The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network
Open accessAbstract
Raman distributed optical fiber sensing has the unique ability to measure the spatially distributed profile of temperature that are of great interest to numerous field applications. However, the sensing performance is severely limited by the signal-to-noise ratio (SNR). The existing SNR enhancement schemes have drawbacks such as increased system complexity, degradation of sensor performance metrics such as spatial resolution, poor denoising performance, etc. Here, we report the Raman residual composite dual-convolutional neural network (RRCDNet), a novel convolutional neural network-based denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNet-enhanced Raman distributed fiber sensor system dramatically improves the temperature precision by more than a factor of 100, from 7.57°C to 0.06°C, without hardware modification or degradation of other performance metrics. At the same time, RRCDNet can also enhance other optical fiber sensor systems with one-dimensional signals, such as Rayleigh and Brillouin sensing systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, distributed optical fibre sensors exposed to radiation can experience increased noise intensity (Guo et al., 2024;Khanam et al., 2021).”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: State of the art and challenges (2025) · cites this paper as Guo et al., 2024
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Research works printed but not linked: 49
- 10.1038/s41377-022-00811-x · DOI
- 10.1038/s41377-022-01067-1 · DOI
- 10.1038/s41566-020-0676-z · DOI
- 10.1038/s41467-019-13262-7 · DOI
- 10.1126/science.abe6648 · DOI
- 10.1038/s41467-022-31681-x · DOI
- 10.1038/s41467-021-25114-4 · DOI
- 10.1002/adpr.202000078 · DOI
- 10.1364/PRJ.411870 · DOI
- 10.1038/s41467-018-05404-0 · DOI
- 10.1038/121501c0 · DOI
- 10.1063/1.5113955 · DOI
- 10.1049/el:19850752 · DOI
- 10.1109/JLT.2021.3135653 · DOI
- 10.1109/JSEN.2019.2961185 · DOI
- 10.3390/s20174903 · DOI
- 10.1109/JSEN.2019.2895735 · DOI
- 10.1109/JPROC.2022.3199742 · DOI
- 10.1109/TIM.2022.3225033 · DOI
- 10.1109/TIE.2022.3146503 · DOI
- 10.1364/OE.27.036183 · DOI
- 10.3390/s22062139 · DOI
- 10.1109/JLT.2021.3116203 · DOI
- 10.1049/el:19850402 · DOI
- 10.1109/JLT.2022.3162725 · DOI
- 10.1016/j.optcom.2019.124561 · DOI
- 10.1364/AO.59.000022 · DOI
- 10.1364/JOCN.9.000027 · DOI
- 10.1109/JLT.2015.2493438 · DOI
- 10.1109/JSEN.2021.3124906 · DOI
- 10.1364/oe.440265 · DOI
- 10.1038/s41467-020-19201-1 · DOI
- 10.1016/j.measurement.2013.09.001 · DOI
- 10.1109/jsen.2016.2623860 · DOI
- 10.1109/18.119727 · DOI
- 10.1016/j.optlastec.2014.06.012 · DOI
- 10.3390/s23073637 · DOI
- 10.1109/JSEN.2022.3183014 · DOI
- 10.1109/JLT.2022.3149400 · DOI
- Lehtinen, Noise2Noise: Learning image restoration without clean data, 4620
- 10.1109/TIP.2022.3144017 · DOI
- 10.1109/JLT.2020.3032150 · DOI
- 10.1038/ncomms10870 · DOI
- 10.1007/s11263-019-01198-w · DOI · Paper page
- 10.1016/j.neunet.2019.08.022 · DOI
- 10.1109/ICCV.2015.123 · DOI · Paper page
- 10.1109/CVPR.2019.00223 · DOI
- 10.1109/JAS.2020.1003048 · DOI
- 10.1364/PRJ.442352 · DOI
Cited by
Sorted by citations
Thai, T.-T. N., Do, P., Iung, B., & Gardoni, P. (2025) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2025.v16i4.4306 · Cited by 6
Metadata
- Authors
- Guo, H., Li, J., Xue, X., & Zhang, M.
- Affiliations
- Taiyuan University of Technology (CN)
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering (CN)
- Venue
- Journal of Lightwave Technology
- Published
- 2024-05-15
- Volume
- 42
- Issue
- 10
- Pages
- 3918-3928
- ISSN
- 0733-8724 (ISSN-L)
- 1558-2213
- OpenAlex
- W4391854877
- Cited by
- 18
- References
- 3
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 2
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Lightwave Technology (publishedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (62205234, 62075151, 62105234)
- Fundamental Research Program of Shanxi Province (202103021223042)
- Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Fiber Optic Sensors
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Photonic Crystal and Fiber Optics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
- Keywords
- temperature profile measurement, signal denoising, convolutional neural networks, signal-to-noise ratio improvement, spatial resolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
