Zobrazeno 1 - 10
of 322
pro vyhledávání: '"long short-term memory network (LSTM)"'
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 5, Pp 1424-1440 (2024)
Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and sin
Externí odkaz:
https://doaj.org/article/776bd881aff54fcbb0a3ba3db7ef914c
Autor:
Stella Roussou, Eva Michelaraki, Christos Katrakazas, Amir Pooyan Afghari, Christelle Al Haddad, Md Rakibul Alam, Constantinos Antoniou, Eleonora Papadimitriou, Tom Brijs, George Yannis
Publikováno v:
European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024)
Abstract The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to asse
Externí odkaz:
https://doaj.org/article/69b1b5004a5742908e65a1d3b5782965
Autor:
Xiaoli Zhou, Chao Bei
Publikováno v:
PeerJ Computer Science, Vol 10, p e2192 (2024)
Background For space object detection tasks, conventional optical cameras face various application challenges, including backlight issues and dim light conditions. As a novel optical camera, the event camera has the advantages of high temporal resolu
Externí odkaz:
https://doaj.org/article/99d5f7d0c1ef4e988246aca8386dbe30
Publikováno v:
IEEE Access, Vol 12, Pp 100544-100558 (2024)
Sensor fault classification and reconstruction frameworks are crucial for the stable, safe, and reliable operations of Structural Health Monitoring (SHM) systems. Existing literature addressing reliability and efficiency is confronted with several ch
Externí odkaz:
https://doaj.org/article/1ccc8a79421048f7b096ed98dc5d179a
Publikováno v:
Journal of Hydroinformatics, Vol 26, Iss 1, Pp 255-283 (2024)
Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-
Externí odkaz:
https://doaj.org/article/b9fe51c75912485b8d513b419995d2ee
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 219-230 (2023)
As the most critical equipment for electricity transmission, power transformers (PTs) generally have irreversible insulating degradation, which makes them susceptible to aging breakdowns. Predicting the aging-related failure probability (AFP) of PTs
Externí odkaz:
https://doaj.org/article/b14bc7e3cc14441ba2c20d12e83afae6
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
IntroductionAn accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network l
Externí odkaz:
https://doaj.org/article/90567d854b004e9f9f5e19beff4f793f
Autor:
Abhinav Gupta
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 51, Iss , Pp 101607- (2024)
Study region: A total of 461 watersheds across the USA.Study Focus:This study aimed to assess the usefulness of data from donor watersheds to predict streamflow in parent watersheds. For this purpose, Long Short-Term Memory network (LSTM) was used as
Externí odkaz:
https://doaj.org/article/0cd0b636d19641be80633572f38fbbea
Autor:
Zhou Sheng
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery tec
Externí odkaz:
https://doaj.org/article/dd9d0e087a7e45378167a4fa8819479c
Publikováno v:
智慧农业, Vol 5, Iss 1, Pp 34-43 (2023)
Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability.
Externí odkaz:
https://doaj.org/article/cfc6474a83574c2fa8a273cc237c0881