Akash R — PhD Scholar, Department of Electrical Engineering, Indian Institute of Technology Madras. Machine learning, laser-induced breakdown spectroscopy, and optical sensing for condition monitoring of high-voltage insulation and energy systems.
From laser plasmas to vision transformers — non-destructive, data-driven monitoring of critical electrical infrastructure.
Intelligent diagnostics for outdoor insulators in transmission and distribution networks — ML, deep learning, computer vision, LIBS, recurrence plots, fluorescent fiber sensing, and transformer architectures for hydrophobicity, pollution, aging, and surface tracking.
Image-based hydrophobicity classification with a Hybrid Swin Transformer capturing local and global dependencies in water-droplet patterns — robustness under FGSM and PGD attacks, hardened by adversarial training.
Non-destructive monitoring of insulator pollution and silicone rubber aging via laser-induced breakdown spectroscopy — spectral-image conversion, corner detection, GLCM features, CNN classification, and LVQ networks.
Non-contact optical methods for early tracking detection in silicone rubber insulators — fluorescent fiber sensing, optical emission spectroscopy, Gramian Angular Fields, CNN classification, and t-SNE visualization.
ML applied to broader energy problems: long-term wind speed forecasting, LIBS–EIS soil-moisture estimation in grounding systems, and deep-learning prediction of electrostatic charging tendency in ester nanofluids.
Experimental HV testing meets interpretable AI: optical sensing, spectroscopy, LIBS, EIS, image and signal processing, feature extraction, deep learning, and explainability — designed for deployment, not just benchmarks.
Peer-reviewed journal and conference papers. Filter by area or year.
A non-contact system and method for detecting surface tracking degradation in high-voltage polymeric insulators — enabling early intervention before irreversible failure. Protected for a 20-year term from filing.
IEEE 4th International Conference on Smart Technologies for Power, Energy and Control · with IIT Madras.
Marine Technology Society — for "Machine Learning based Fault Detection in Wind Energy Conversion" at OCEANS Conference 2022.
Dept. of EEE, Karunya University — best outgoing student of the 2016–2020 batch.
From HoD, EEE, KITS — for handling sessions in a two-day hands-on Python training program.
Second place at Mindkraft, a national-level techno-management fest, KITS.
From HoD, EEE, KITS — for organizing the department symposium as Association President.
From the Vice Chancellor, KITS — for dedicated service in a national technical fest.
Green Energy Club, Dept. of EEE, Karunya University.
Polymeric-insulator monitoring: pollution diagnosis, hydrophobicity monitoring, and tracking-detection research.
LIBS–EIS-based soil-moisture estimation for grounding systems.
Open to research collaboration, academic discussion, and industry engagement in AI-driven monitoring of electrical infrastructure.