Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

Abstract

It is difficult to combine human sensory cognition with quality detection to form a pattern recognition system based on human perception. In the future, miniature stepper motor modules will be widely used in advanced intelligent equipment. However, the reducer module based on powder metallurgy parts and the stepper motor may have various defects during operation, with varying definitions of those that affect the user comfort. It is tremendously important to develop an intelligent system to effectively simulate human senses. In this work, an elaborated personification of the perceptual system is proposed to simulate the ventral and flow of the human perception system: two branch systems consisting of a spatiotemporal convolutional neural network (S-CNN) and a concatenated HoppingNet temporal convolutional neural network (T-CNN). To ensure high robustness of the system, we combined principal component analysis (PCA) with the opinions of an experienced quality control (QC) team members to screen the data, and used a bionic ear to simulate human perception characteristics. After repeated comparisons of the tester, the results show that our anthropoid pattern sensing system has high accuracy and robustness for a stepper motor module.

Emerging Paradigms and Architectures for Industry 4.0 Applications

“…Other relevant Industry 4.0 technologies were studied in [8][9][10]. In [8], the authors analyze the problem of how to monitor and control additive manufacturing processes in real-time.…”

“…The accuracy of such a model is verified through a prototype, whose AC/DC converter architecture was optimized in terms of parameters such as efficiency, voltage levels, operation frequency, duty cycle, or load. With respect to [10], a perceptual system is proposed to simulate the ventral flow of the human perception system. The main objective of the proposed solution is to simulate human senses that can be later used to estimate user comfort.…”