Zobrazeno 1 - 10
of 2 683
pro vyhledávání: '"Hybrid models"'
Autor:
Wulfran Fendzi Mbasso, Reagan Jean Jacques Molu, Ambe Harrison, Mukesh Pushkarna, Fritz Nguemo Kemdoum, Emmanuel Fendzi Donfack, Pradeep Jangir, Pierre Tiako, Milkias Berhanu Tuka
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive data
Externí odkaz:
https://doaj.org/article/fbe4290568a04443aa031c603b099480
Publikováno v:
Journal of Statistical Theory and Applications (JSTA), Vol 23, Iss 3, Pp 290-314 (2024)
Abstract The estimation of a certain threshold beyond which an extreme value distribution can be fitted to the tail of a data distribution remains one of the main issues in the theory of statistics of extremes. While standard Peak over Threshold (PoT
Externí odkaz:
https://doaj.org/article/6c01af054a2846b2bf58243361736a24
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-39 (2024)
Abstract In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable
Externí odkaz:
https://doaj.org/article/454ecca6808540f196021c9c73e422c2
Publikováno v:
Journal of Materials Research and Technology, Vol 32, Iss , Pp 2767-2779 (2024)
To address the challenges posed by inadequate data and data utilization in multiple scenarios of fatigue loading, a Physics-informed Transfer Learning (PITL) model has been developed to predict the fatigue life of IN718 superalloy. Strain-controlled
Externí odkaz:
https://doaj.org/article/ec9819efaf08439fb0dcd08d28121e4a
Publikováno v:
Proceedings of the Estonian Academy of Sciences, Vol 73, Iss 3, Pp 264-278 (2024)
Mathematical modelling of physical phenomena is based on the laws of physics, but for complicated processes, phenomenological models could enhance the descriptive and prescriptive power of the analysis. This paper describes some hybrid models, where
Externí odkaz:
https://doaj.org/article/8f50221d41bf4386bd3b6d91d462e309
Publikováno v:
Energy and AI, Vol 18, Iss , Pp 100436- (2024)
Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital represen
Externí odkaz:
https://doaj.org/article/6adeba89ad0248869c5aefb50b00b384
Autor:
Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100559- (2024)
In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultu
Externí odkaz:
https://doaj.org/article/a03aabe6ffbf45c59a38db990d091283
Autor:
Senwang Huang, Liping Chen
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 15, Iss 1 (2024)
Landslide susceptibility maps (LSMs) can play a bigger role in promoting the understanding of future landslides. This paper explores and compares the capability of three state-of-the-art bivariate models, namely the frequency ratio (FR), statistical
Externí odkaz:
https://doaj.org/article/cb7e1cc5971c4942869ca07b48c25a4b
Publikováno v:
Heliyon, Vol 10, Iss 21, Pp e38993- (2024)
Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throu
Externí odkaz:
https://doaj.org/article/e49bc6db36c54b54824bf98184fb2a41