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An additional secondary factor (ASF) correction method is proposed to improve the accuracy of Loran-C navigation and positioning based on a combination of backward propagation neural network (BPNN) and transfer learning. The BPNN is used to train a network model based on the theoretical ASF data source calculated by the integral equation method. Although the trained BPNN model fitted well with the theoretical ASF results, it is not sufficient for ASF prediction in the actual environment involving uncertain topography and geology. To compensate for this deficiency, transfer learning is employed to further finely calibrate the BPNN model against the ASF measurements. By using the advantages of BPNN and transfer learning, the proposed method achieved higher accuracy than the purely theoretical method. Measurement results are used to validate the superiority of the proposed ASF correction method to the theoretical one. Particularly, the mean absolute error of the ASF prediction results decreased from 2.02 to 0.29 μs after introducing the transfer learning in the trained BPNN model. [ABSTRACT FROM AUTHOR]

We have designed and fabricated a single-mode Raman gain fiber (RGF) and use it to experimentally demonstrate a Raman distributed temperature sensor (RDTS) for high-precision temperature measurement with low power consumption. We have performed simulation to analyze the impact of fluorine-doped inner cladding on the control of the acoustic field and suppression of stimulated Brillouin scattering (SBS), showing that the SBS threshold can be improved by 37dB at 6wt% fluorine-doping concentration. Taking advantage of high Raman gain, a maximum signal-to-noise ratio (SNR) improvement of 6dB has been realized by using RGF under the same pump power as by using single-mode fiber (SMF), and the temperature uncertainty at the fiber end is enhanced from 0.9°C to 0.5°C. To achieve comparable sensing accuracy as that of SMF-based RDTS, our RGF-based RDTS only consumes half of the power, showing good energy efficiency. The magnitude of SNR and temperature uncertainty enhancement is limited by the relatively large fiber attenuation, but it can be further improved through optimization of the fiber design with high fluorine-doping concentration and graded index structure. [ABSTRACT FROM AUTHOR]

Black phosphorus (BP) has been proposed as the channel material in the next generation ultrascaled CMOS devices. In order to gain insight into the current characteristics in 2-D layered materials, the current distribution of a few-layer BP Schottky barrier FET is investigated via state-of-the-art quantum device simulations. Approximately 40% of the total current was found to be concentrated in the top layer when the device was switched on, with the remaining current distributed among the other layers. In comparison, ~80% of the current concentrated below the surface in a Si device with the same structure. These features are related to the strength of the intra/interlayer interaction in few-layer BP and are unique to 2-D layered materials. Moreover, the current distribution and the device performance were different for the top- and side-contacted devices, with the side-contacted devices yielding lower resistance compared with the top-contacted devices. [ABSTRACT FROM PUBLISHER]