吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (5): 1844-1849.doi: 10.13229/j.cnki.jdxbgxb20190721

• 计算机科学与技术 • 上一篇    

基于机器学习的虚拟网络感知数据缺陷自动检测

方伟1,2(),黄羿1,3(),马新强1,3   

  1. 1.浙江大学 智能系统与控制研究所，杭州 310027
    2.淮北职业技术学院 计算机科学技术系，安徽 淮北 235000
    3.重庆文理学院 大数据智能计算与可视化研究所，重庆 402160
  • 收稿日期:2019-07-18 出版日期:2020-09-01 发布日期:2020-09-16
  • 通讯作者: 黄羿 E-mail:fangwei653241@163.com;fanjigong8975@163.com
  • 作者简介:方伟（1979-），男，副教授，硕士.研究方向：机器学习,网络工程.E-mail:fangwei653241@163.com
  • 基金资助:
    安徽省高校自然科学研究重点项目(KJ2018A0713);安徽高校优秀青年骨干人才国内访问研修项目(gxgnfx2018108)

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

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摘要：

针对基于数据挖掘以及模式匹配的两种缺陷感知数据检测算法交并比（IOU）小，导致检测效果差的问题，在机器学习的基础上研究一种新的虚拟网络感知数据缺陷自动检测算法。该算法主要分为两个阶段内容：前一阶段采集和处理感知数据，准备好后续BP神经网络模型的学习样本；后一阶段构建和训练神经网络模型，并将前一阶段得到的学习样本作为输入数据输入到训练好的BP神经网络模型当中，实现缺陷数据与正常数据的区分，完成感知数据缺陷自动检测。结果表明：基于机器学习的虚拟网络感知数据缺陷自动检测算法交并比（IOU）为0.9588，与基于数据挖掘以及模式匹配的两种缺陷感知数据检测算法相比，更接近1，说明本文算法检测效果更好。

关键词: 机器学习, 虚拟网络, 感知数据, 缺陷自动检测算法

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

中图分类号: 

  • TP311.26

图1

RFID阅读器组成结构"

图2

机器学习分类"

图3

BP神经网络模型"

图4

实验测试平台"

表1

实验测试环境"

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

图5

交并比"

表2

三种算法的交并比"

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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