[HTML][HTML] Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles
State of charge (SoC) estimation is critical for the safe and efficient operation of electric
vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN) …
vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN) …
TSANet: Forecasting traffic congestion patterns from aerial videos using graphs and transformers
Forecasting traffic congestion patterns in lane-less traffic scenarios is a complex task
because of the combination of high & irregular vehicle densities, fluctuating speeds, and the …
because of the combination of high & irregular vehicle densities, fluctuating speeds, and the …
[HTML][HTML] Energy consumption prediction in water treatment plants using deep learning with data augmentation
Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role
in meeting stringent effluent quality regulations. Accurate prediction of energy consumption …
in meeting stringent effluent quality regulations. Accurate prediction of energy consumption …
A survey on few-shot learning for remaining useful life prediction
R Mo, H Zhou, H Yin, X Si - Reliability Engineering & System Safety, 2025 - Elsevier
The prediction performance of most data-driven remaining useful life (RUL) prediction
methods relies on sufficient training samples, which is challenging in few-shot scenarios …
methods relies on sufficient training samples, which is challenging in few-shot scenarios …
Time pattern reconstruction for classification of irregularly sampled time series
Abstract Irregularly Sampled Time Series (ISTS) include partially observed feature vectors
caused by the lack of temporal alignment across dimensions and the presence of variable …
caused by the lack of temporal alignment across dimensions and the presence of variable …
TFformer: A time–frequency domain bidirectional sequence-level attention based transformer for interpretable long-term sequence forecasting
Transformer methods have shown strong predictive performance in long-term time series
prediction. However, its attention mechanism destroys temporal dependence and has …
prediction. However, its attention mechanism destroys temporal dependence and has …
[HTML][HTML] Evaluating the role of data enrichment approaches towards rare event analysis in manufacturing
Rare events are occurrences that take place with a significantly lower frequency than more
common, regular events. These events can be categorized into distinct categories, from …
common, regular events. These events can be categorized into distinct categories, from …
MrCAN: Multi-relations aware convolutional attention network for multivariate time series forecasting
J Zhang, Q Dai - Information Sciences, 2023 - Elsevier
Multivariate time series forecasting (MTSF) has gathered extensive attention in various
research areas. Many researchers leverage deep neural networks to explore spatial …
research areas. Many researchers leverage deep neural networks to explore spatial …
[HTML][HTML] A spatial-temporal attention method for the prediction of multi ship time headways using AIS data
Abstract Ship Time Headway (STH) is the time interval between two consecutive ships
arriving in the same water area. It serves as a crucial indicator for visually measuring the …
arriving in the same water area. It serves as a crucial indicator for visually measuring the …
Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence
Background Colorectal cancer (CRC) is the third most prevalent and second most lethal
form of cancer in the world. Consequently, CRC cancer prevalence projections are essential …
form of cancer in the world. Consequently, CRC cancer prevalence projections are essential …