Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace
  Based on Denoising Convolutional Autoencoder and Bidirectional Long
  Short-Term Memory   Optical time-domain reflectometry (OTDR) has been widely used for
characterizing fiber optical links and for detecting and locating fiber faults.
OTDR traces are prone to be distorted by different kinds of noise, causing
blurring of the backscattered signals, and thereby leading to a misleading
interpretation and a more cumbersome event detection task. To address this
problem, a novel method combining a denoising convolutional autoencoder (DCAE)
and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the
former is used for noise removal of OTDR signals and the latter for fault
detection, localization, and diagnosis with the denoised signal as input. The
proposed approach is applied to noisy OTDR signals of different levels of input
SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i)
the DCAE is efficient in denoising the OTDR traces and it outperforms other
deep learning techniques and the conventional denoising methods; and (ii) the
BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an
improvement of 13.74% compared to the performance of the same model trained
with noisy OTDR signals.

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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga