Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks

Abstract

This paper presents a new approach to datadriven modeling of isotropic haptic textures using frequencydecomposed neural networks from the contact acceleration data that are captured when a stylus is scanned on a textured surface with diverse scanning velocities and normal forces. We first describe a motorized texture scanner that was developed for accurate and easy data collection under a wide variety of conditions. We then propose two neural network models with different topologies: a unified model that feeds all of acceleration data, scanning velocity, and normal force as input variables to a single large neural network and a decomposed model that consists of a number of smaller neural networks each of which is trained with the acceleration data for a pair of scanning velocity and normal force. An experiment with real samples showed that the unified model has better cross-validation ability in terms of spectral rms errors and its performance is comparable to the best available in the literature. We also present some preliminary results of anisotropic texture modeling achieved by extending the unified model.

A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring

“…Data-driven modeling describes the inherent relationships between input and output variables without the need to understand all the mechanism details [2, [15][16][17][18]. In recent years, data-driven modeling methods, such as partial least squares (PLS) [19], multiple linear regression (MLR) [20] and artificial neural networks (ANNs) [21], have been successfully used for industrial processes monitoring. However, data-driven modeling of more complex industrial processes implies more requirements of training samples and training time.…”