Zobrazeno 1 - 10
of 325
pro vyhledávání: '"short‐term memory neural networks"'
Autor:
Aleksa Cuk, Timea Bezdan, Luka Jovanovic, Milos Antonijevic, Milos Stankovic, Vladimir Simic, Miodrag Zivkovic, Nebojsa Bacanin
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease
Externí odkaz:
https://doaj.org/article/00eb5b7cde4841f1be1f9b7cdeab651b
Publikováno v:
IEEE Access, Vol 12, Pp 73451-73469 (2024)
Wind power, as a pivotal technology in the fight against climate change and the advancement of sustainable energy, occupies an essential role in the global shift towards a new energy paradigm. However, the inherent fluctuation and episodic nature of
Externí odkaz:
https://doaj.org/article/9a8c89b17aab4df6b92fc0fee1e4b174
Publikováno v:
IEEE Access, Vol 12, Pp 56774-56788 (2024)
Efficient and accurate short-term electric load forecasting plays a significant role in energy conservation and reducing carbon emissions. Recurrent neural networks (RNN) and their derived deep learning models have continuously improved the accuracy
Externí odkaz:
https://doaj.org/article/9f71f6d0f1c04aaeae3d3a0ec374ce07
Publikováno v:
Water, Vol 16, Iss 17, p 2452 (2024)
The fluctuation of the tide is closely related to the production and life of people in coastal areas, and the change in the tide level will have a significant impact on the safety of infrastructure, ship travel, ecological environment, and other issu
Externí odkaz:
https://doaj.org/article/e6511b1799584d94ab336a470a612959
Publikováno v:
Buildings, Vol 14, Iss 7, p 2244 (2024)
In tunnel construction, the prediction of the surrounding rock deformation is related to the construction safety and stability of the tunnel structure. In order to achieve an accurate prediction of the surrounding rock deformation in soft rock tunnel
Externí odkaz:
https://doaj.org/article/6ca38a4f68cb4194b6cb9f2642950bea
Publikováno v:
Applied Sciences, Vol 14, Iss 13, p 5905 (2024)
Inertial navigation systems experience error accumulation over time, leading to the use of integrated navigation as a classical solution to mitigate inertial drift. This provides a novel approach to navigation and positioning by using the combined ad
Externí odkaz:
https://doaj.org/article/b7336e7cbe794b95a2b4c89410771696
Publikováno v:
Frontiers in Energy Research, Vol 11 (2024)
This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic mapping incorporates the standard WOA and modifies its internal convergence fac
Externí odkaz:
https://doaj.org/article/b13ec563327b4822a1b7b385cf5ea953
Publikováno v:
PeerJ Computer Science, Vol 9, p e1569 (2023)
Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, th
Externí odkaz:
https://doaj.org/article/121c8d47ed674b47a65aa366081bd343
Publikováno v:
IEEE Access, Vol 11, Pp 83867-83880 (2023)
Coal mine roof accidents pose a significant threat to the safety of personnel and equipment. To mitigate these risks, this study presents an improved time-series prediction model, called Nadam-LSTM, for mining pressure data at the working face in coa
Externí odkaz:
https://doaj.org/article/01ae6533c52944f0a4184c22fa4ae94d
Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data
Publikováno v:
Energy Informatics, Vol 5, Iss S1, Pp 1-21 (2022)
Abstract Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to
Externí odkaz:
https://doaj.org/article/87cc8104e7044e5bb41ffe5cc5f44d8b