Existing neural network-based methods for de-raining single images exhibit dissatisfactory results owing to the inefficient propagation of features when objects with sizes and shapes similar to those of rain streaks are present in images. Furthermore, existing methods do not consider that the abundant information included in rain streaked images could interfere with the training process. To overcome these limitations, in this paper, we propose a deep residual learning algorithm called FastDerainNet for removing rain streaks from single images. We design a deep convolutional neural network architecture, based on a deep residual network called the share-source residual module (SSRM), by substituting the origins of all shortcut connections for one point. To further improve the de-raining performance, we adopt the SSRM as the parameter layers in FastDerainNet and use image decomposition to modify the loss function. Finally, we train FastDerainNet on a synthetic dataset. By learning the residual mapping between rainy and clean image detail layers, it is able to reduce the mapping range and simplify the training process. Experiments on both synthetic and real-world images demonstrate that the proposed method achieves increased performance with regard to de-raining, in addition to preserving original details, in comparison with other state-of-the-art methods.

The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of power distribution networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief network (DBN) simulation model for power distribution network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of power distribution network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural network to the reliability analysis of distribution network.

The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serialization model method decreases the extraction accuracy as the text length increases. To address such problems, the goal of this paper is to develop a new approach for document-level relationship extraction by applying a new idea through the consideration of so-called “Local Relationship and Global Inference” (in short, LRGI), which means that we first encode the text using the BERT pre-training model to obtain a local relationship vector first by considering a local context pooling and bilinear group algorithm and then establishing a global inference mechanism based on Floyd’s algorithm to achieve multi-path multi-hop inference and obtain the global inference vector, which allow us to extract multi-classified relationships with adaptive thresholding criteria. Taking the DocRED dataset as a testing set, the numerical results show that our proposed new approach (LRGI) in this paper achieves an accuracy of 0.73, and the value of F1 is 62.11, corresponding to 28% and 2% improvements by comparing with the classical document-level relationship extraction model (ATLOP), respectively.

The textual similarity task, which measures the similarity between two text pieces, has recently received much attention in the natural language processing (NLP) domain. However, due to the vagueness and diversity of language expression, only considering semantic or syntactic features, respectively, may cause the loss of critical textual knowledge. This paper proposes a new type of structure tree for sentence representation, which exploits both syntactic (structural) and semantic information known as the weight vector dependency tree (WVD-tree). WVD-tree comprises structure trees with syntactic information along with word vectors representing semantic information of the sentences. Further, Gaussian attention weight is proposed for better capturing important semantic features of sentences. Meanwhile, we design an enhanced tree kernel to calculate the common parts between two structures for similarity judgment. Finally, WVD-tree is tested on widely used semantic textual similarity tasks. The experimental results prove that WVD-tree can effectively improve the accuracy of sentence similarity judgments.