Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Hydro-environment Research"
DOI: 10.1016/j.jher.2021.01.006
Abstract: Abstract Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a… read more here.
Keywords: water depth; water; neural networks; soft sensing ... See more keywords
Long-term gear life prediction based on ordered neurons LSTM neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Measurement"
DOI: 10.1016/j.measurement.2020.108205
Abstract: Abstract Gear failure may affect the operation of mechanical equipment, and even cause the catastrophic break of machine and even casualties. Thus, the remaining useful life (RUL) estimation of the gear has important significance. This… read more here.
Keywords: term; lstm neural; long term; based ordered ... See more keywords
Training LSTM-neural networks on early warning signals of declining cooperation in simulated repeated public good games
Sign Up to like & getrecommendations! Published in 2020 at "MethodsX"
DOI: 10.1016/j.mex.2020.100920
Abstract: We present results of attempts to expand and enhance the predictive power of Early Warning Signals (EWS) for Critical Transitions (Scheffer et al. 2009) through the deployment of a Long-Short-Term-Memory (LSTM) Neural Network on agent-based… read more here.
Keywords: warning signals; cooperation; lstm neural; early warning ... See more keywords
Realtime prediction of dynamic mooring lines responses with LSTM neural network model
Sign Up to like & getrecommendations! Published in 2020 at "Ocean Engineering"
DOI: 10.1016/j.oceaneng.2020.108368
Abstract: Abstract A Long Short-Term Memory (LSTM) neural network model is developed to provide a real-time calculation tool for monitoring the mooring line responses under the operating condition by using the vessel motion as the only… read more here.
Keywords: lstm neural; neural network; network model; prediction ... See more keywords
PM2.5 Forecast Based on a Multiple Attention Long Short-Term Memory (MAT-LSTM) Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "Analytical Letters"
DOI: 10.1080/00032719.2020.1788050
Abstract: Abstract Air pollution, especially by particulate matter with diameters less than 2.5 μm (PM2.5), is a serious threat to public health. The accurate prediction of PM2.5 concentration is significant for air pollution control and the prevention… read more here.
Keywords: lstm neural; mat lstm; attention; model ... See more keywords
Brain model state space reconstruction using an LSTM neural network
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/acd871
Abstract: Objective. Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions… read more here.
Keywords: neural network; state; filter; brain ... See more keywords
Sequential Fault Diagnosis Based on LSTM Neural Network
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2794765
Abstract: Fault diagnosis of chemical process data becomes one of the most important directions in research and practice. Conventional fault diagnosis and classification methods first extract features from the raw process data. Then certain classifiers are… read more here.
Keywords: diagnosis; lstm neural; process; fault diagnosis ... See more keywords
Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2995044
Abstract: Forecasting the short-term metro ridership is an important issue for operation management of metro systems. However, it cannot be solved well by the single long short-term memory (LSTM) neural network alone for the irregular fluctuation… read more here.
Keywords: short term; term metro; lstm neural; metro ridership ... See more keywords
LSTM Neural Networks With Attention Mechanisms for Accelerated Prediction of Charge Density at Onset Condition of DC Corona Discharge
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3222269
Abstract: The onset process of corona discharge is naturally nonlinear and dynamic. The conventionally physical-based onset model and numerical computation of onset charge distribution are hampered by the computational power and given time. Here, in order… read more here.
Keywords: neural networks; charge; lstm neural; discharge ... See more keywords
Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm
Sign Up to like & getrecommendations! Published in 2019 at "Mathematical Problems in Engineering"
DOI: 10.1155/2019/1934796
Abstract: Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for… read more here.
Keywords: lstm neural; improved pso; nickel price; model ... See more keywords
A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model
Sign Up to like & getrecommendations! Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/1643413
Abstract: As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency… read more here.
Keywords: neural network; bitcoin gold; prediction; model ... See more keywords