Automatic Identification Technology of Optical Fiber based on Genetic Neural Network Algorithm

被引:1
作者
Cui, Qi [1 ]
Yin, Shiya [1 ]
机构
[1] Hanjiang Normal Univ, Sch Math & Comp Sci, Shiyan 442000, Hubei, Peoples R China
来源
2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) | 2022年
关键词
Genetic Algorithm; Neural Network Algorithm; Optical Fiber; Automatic Recognition Technology;
D O I
10.1109/IAEAC54830.2022.9929915
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of image recognition technology, many manufacturers design and manufacture different fiber cleaning machines. Using the advantages of neural network(NN) in pattern recognition, this paper proposes a genetic neural network algorithm(NNA) combining NN and genetic algorithm to study the automatic optical fiber recognition technology. Optical fiber image recognition and image preprocessing are discussed, and the hardware implementation of optical fiber automatic recognition system is studied; The Gabor extracted image texture features are analyzed, and the simulation test of optical fiber image recognition is carried out according to the Gabor extracted feature training. The test results verify the accuracy of the optical fiber automatic recognition technology proposed in this paper.
引用
收藏
页码:2014 / 2020
页数:7
相关论文
共 12 条
  • [1] Real-time Monitoring of Subsea Gas Pipelines, Offshore Platforms, and Ship Inspection Scores Using an Automatic Identification System
    Artana K.B.
    Pitana T.
    Dinariyana D.P.
    Ariana M.
    Kristianto D.
    Pratiwi E.
    [J]. Journal of Marine Science and Application, 2018, 17 (1) : 101 - 111
  • [2] Cahyani N, 2021, INDONESIAN J STAT IT, V5, P396
  • [3] Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem
    Davoudi, Khatereh
    Thulasiraman, Parimala
    [J]. SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, 2021, 97 (08): : 511 - 527
  • [4] Extraction of low-dimensional features for single-channel common lung sound classification
    Engin, M. Alptekin
    Aras, Selim
    Gangal, Ali
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2022, 60 (06) : 1555 - 1568
  • [5] Et t., 2021, TURKISH J COMPUTER M, V12, P3216
  • [6] Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model
    Gadde, Sridevi
    Charkravarthy, A. S. N.
    Satyanarayana, S.
    Murali, M.
    [J]. SOFT COMPUTING, 2022, 26 (11) : 5399 - 5408
  • [7] Test Signal Planning for Identifying the Aerodynamic Characteristics of Automatically Controlled Aircraft Taking into Account the Uncertainty of A Priori Data
    Grigor'ev, N. V.
    [J]. AUTOMATION AND REMOTE CONTROL, 2022, 83 (04) : 600 - 612
  • [8] Loghmanian S., 2018, INT J COMPUTATIONAL, V13, P27
  • [9] Osman O, 2020, IEEE SENS J, VPP, P1
  • [10] An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network
    Pattanaik, Debasish
    Mishra, Sanhita
    Khuntia, Ganesh Prasad
    Dash, Ritesh
    Swain, Sarat Chandra
    [J]. OPEN ENGINEERING, 2020, 10 (01): : 630 - 641