Title: Abnormal state recognition method for online intelligent examination based on improved genetic algorithm

Authors: Bo Yang; Hongbin Li; Huan Xie; Jianyong Zhao; Rong Zhu; Lei Zhao

Addresses: Beijing Electric Power Research Institute, Beijing 100075, China ' State Grid Beijing Electric Power Company, Beijing 100031, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China ' Beijing Electric Power Research Institute, Beijing 100075, China

Abstract: In order to overcome the defects of the traditional test monitoring scheme, this paper proposes a new method of online intelligent test abnormal state recognition based on improved genetic algorithm. This method sets the test status parameters and defines the criteria of abnormal status, collects the online test information and builds the information database. Image preprocessing is realised from two aspects of image segmentation and greyscale processing. The improved genetic algorithm is used to analyse and collect data intelligently and search image feature points to obtain abnormal feature extraction results. Match the extracted feature results with the established database information to realise the abnormal state recognition results, and start the corresponding abnormal alarm program. The experimental results show that the accuracy of the proposed method is 18.57% higher than that of the traditional method, indicating that the method has a better application prospect.

Keywords: improved genetic algorithm; online examination system; intelligent examination; abnormal state; state recognition.

DOI: 10.1504/IJICT.2021.114853

International Journal of Information and Communication Technology, 2021 Vol.18 No.3, pp.334 - 350

Received: 21 Nov 2019
Accepted: 31 Dec 2019
Published online: 10 May 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article