In many scientific fields, the continuous operation of ultra-stable lasers is crucial for applications. To speed up the frequency stabilization process in case of the occurence of unexpected interruptions, a prompt frequency stabilization approach based on an improved mean shift algorithm is proposed and verified with a homemade laser system. We developed a double-loop feedback controller to steer the laser frequency with fast and slow channels, respectively. In this study, an improved mean shift algorithm is utilized to intelligently search for the transmission signal, which involves adaptively updating the sliding window radius and incorporating a Gaussian kernel function to update the shift vector. The number of lock points on the left and right sides of the central point determines the scanning direction to search for the transmission signal quickly. The laser is intentionally interrupted 306 times within 10,000 s to evaluate the relocking performance. The median auto-locking time of the laser is improved from 16 s to 4 s. By beating with another ultra-stable laser system, the laser frequency instability is measured to be less than $2.1\times {10}^{-14}$ and the linewidth is 5 Hz. This work improves the adaptation and relocking ability of the ultra-stable laser in a complex environment.

In this paper, we present a high-precision optical frequency noise detection and comparison technique using a two-way transfer method over a 260 km field fiber link. This method allows for the comparison of optical frequencies between remote optical references without the need for data transfer through communication. We extend a previously established two-way comparison technique to obtain all data at the local site. Two optical carrier signals are injected into the bidirectional fiber from both ends, and one carrier is reflected back from the remote end. This enables the phase comparison of the two carrier signals at a single site without the need to transmit experimental data. The common-mode frequency noise induced by the bidirectional fiber link is detected and effectively suppressed without the need for sophisticated active fiber noise control. Our demonstration system, which uses a 260 km field fiber link and a common laser source, achieves a fractional instability of $2.5\times {10}^{-17}$ at 1 s averaging time and scales down to $3.5\times {10}^{-21}$ at 8000 s. This scheme offers the distinct advantage of completing the comparison at a single site, eliminating the need for remote data transfer via communication. This method is expected to enhance reliability for high-precision frequency comparisons between remote optical clocks and advanced atomic clocks.

Short-term electric load forecasting is crucial for the daily scheduling and market trading of power systems, ensuring their stable operation. However, the randomness and uncertainty of load series make accurate forecasting exceptionally challenging. To enhance the forecasting accuracy, a novel MPRF residual fusion model based on multilayer perceptron (MLP) and a periodic difference gated recurrent unit (PDGRU) is proposed in this paper. This model focuses on the important, detailed features of the power load series to better capture its volatility. Firstly, the main features are extracted by utilizing the nonlinear mapping capability of MLP. By separating these main features from the original data, more detailed residual features are obtained. Then, in order to make better use of the periodicity of the detailed component, a PDGRU model is proposed that uses periodic difference to optimize the GRU network. Finally, the extracted features are fused to produce the final forecasting result. Results validated by the actual data sets for Shaanxi province, China, show higher accuracy of the proposed model in comparison with other forecasting methods.