An improved quadratic polynomial fitting method is applied to extract Brillouin frequency shift from Brillouin spectrum along optical fiber to improve the real-time performance of strain measurement along optical fiber composite overhead lines in the distribution Internet of Things in electricity. The principle of the improved quadratic polynomial fitting method is to use the median filter algorithm to improve the accuracy of spectrum peak location, and then select the symmetric Brillouin spectrum for quadratic polynomial fitting. The spectrum fitting method based on the Lorentzian model, the original and improved quadratic polynomial fitting methods are used to extract Brillouin frequency shift according to the Brillouin spectra with different signal-to-noise ratios. The results reveal that the improved quadratic polynomial fitting method has the similar accuracy with the spectrum fitting method based on the Lorentzian model, and the computation time is only about 1/100 of that of the latter. This work provides a reference to improve the real-time performance of strain measurement for overhead lines in distribution Internet of Things in electricity.
Load-deflection model of press straightening process is explained. The method and procedures of establishing load-deflection model by using ANSYS software are described. Based on the model, the straightening stroke can be calculated, so it is solved to apply FEM to automatic straightening machine. An example is given, calculation results by FEM are in good agreement with the experimental results. The validity of the method is proved.
This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates the information flow, and increases the training efficiency and accuracy of feature extraction and propagation with fewer parameters in the network. Sub-pixel shuffling and dropout techniques are used in the designed network and applied to reduce the computational demand and improve training efficiency. The network is trained in a supervised manner, where the input and output are the measurements of the available channels at response available locations and desired channels at response unavailable locations. The proposed densely connected convolutional networks automatically extract the high-level features of the input data and construct the complicated nonlinear relationship between the responses of available and desired locations. Experimental studies are conducted using the measured acceleration responses from Guangzhou New Television Tower to investigate the effects of the locations of available responses, the numbers of available and unavailable channels, and measurement noise. The results demonstrate that the proposed approach can accurately reconstruct the responses in both time and frequency domains with strong noise immunity. The reconstructed response is further used for modal identification to demonstrate the usability and accuracy of the reconstructed responses. The applicability of the proposed approach for structural health monitoring is further proved by the highly consistent modal parameters identified from the reconstructed and true responses.
