Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years.

The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured $\phi$-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.

Non-local mean (NLM) can significantly improve the signal-to-noise ratio (SNR), but it inevitably reduces the spatial resolution of distributed optical fiber sensors (DFOS), which hinders its practical application and the improvement of DFOS performance. In this paper, the quantitative relationship between the signal broadening of a phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) and the NLM parameters is analyzed to identify the cause and extent of the spatial resolution degradation. The denoising results for the mimic periodic and $\phi$-OTDR vibration signals indicate that the signal broadening is mainly due to the similarity window size of NLM, and the signal amplitude reduction is caused by the Gaussian smoothing parameter. Compared with the reference signals, the signal broadening of the mimic and $\phi$-OTDR signals after denoising are 2.56% and 2.74%, respectively, which is much less than the previous results. The signal amplitude is reduced by 9.25% and 13.62%, respectively. This work promotes the application of NLM and improves the performance of DFOS.