System Identification Using the Signed Cumulative Distribution Transform In Structural Health Monitoring Applications

This paper presents a novel, data-driven approach to identifying partial differential equation (PDE) parameters of a dynamical system in structural health monitoring applications. Specifically, we adopt a mathematical "transport" model of the sensor data that allows us to accurately estimate the model parameters, including those associated with structural damage. This is accomplished by means of a newly-developed transform, the signed cumulative distribution transform (SCDT), which is shown to convert the general, nonlinear parameter estimation problem into a simple linear regression. This approach has the additional practical advantage of requiring no a priori knowledge of the source of the excitation (or, alternatively, the initial conditions). By using training sensor data, we devise a coarse regression procedure to recover different PDE parameters from a single sensor measurement. Numerical experiments show that the proposed regression procedure is capable of detecting and estimating PDE parameters with superior accuracy compared to a number of recently developed "Deep Learning" methods. The Python implementation of the proposed system identification technique is integrated as a part of the software package PyTransKit (https://github.com/rohdelab/PyTransKit).

Radon cumulative distribution transform subspace modeling for image classification

We present a new supervised image classification method for problems where the data at hand conform to certain deformation models applied to unknown prototypes or templates. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method is simple to implement, non-iterative, has no hyper-parameters to tune, it is computationally efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems, especially in a learning with few labels setting. Furthermore, we show improvements with respect to neural network-based methods in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at https://github.com/rohdelab/rcdt_ns_classifier.