Lever Mechanism for Diaphragm-Type Vibrators to Enhance Vibrotactile Intensity.

Thin and light vibrators that leverage the inverse piezoelectric effect with a diaphragm mechanism are promising vibrotactile actuators owing to their form factors and high temporal and frequency response. However, generating perceptually sufficient displacement in the low-frequency domain is challenging. This study presents a lever mechanism mounted on a diaphragm vibrator to enhance the vibrotactile intensity of low-frequency vibrotactile stimuli. The lever mechanism is inspired by the tactile contact lens consisting of an array of cylinders held against the skin on a sheet that enhances micro-bump tactile detection. We built an experimental apparatus including our previously developed thin-film diaphragm-type vibrator, which reproduced the common characteristic of piezoelectric vibrators: near-threshold displacement (10 to 20 µm) at low frequency. Experiments demonstrated enhanced vibrotactile intensity at frequencies less than 100 Hz with the lever mechanism. Although the arrangement and material of the mechanism can be improved, our findings can help improve the expressiveness of diaphragm-type vibrators.

Role of Layers and Neurons in Deep Learning With the Rectified Linear Unit.

Deep learning is used to classify data into several groups based on nonlinear curved surfaces. In this paper, we focus on the theoretical analysis of deep learning using the rectified linear unit (ReLU) activation function. Because layers approximate a nonlinear curved surface, increasing the number of layers improves the approximation accuracy of the curved surface. While neurons perform a layer-by-layer approximation of the most appropriate hyperplanes, increasing their number cannot improve the results obtained via canonical correlation analysis (CCA). These results illustrate the functions of layers and neurons in deep learning with ReLU.