A high precision recognition method for abnormal data in an optical network based on data mining Online publication date: Fri, 24-Apr-2020
by Jinkun Sun
International Journal of Sensor Networks (IJSNET), Vol. 33, No. 1, 2020
Abstract: In order to overcome the problem that abnormal data in optical networks affect the quality of data transmission and result in the loss of data information. In this paper, a new high-precision method for abnormal data recognition in an optical network based on data mining is proposed. The acquisition system is used to collect data in the optical network, and photoelectric conversion is carried out. The collected data are filtered and processed by a filter. A segmentation method is used to extract the sequence features of the data. A distance method is used to complete feature matching to realise the recognition of abnormal data in the optical network. The experimental results show that, compared with the three traditional methods for abnormal data recognition in optical networks, the proposed method has higher recognition accuracy and speed, which can detect abnormal data in optical networks quickly and accurately, and ensure the quality of data transmission.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook