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After the completion of the Three Gorges Dam, it increases the risk of inducing an earthquake. We use the GRACE Gravity Field Model to analyze the relationship between the operation of the Three Gorges Reservoir and the change of gravity field in western Sichuan. The research results indicate that the reservoir water level and the western Sichuan gravitational field are positively correlated. In the early stage of rising water level, the change of gravity field is not apparent, and the change of gravity field gradually increases with time. Therefore, the change of reservoir water level affects the gravity field in western Sichuan. The dynamic changes of the gravity field can reflect the Earth’s material change and deformation process and are closely related to earthquakes. Consequently, the Three Gorges Dam will indirectly affect the seismicity in western Sichuan by affecting the gravity field. The research provides valuable information for studying regional reservoir earthquake disasters and supports related policy decisions.

In this article, the integrity of the seismic catalog obtained (1970–2014, M > 2.8) was verified according to the Gutenberg–Richter relation, the appropriate minimum magnitude was determined, and the whole region was divided into five areas according to the geological structure background of the whole research object and the trend of the historical seismic zone. We applied multifractal analysis in each partition. The results showed that although in different geological backgrounds, before major earthquakes, the earthquake time series information dimension had different degrees of growth, and the parameter after the flame had different degrees of decline. To a certain extent, this reflected the seismic energy accumulation and release process. In addition, the variation of fractal parameters in scale-free regions of time series and spatial distribution series also indicated that these two kinds of sequences in different regions show the characteristics of a multifractal structure rather than a single and uniform fractal structure.

In this study, earthquakes with magnitudes above 3.0 on the Richter scale in southwest China from 1900 to 2013 are taken as research objects. The spatio-temporal migration characteristics of seismic activity in the area of concern are studied by using geostatistics method to explore the rules of seismic activity. From the perspective of geostatistics and based on z-value, eight major seismic zones in southwest China were studied zone-wise by using the geostatistics tool ArcGIS and the seismic analysis tool ZMAP to explore the spatial and temporal migration characteristics of earthquakes. It was found that each seismic zone had its own quiet period with relatively inactive seismic activity. Simultaneously, through the study of the time-varying characteristics of the seismic belt, it was found that the time-varying regularity of the seismic frequency of the fault belt with the same or related geological structure background is roughly consistent. In conclusion, the seismic activity in southwest China will still be active in the future.

Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P < 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control. [ABSTRACT FROM AUTHOR]

With the development of recommendation technology, it is of great significance to analyze users' digital footprints on social networking sites, extract user behavior rules, and make a relatively accurate assessment of each user's needs, to provide personalized services for users. It has been found that the users' behavior on social networking sites has a great correlation with the user's personalities. The OCEAN model's five characteristics can cover all aspects of user personality. There are some shortcomings in the current micro-directional propagation method. This paper has improved the traditional collaborative filtering method and proposed a collaborative filtering method based on user clustering. The model first clusters the users according to their OCEAN model, and then it filters the users collaboratively in the cluster to which the user belongs with the collaborative filtering method based on an optimized singular value decomposition (SVD) recommendation algorithm, called the BiasSVD recommendation algorithm -- to reduce the dimensionality of the data. Then it generates recommendations. Experiments show that clustering users' OCEAN models can improve the accuracy of recommendations. Compared with the entire user space, it greatly reduces the recommendation time, and effectively solves the cold start problem in micro directional propagation. [ABSTRACT FROM AUTHOR]

How to eliminate useless information in the vast network information and retain effective information is a problem that needs to be continuously explored in the field of deep learning. This paper conducts text classification on the network evaluation frequently encountered in daily life, mainly to screen out the meaningless comments published by Internet users, to have access to more useful information. In this paper, a text filtering model was constructed based on word vector and Long Short-Term Memory (LSTM) and improved by adding Deep Averaging Networks (DAN) and convolutional neural network (CNN). The major improvement of the LSTM&DAN model was to retain the original word vector information and to improve the accuracy of the text classification model without increasing hyperparameter and model structure complexity. The LSTM&CNN model mainly combines the advantages of convolutional neural network in exploring the deep information of text, which was an improvement over the original LSTM. It was proved by experiments that this improvement is meaningful. Compared with the shallow neural network, the accuracy has been greatly improved. [ABSTRACT FROM AUTHOR]

In recent years, with economic development, urbanization has been accelerating. In the past 100 years, the global average temperature has increased by 0.5°C, and according to the predictions of most global climate models, the temperature will continue to increase by 1.5°C to 3.0°C in the next 100 years. Under the influence of global warming, the urban heat island effect problem is becoming more and more serious, bringing much harm. Studying the thermal effect and its influencing factors is of great significance for sustainable urban development. In this study, the seasonal and interannual surface temperature changes of the study area are inverted based on the SW-TES algorithm, and the LST inversion of the algorithm is simply verified. At the same time, a cell dichotomous model was established to explore the influencing factors of the urban heat island effect and the influence of different land use types and normalized vegetation indices in the Hangzhou metropolitan area was analyzed. The results showed that the overall heating trend of the study area showed a heating trend, the expansion rate of regional construction land was relatively fast, and the scale of urban land was increasing. Based on the results of surface temperature changes of areas with different land types, we obtained the temperature trends of each land type [from 2005 to 2018] and found that there was a negative correlation between surface temperature and normalized vegetation index. This study provides a theoretical basis to evaluate the urban heat island effect and analyze factors that impact it and would promote sustainable urban development. [ABSTRACT FROM AUTHOR]

Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm's ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the  F 1 -score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested. [ABSTRACT FROM AUTHOR]