Results 251 to 260 of about 11,734,216 (296)

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

Self-Concentration Detection Based on Doubled Amplitude/Phase Processing in Node PDE Modular Models. [PDF]

Biomimetics (Basel)
Zjavka L.
europepmc   +1 more source

Enhancing multivariate quality monitoring: Integrating LSTM and MLS-SVR with Max-MCUSUM frameworks. [PDF]

MethodsX
Ahsan M, Hakim MRR, Hidayatullah SBS, Lee MH.   +3 more
europepmc   +1 more source

Automated Vision-Based Sensing of Paediatric Pain Using 2D-Facial Landmark Trajectories Derived from 3D Geometric Normalisation and a Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) Network. [PDF]

Sensors (Basel)
Fabila T, Du T, Tan CW, Sultana R, Bong CL, Sng BL.   +5 more
europepmc   +1 more source

Research on LSTM-based spatial target trajectory forecasting enhanced by attention mechanisms. [PDF]

PLoS One
Luo Q, Ji J, Yang T, Xu Y, Yao Y.
europepmc   +1 more source

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e-LSTM: An Efficient Hardware Architecture for Long Short-Term Memory

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019
Long Short-Term Memory (LSTM) and its variants have been widely adopted in many sequential learning tasks, such as speech recognition and machine translation. Significant accuracy improvements can be achieved using complex LSTM model with a large memory requirement and high computational complexity, which is time-consuming and energy demanding. The low-
Zhisheng Wang, Meiqi Wang, Jinming Lu
exaly   +3 more sources

Evolutionary Design of Long Short Term Memory (LSTM) Ensemble

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Long Short Term Memory (LSTM) based Recurrent Neural Network is an effective deep learning model with a wide variety of applications. Despite numerous recent success, using a single LSTM network cannot achieve reliable performance on complicated machine learning tasks.
Ramya Anasseriyil Viswambaran, Gang Chen 0002, Bing Xue 0001, Mohammad Nekooei   +3 more
openaire   +1 more source

PI-LSTM: Physics-Infused Long Short-Term Memory Network

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019
We introduce a novel machine learning-based fusion model termed as PI-LSTM (Physics-Infused Long Short-Term Memory Networks) that integrates first principle Physics-Based Models and Long Short-Term Memory (LSTM) network. Our architecture aims at combining equation-based models with data-driven machine learning models to enable accurate predictions of ...
Shubhendu Kumar Singh, Ruoyu Yang, Amir Behjat, Rahul Rai, Souma Chowdhury, Ion Matei   +5 more
openaire   +2 more sources