@misc{Hu_Xin_Markov_2024,
 author={Hu, Xin and Dai, Jingyi and Yu, Hao and Wei, Ziyi and Shen, Wei and Wu, Haiyang and Xu, Yingwen and Hu, Chengyong and Deng, Chuanlu and Huang, Yi},
 contributor={Urbańczyk, Wacław. Redakcja},
 identifier={DOI: 10.37190/oa240208},
 year={2024},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 description={Optica Applicata, Vol. 54, 2024, nr 2, s. 217-229},
 description={Optica Applicata is an international journal, published in a non-periodical form in the years 1971-1973 and quarterly since 1973. From the beginning of the year 2008, Optica Applicata is an Open Access journal available online via the Internet, with free access to the full text of articles serving the best interests of the scientific community. The journal is abstracted and indexed in: Chemical Abstracts, Compendex, Current Contents, Inspec, Referativnyj Zhurnal, SCI Expanded, Scopus, Ulrich’s Periodicals Directory},
 description={http://opticaapplicata.pwr.edu.pl/},
 language={eng},
 abstract={To improve the model training efficiency and the classification performance of the phase-sensitive optical time-domain reflectometer (Φ-OTDR) in disturbance events recognition, a preprocessing method based on Markov transition fields (MTF) and auto-encoder (AE) is proposed. The phase time series, derived from demodulation of the original scattering signals, are converted into images by using the MTF method. Subsequently, an auto-encoder is introduced to perform a dimensionality reduction characterization of the MTF images, and the outputs of the encoder will be used as features for classification. The experimental results demonstrate that, compared with directly processing time series using 1-D CNN and classifying MTF images using CNN, the features obtained by the proposed method can accelerate the training process and improve the recognition performance of the classification model. The recognition accuracy for the four classes of events on the fence reaches 95.6%, representing a 12% increase.},
 type={artykuł},
 title={Markov transition fields and auto-encoder-based preprocessing for event recognition of Φ-OTDR},
 keywords={optyka, distributed optical fiber sensing, Φ-OTDR, disturbance recognition, Markov transition fields, auto-encoder},
}