AIoT enabled resampling filter for temperature extraction of the Brillouin gain spectrum.

The Artificial Intelligence of Things (AIoT) turns passive fiber sensors into learning machines. It can be used to integrate intelligent nodes into a multi-dimensional sensing system. In this study, the temperature measurement system based on Brillouin Gain Spectrum (BGS) test setup is creatively implemented with the AIoT architecture; the training and testing stages of the neural network are divided into different layers of equipment to improve performance and reduce the network traffic. To enable the lightweight and low-power consumption edge computing device to extract accurate temperature from the BGS during testing, we have integrated the post-processing method inspired by curve fitting into the machine learning and proposed the efficient digital resampling filter. The resampling filter approach is achieved by the peak range algorithm with Gauss differential operator and digital band-pass filter. The evaluation results for different methods on the BGS datasets show the superior performance of our approach. Notably, the approach can increase temperature extraction accuracy and compress the sampling data. The RMSEA of the extraction temperature is 0.5635, which can bring an almost 21% accuracy increase over the classic method. Compared with the classic method of processing the original data on the same hardware platform, the amount of data post-processed by the filter is reduced by 75%; the time consumption is reduced by 22%.

Sweep frequency method with variance weight probability for temperature extraction of the Brillouin gain spectrum based on an artificial neural network.

The development of optical fiber sensors has led to the possibility of accumulating vast, real-time databases of acoustic and other measurements throughout fiber networks, which brings even more widespread concern on improving the sampling effectiveness. In this paper, we present two kinds of sweep frequency methods based on using a neural network to extract temperature from the Brillouin gain spectrum (BGS). Gauss centralization and variance weight probability methods are proposed to compare with the uniform sweep frequency method. By analyzing formulas of the ideal BGS model, we find the gain near the peak of Brillouin gain spectrum has greater correlation with temperature extraction than other positions. Therefore, the Gaussian centralized sweep method is proposed. We further investigate the variation of the weights in the neural network and Brillouin data distribution in different positions and find that the variance is positively correlated with the weights in hidden layers. So, we propose the sweep frequency method based on variance weight probability and make a complement to interpret the rationality of this method in neural network. In all the aforementioned approaches, 281 points are obtained between the 9.07 GHz to 9.35 GHz range under the same condition. The data of each method is trained ten times and tested through the same neural network structure. All the RMSE of each test stage covers all data collecting the passage. The result shows that the RMSE of variance weight probability sweep frequency method is 0.5277, which is superior to the Gauss centralization sweep frequency method that was 0.6864 and the uniform sweep frequency method that was 0.9140.

[Effect of perilipin 1 on chicken preadipocyte lipid accumulation].

AIM: To explore the effect of perilipin 1 on chicken preadipocyte lipid accumulation. METHODS: Firstly, pcDNA3.1-perilipin 1 gene eukaryotic expression vector was constructed and transfected into primary cultured chicken preadipocytes. Twenty-four hours after transfection, chicken preadipocytes were cultured in complete medium with oleate, to induce preadipocyte differentiation. Then perilipin 1 was detected by Western blotting; the role of perilipin 1 in preadipocyte lipid accumulation was investigated by Oil Red O staining extraction assay; the expression levels of other genes (FAS, ACC and ATGL) related to the chicken adipocyte lipid metabolism were tested by real-time RT-PCR. RESULTS: pcDNA3.1-perilipin 1 gene eukaryotic expression vector upregulated the expression of perilipin 1 in the transfected chicken preadipocytes. Over-expression of perilipin 1 promoted chicken preadipocyte lipid accumulation, while there was no significant change in the expression levels of other chicken lipid metabolism related genes (FAS, ACC and ATGL). CONCLUSION: Over-expression of perilipin 1 can promote chicken preadipocyte lipid accumulation.