Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

Abstract

A visual mechanical signal detection and loading platform with super-resolution based on deep learning is designed to improve the detection accuracy of mechanical signals. The visual mechanical signal detection and loading platform with super-resolution include three-dimensional (3D) biological force quantitative detection platform and the mechanical signal loading platform with 3D magnetic distortion and ultrahigh resolution. In the 3D biological force quantitative detection platform, four 3D force sensors are used to collect mechanical signals, and the improved fuzzy clustering fusion method is used to fuse the mechanical signals collected by 3D force sensors to improve the detection accuracy of mechanical signals. The mechanical signal loading platform of 3D magnetic distortion and ultrahigh resolution technology connects the 3D magnetic distortion instrument and microscope, collects images through high-speed scanning components and distorted magnetic field, reconstructs the collected images by deep learning method, obtains ultrahigh-resolution mechanical signal visual images, and triggers mechanical signal loading and release by synchronous interactive system. The consequences of the experiment demonstrate that the designed platform can display the super-

The data flow risk monitoring system of the expressway networking system based on deep learning

“…The expressway networking system data flow risk monitoring index data s j { }( = 1, 2, …, N) j after preprocessing such as scale transformation and time slicing is input into the input layer of the deep convolution neural network. 24 Hidden layer: this layer is an important part of feature extraction and feature mapping in the deep convolution neural network. It includes two important operations, namely convolution and pooling.…”

“…Input layer: this layer is used for data input. The expressway networking system data flow risk monitoring index data $$\{{s}_{j}\}(j=1,2,\ldots ,\bar{{\rm{N}}})$$ after preprocessing such as scale transformation and time slicing is input into the input layer of the deep convolution neural network 24 …”