This paper conducts low temperature welding tests on Q460GJC thick plate (60 mm), and based on the basic theory of phase transformation structure evolution, a three-dimensional microstructure evolution analysis method for large welded joints is established, and the analysis of the evolution process of multi-layer and multi-pass weld structure under the low temperature environment of thick plates is completed. The comparison and analysis of test and numerical simulation results are in good agreement, which proves that the welding phase transformation model realizes the digitalization of metallurgical phase transformation in steel structure welding, and optimizes welding process parameters. It is of great significance to improve the quality of welding products and lay a foundation for predicting the performance of welded joints from the micro level.

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.