Research on software credibility algorithm based on deep convolutional sparse coding

Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the algorithm from two aspects: factor processing based on deep convolution neural network and trust classification based on sparse representation.

A New Image Classification System Using Deep Convolution Neural Network And Modified Amsgrad Optimizer

The Journal of The University of Duhok ◽

10.26682/sjuod.2019.22.2.10 ◽

2019 ◽

Vol 22 (2) ◽

pp. 89-101

Author(s):

Arman Mohammed ◽

◽

Ahmed Tahir ◽

Keyword(s):

Neural Network ◽

Image Classification ◽

Classification System ◽

Automatic Fruit Detection System using Multilayer Deep Convolution Neural Network

2021 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci50826.2021.9402513 ◽

2021 ◽

Author(s):

R.S. Latha ◽

G.R. Sreekanth ◽

R.C. Suganthe ◽

M. Geetha ◽

N. Swathi ◽

...

Keyword(s):

Neural Network ◽

Detection System ◽

Recognition of Rock Micro-fracture Signal Based on Deep Convolution Neural Network Inception Algorithm

IEEE Access ◽

10.1109/access.2021.3086630 ◽

2021 ◽

pp. 1-1

Author(s):

Guili Peng ◽

Xianguo Tuo ◽

Tong Shen ◽

Jing Lu

Keyword(s):

Neural Network ◽

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201965 ◽

2020 ◽

pp. 1-9

Author(s):

Yiming Guo ◽

Hui Zhang ◽

Zhijie Xia ◽

Chang Dong ◽

Zhisheng Zhang ◽

...

Keyword(s):

Neural Network ◽

Degradation Process ◽

Rolling Bearing ◽

Remaining Useful Life ◽

Dual Channel ◽

Time Domain Data ◽

Channel Input ◽

Deep Convolution Neural Network ◽

Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.