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An Inverse Neural Network Model for Data-Driven Texture Rendering on Electrovibration Display
Reza Haghighi Osgouei, Sunghwan Shin, Seongwon Cho, Jin Ryong Kim, and Seungmoon Choi
(POSTECH, South Korea; Electronics and Telecommunications Research Institute, South Korea)
Demo 9
Publisher's Version
Picture (Local)
Abstract: We propose a data-driven method for realistic texture rendering on an electrovibration display. To compensate the nonlinear dynamics of an electrovibration display, we use nonlinear autoregressive with external input (NARX) neural networks as an inverse dynamics model of an electrovibration display. The neural networks are trained with lateral forces resulting from actuating the display with a pseudo-random binary signal (PRBS). The lateral forces collected from the textured surface with various scanning velocities and normal forces are fed into the neural network to generate the actuation signal for the display. The generated signal is interpolated by user’s scanning velocities and normal forces measured by an infrared-ray frame and a load cell in real time.


Time stamp: 2019-03-26T22:14:57+01:00