Results 31 to 40 of about 873,018 (176)

A Multi-Head Attention ResCNN–BiGRU Model for Robust SOC Estimation in EVs Lithium-Ion Batteries Using Real-World Driving Data

IEEE Access
Precise assessment of the state of charge (SOC) is essential for ensuring the safety, efficiency, and dependability of electric vehicles (EVs). However, achieving high accuracy remains challenging due to the complex nonlinear characteristics of lithium ...
N. Vigneswar, R. Manivannan, Chala Merga Abdissa   +2 more
doaj   +1 more source

From Dictionaries to Deep Learning: A Systematic Mapping Review of the Natural Language Processing Tasks Used to Analyze Sustainability Reports

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study aims at shedding light on the vast landscape of natural language processing (NLP) tasks used when analyzing sustainability reports or sustainability within integrated annual reports. A systematic literature review is carried out, identifying 160 studies of relevance.
Hannes Cordes
wiley   +1 more source

Improved Bi-GRU for parkinson’s disease severity analysis [PDF]

Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities.
Arunachalam, Malathi, Gandhi, Vaibhav, Ramar, Ramalakshmi, Ananthan, Bhuvanesh   +3 more
core   +3 more sources

Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy ...
Wei Liu, Zihui Lin, Yuan Hu, Aodong Tian, Xintai Yuan, Jens Wickert   +5 more
doaj   +1 more source

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

Research on explainable BiGRU deep learning framework for short term load forecasting in smart power systems

Discover Artificial Intelligence
This study proposes a novel forecasting framework based on a parallel multi-input Bi-GRU architecture combined with a sliding window-based Multi-Input Multi-Output (MIMO) prediction strategy.
Zhiwei Wang, Mengzhou Xu, Jingchang Hao, Wei Li, Shiyao Cheng   +4 more
doaj   +1 more source

A hybrid Bi-GRU–bi-LSTM framework for multivariate climate finance forecasting: Application to the most vulnerable countries

Array
Accurately forecasting climate finance flows is essential for guiding equitable resource allocation to the most vulnerable countries under the Paris Agreement. This study proposes a novel hybrid deep learning framework that integrates Bidirectional Gated
Amna Farooqui Arsalan, Falak Khan
doaj   +1 more source

A Probabilistic Fractional Order Physics Informed Mamba Kolmogorov‐Arnold Network for State‐of‐Charge Estimation in Grid‐Connected Battery Energy Storage Systems

Energy Science &Engineering, EarlyView.
This paper introduces the probabilistic fractional‐order Mam‐KAN (PFO‐Mam‐KAN) controller, a physics‐informed gray‐box framework for real‐time battery state‐of‐charge estimation. By unifying efficient Mamba encoders with uncertainty‐aware fractional physics, it achieves superior 0.31% RMSE accuracy and robust grid‐support operation under dynamic ...
Arun Kumar Rawat, Subhash Chandra, Vinay Kumar Deolia   +2 more
wiley   +1 more source

GRU-BERT for NILM: A Hybrid Deep Learning Architecture for Load Disaggregation

AI
Non-Intrusive Load Monitoring (NILM) aims to disaggregate a household’s total aggregated power consumption into appliance-level usage, enabling intelligent energy management without the need for intrusive metering.
Annysha Huzzat, Ahmed S. Khwaja, Ali A. Alnoman, Bhagawat Adhikari, Alagan Anpalagan, Isaac Woungang   +5 more
doaj   +1 more source

Multi‐command wearable magnetic human‐machine interface enabled by low‐noise sensing and time‐series deep learning

InfoScience, EarlyView.
This study integrates a wrist‐worn sensing platform based on a low‐noise planar Hall magnetoresistive sensor with time‐series deep learning to enable long‐range, single‐sensor, multi‐command gesture recognition with high accuracy. Further evaluations show reliable generalization across different days, previously unseen users, wearing‐position ...
Guannan Mu, Proloy Taran Das, Jan Schmidtpeter, Andreas Knüpfer, Rui Xu, Denys Makarov   +5 more
wiley   +1 more source