Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1844-1849.doi: 10.13229/j.cnki.jdxbgxb20190721

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Automatic defect detection for virtual network perceptual data based on machine learning

Wei FANG1,2(),Yi HUANG1,3(),Xin-qiang MA1,3   

  1. 1.Institute of Intelligent Systems and Control, Zhejiang University, Hangzhou 310027, China
    2.Department of Computer Science and Technology, Huaibei Vocational and Technical College, Huaibei 235000, China
    3.Institute of Intelligent Computing and Visualization Based on Big Data, Chongqing University of Arts and Sciences, Chongqing 402160, China
  • Received:2019-07-18 Online:2020-09-01 Published:2020-09-16
  • Contact: Yi HUANG E-mail:fangwei653241@163.com;fanjigong8975@163.com

Abstract:

To solve the problem that the intersection and parallelism ratio (IOU) of two defect-aware data detection algorithms based on data mining and pattern matching is smaller, which leads to poor detection effect, a new automatic defect detection algorithm for virtual network sensing data is proposed on the basis of machine learning. First, the perceptual data is acquired and processed as the learning samples. Then, a BP neural network model is constructed and trained. Finally, the learning samples are used as the input of the trained BP neural network model to distinguish the defect data from normal data to realize automatic defect detection of perceptual data. The results show that the IOU of machine learning-based automatic defect detection algorithm for virtual network sensing data is 0.9588, which is closer to 1 than that of data mining-based and pattern matching-based defect sensing data detection algorithms, which shows that the detection effect of this algorithm is better.

Key words: machine learning, virtual network, perceived data, automatic defect detection algorithm

CLC Number: 

  • TP311.26

Fig.1

Composition of RFID reader"

Fig.2

Machine learning classification"

Fig.3

BP neural network model"

Fig.4

Experimental test platform"

Table 1

Experimental test environment"

名称说明
硬件环境处理器2 GHz Intel Core i5
内存8 GB
软件环境操作系统macOS Sierra 10.12.6
实验工具Python2.7
Numpy1.11.2
Scikit-learn 0.019.0
Matplotlib 2.0.2

Fig.5

Intersection-parallel ratio"

Table 2

Intersection-parallel ratio of three algorithms"

算法IOU
基于机器学习的虚拟网络感知数据缺陷自动检测0.9588
基于数据挖掘的缺陷感知数据检测0.7456
基于模式匹配的缺陷感知数据检测0.8145
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