3 papers
cs.LG2025
TS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning
Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu
Existing data-driven methods rely on the extraction of static features from time series to approximate the material removal rate (MRR) of semiconductor manufacturing processes such…
cs.LG2025
Incremental Multistep Forecasting of Battery Degradation Using Pseudo Targets
Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu
Data-driven models accurately perform early battery prognosis to prevent equipment failure and further safety hazards. Most existing machine learning (ML) models work in offline mo…
cs.LG2025
Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation
Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu
Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over…