At this stage, the PM2.5 concentration prediction algorithm ignores the influence of other air pollution factors, and has not realized the time-dependent integration with the influence of other environmental pollutants. In this regard, the PCA-EDWaveNet-LSTM algorithm considering other air pollution characteristics is proposed. The algorithm proposes to consider other air pollution factors, combine the influence of other air pollution factors with the times dependence on PM2.5 particle concentration, and establish a PCA-EDWaveNet-LSTM algorithm based on air pollution characteristics. In the empirical analysis of PM2.5 historical concentration prediction in Xi’an, the algorithm is compared with RF_Regression algorithm, SVM algorithm, and LSTM neural network. The results show that the prediction performance of this algorithm is better than various traditional prediction algorithms in PM2.5 concentration prediction.
Image captioning tasks based on deep learning encompasses two major domains: computer vision and natural language processing. The Transformer architecture has achieved leading performance in the field of natural language processing, There have been studies using Transformer in image caption encoder and decoder, the results proving better performance compared to previous solutions. Positional encoding is an essential part in Transformer. Rotary Transformer proposed Rotary Position Embedding (RoPE), has achieved comparable or superior performance on various language modeling tasks. Limited work has been done to adapt the Roformer's architecture to image captioning tasks. The study conduct research based on the positional encoding of Transformer architecture, our proposed model consists of modified Roformer as an encoder and BERT as a decoder. With extracted feature as inputs as well as some training tricks, our model achieves similar or better performance on MSCOCO dataset compared to “CNN+RNN” models and regular transformer solutions.
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