Articles | Volume 6, issue 2
https://doi.org/10.5194/jsss-6-389-2017
© Author(s) 2017. This work is distributed under the Creative Commons Attribution 4.0 License.
Special issue:
Regular research article
| 
19 Dec 2017
Regular research article |
| 19 Dec 2017
| 19 Dec 2017
Inverse calculation of strain profiles from ETDR measurements using artificial neural networks
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Cited articles
Baviskar, S. and Heimovaara, T.: Quantification of soil water retention parameters using multi-section TDR-waveform analysis, J. Hydrol., 549, 404–415, https://doi.org/10.1016/j.jhydrol.2017.03.068, 2017.
Coccorese, E., Martone, R., and Morabito, F. C.: A neural network approach for the solution of electric and magnetic inverse problems, IEEE T. Magn., 30, 2829–2839, https://doi.org/10.1109/20.312527, 1994.
Höhne, R., Kostka, P., and Modler, N.: Cyclic testing of novel carbon fiber based strain sensor with spatial resolution, 17th European Conference on Composite Materials, Munich, 2016.
Höhne, R., Ehrig, T., Kostka, P., and Modler, N.: Phenomenological investigation of a carbon fiber based strain sensor with spatial resolution by means of time domain reflectometry, Materialwiss. Werkst., 47, 1024–1033, 2017a.
Höhne, R., Kostka, P., and Modler, N.: Characterization of the spatial resolution capability of a novel carbon fiber strain sensor based on characteristic impedance measurements, Proceedings Sensor 2017, 166–171, 2017b.
