Experiment Study on Local Scouring Depth around Pile Permeable Spur Dikes

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Named Entity Recognition in Power Marketing Domain Based on Whole Word Masking and Dual Feature Extraction

With the aim of solving the current problems of low utilization of entity features, multiple meanings of a word, and poor recognition of specialized terms in the Chinese power marketing domain named entity recognition (PMDNER), this study proposes a Chinese power marketing named entity recognition method based on whole word masking and joint extraction of dual features. Firstly, word vectorization of the electricity text data is performed using the RoBERTa pre-training model; then, it is fed into the constructed dual feature extraction neural network (DFENN) to acquire the local and global features of text in a parallel manner and fuse them. The output of the RoBERTa layer is used as the auxiliary classification layer, the output of the DFENN layer is used as the master classification layer, and the output of the two layers is dynamically combined through the attention mechanism to weight the outputs of the two layers so as to fuse new features, which are input into the conditional random field (CRF) layer to obtain the most reasonable label sequence. A focal loss function is used in the training process to alleviate the problem of uneven sample distribution. The experimental results show that the method achieved an F1 value of 88.58% on the constructed named entity recognition dataset in the power marketing domain, which is a significant improvement in performance compared with the existing methods

Dynamic Change of Sedimental Microbial Community During Black Bloom-an In Situ Enclosure Simulation Study

Black bloom is a worldwide environmental problem. Sediment microbes play important roles in the process of black bloom. The dynamic change of sedimental microbial community and their potential link between taste and odor compounds during black bloom was investigated in an in situ black bloom enclosure simulation experiment. Through high-throughput sequencing and analysis, pronounced shifts of sedimental microbial community were observed on the 3rd and 7th day in the black bloom group. Microbes in Cyanobacteria, Verrucomicrobia, Planctomycetes, and Actinobacteria were obviously increased, while microbes from the phyla OP8, Chloroflexi, and Acidobacteria were decreased significantly. RDA analysis revealed that the concentrations of chlorophyll a (Chla), total phosphorus (TP), and turbidity (NTU) in the water and the TP, TN concentrations in the sediment were the main environmental factors that affect the microbial community in the sediment. Correlation analysis revealed that microbesDechloromonassp.(OTU003567 and OTU000093),Desulfococcussp. (OTU000911),Chromatiaceae(OTU001222), andMethanosaetasp. (OTU004809) were positively correlated with the taste and odor substances in the sediment, such as dimethyl sulfide (DMS), beta-ionone, beta-cyclocitral and geosmin. The sedimental microbial community gradually recovered in the late phase of black bloom, indicating the stability and self-recovery ability of the sedimental microbial community during black bloom. Noteworthily, we observed many possible pathogens increased significantly during the black bloom, which alerts us to keep away from contaminated sediment when black bloom occurred

Named Entity Recognition for Few-Shot Power Dispatch Based on Multi-Task

In view of the fact that entity nested and professional terms are difficult to identify in the field of power dispatch, a multi-task-based few-shot named entity recognition model (FSPD-NER) for power dispatch is proposed. The model consists of four modules: feature enhancement, seed, expansion, and implication. Firstly, the masking strategy of the encoder is improved by adopting whole-word masking, using a RoBERTa (Robustly Optimized BERT Pretraining Approach) encoder as the embedding layer to obtain the text feature representation, and an IDCNN (Iterated Dilated CNN) module to enhance the feature. Then the text is cut into one Chinese character and two Chinese characters as a seed set, the score for each seed is calculated, and if the score is greater than the threshold value ω, they are passed to the expansion module as candidate seeds; next, the candidate seeds need to be expanded left and right according to offset γ to obtain the candidate entities; finally, to construct text implication pairs, the input text is used as a premise sentence, the candidate entity is connected with predefined label templates as hypothesis sentences, and the implication pairs are passed to the RoBERTa encoder for the classification task. The focus loss function is used to alleviate label imbalance during training. The experimental results of the model on the power dispatch dataset show that the precision, recall, and F1 scores of the recognition results in 20-shot samples are 63.39%, 61.97%, and 62.67%, respectively, which is a significant performance improvement compared to existing methods