhang
Publikováno v:
IET Science, Measurement & Technology, Vol 18, Iss 4, Pp 193-201 (2024)
Abstract This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike sig
Externí odkaz:
https://doaj.org/article/bb6d9662dae94c87a0c7bfaa41d01db8
Autor:
Sabrina Ladouali, Okan Mert Katipoğlu, Mehdi Bahrami, Veysi Kartal, Bachir Sakaa, Nehal Elshaboury, Mehdi Keblouti, Hicham Chaffai, Salem Ali, Chaitanya B. Pande, Ahmed Elbeltagi
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 54, Iss , Pp 101861- (2024)
Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M’sila and M’doukel.Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variation
Externí odkaz:
https://doaj.org/article/c4367aaefeb24021b00ede74e5bbca8e
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 55, Iss 1, Pp 66-72 (2024)
Purposes Users’ abnormal power consumption behaviors need to be distinguished quickly and accurately. Methods An abnormal state detection model is proposed on the basis of smart meter data and data decomposition and random matrix theory, realizing
Externí odkaz:
https://doaj.org/article/d3023aba643b47bb99950be877cbd448
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In this paper, in order to solve the modal aliasing problem of the EMD decomposition algorithm and EEMD decomposition algorithm and optimize the vocal audio data decomposition on the basis of the adaptive VMD algorithm, the EEMD-VMD second-order data
Externí odkaz:
https://doaj.org/article/8894642a4b8c4d1cbb74effc34900330
Publikováno v:
Frontiers in Energy Research, Vol 11 (2024)
Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes dat
Externí odkaz:
https://doaj.org/article/fc251f01aad7431081765f5466d45bc9
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
Fracture modelling is essential for understanding fluid flow in fractured hydrocarbon reservoirs, particularly in the phase of production; however, traditional discrete fracture network (DFN) modelling methods lack constraints that reflect characteri
Externí odkaz:
https://doaj.org/article/9aca378419d744249d3f7757beb37abc
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 14200-14219 (2022)
The development of renewable clean energy wind power is very rapid. However, the strong randomness and weak dispatchability of wind power bring significant challenges to wind power operation and maintenance, such as grid connection, power dispatch, a
Externí odkaz:
https://doaj.org/article/830d743818b0415c84cfe353141c50c7
Publikováno v:
Agriculture, Vol 14, Iss 1, p 2 (2023)
Recently, Synthetic Aperture Radar (SAR) data, especially Sentinel-1 data, have been increasingly used in rice mapping research. However, current studies usually use long time series data as the data source to represent the differences between rice a
Externí odkaz:
https://doaj.org/article/e0d8194aee2f49e291ca0aed6611c027