A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall

Exploring Mean Annual Precipitation Values (2003–2012) in a Specific Area (36°N–43°N, 113°E–120°E) Using Meteorological, Elevational, and the Nearest Distance to Coastline Variables

Gathering very accurate spatially explicit data related to the distribution of mean annual precipitation is required when laying the groundwork for the prevention and mitigation of water-related disasters. In this study, four Bayesian maximum entropy (BME) models were compared to estimate the spatial distribution of mean annual precipitation of the selected areas. Meteorological data from 48 meteorological stations were used, and spatial correlations between three meteorological factors and two topological factors were analyzed to improve the mapping results including annual precipitation, average temperature, average water vapor pressure, elevation, and distance to coastline. Some missing annual precipitation data were estimated based on their historical probability distribution and were assimilated as soft data in the BME method. Based on this, the univariate BME, multivariate BME, univariate BME with soft data, and multivariate BME with soft data analysis methods were compared. The estimation accuracy was assessed by cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results showed that multivariate BME with soft data outperformed the other methods, indicating that adding the spatial correlations between multivariate factors and soft data can help improve the estimation performance