Publications
Development of a ML model for Damage Detection, Localization & Quant. to extend Structure Life [Link]
Mar 10, 2021 Elsevier B.V.
- This paper proposes the use of a physical structure’s Transmissibility functions as input to a novel composite architecture consisting of Deep CNN followed by multivariate linear regressors to detect, localize, and quantify the damage extent in a system.
Patents
- Train a plurality of models while capturing the quantization spectrum introduced by video codec
- Changes in video codec using these models to tackle every quantization band separately
- Train a model with domain-shifted feature-augmented training to overcome local nature of convolution
- Create a detachable model such that we use a much less complex model at the time of inference
- Train a plurality of models while capturing the quantization spectrum introduced by video codec
- Changes in video codec using these models to tackle every quantization band separately
- Train a model with domain-shifted feature-augmented training to overcome local nature of convolution
- Create a detachable model such that we use a much less complex model at the time of inference
- Capturing and fixing domain disparity in online video codec encoder operation
- Prevent model re-trainings earlier required to capture the infinite variation in video data
- Allow user choice between compression and performance by incorporating our solution in Rate-Distortion
- Identifying state-based data changes and variation in video compression pipeline
- Curation of AI-model training data by going beyond the current state, hence, training much more robust models
- Isolation of continual error-propagation through the video compression pipeline
- Removal/mitigation of temporally introduced distortions from training data generated through video codec for AI-models