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With the development of network communication technology and the popularity of social media tools, earthquake-related information has been easily published and disseminated in social networks. This study focuses on obtaining this information and providing guidance for earthquake emergency work. A processing model is proposed to obtain earthquake information from social networks. First, a configuration-driven data acquisition module is designed to acquire earthquake information. Second, according to the characteristics of earthquake information in social media, a seismic emergency thesaurus is selected, and weight is calculated. To solve the low accuracy of inter-class classification, an improved mutual term frequency–inverse document frequency (MTF–IDF) algorithm is proposed. Finally, the thesaurus database is used to classify the acquired earthquake information. By taking the Lushan and Jiuzhaigou earthquakes as examples, the improved MTF–IDF algorithm shows a better effect on the selection of seismic keywords than the traditional TF–IDF algorithm; the F1-measure in classification has increased from 79.86 to 86.93%. The proposed model can rapidly and easily acquire and classify earthquake information according to different sources, which can provide timely information and support for disaster relief.

The permanent magnet synchronous motor (PMSM) is used widely in electric vehicle application due to its high-power density and efficiency. Stator fault is a frequently fault in the motor as it usually works in a harsh environment. Therefore, a stator fault diagnosis method based on the offline motor parameter measurement is proposed to detect and evaluate the stator fault in this paper. Firstly, the line-to-line resistance and inductance of a healthy motor are analyzed when a DC voltage and a high-frequency voltage are excited to the motor respectively, where the DC and AC equivalent circuits at a standstill are introduced. Then, to analyze the resistance and inductance of the stator fault, an extra branch is added to the fault part to obtain the fault equivalent circuits. Accordingly, the stator fault resistance and inductance are derived, and then the resistance and inductance differences between healthy and fault motors are analyzed to provide the basis for the stator fault detection. Furthermore, the fault indicators are defined based on the resistance and inductance differences when a motor has a stator fault. Hence the stator fault severity and location can be evaluated by using these fault indicators. Finally, the experimental results from a 400 W permanent magnet synchronous motor are demonstrated to validate the proposed method.

Locusts are one of the major pests in the world, causing huge losses to the economy, so it is very important to monitor them effectively. The age recognition of locusts is an important part of public opinion monitoring. At present, manual investigation, the visible-light image processing, and trapping methods are mainly used for monitoring. These methods are highly influenced by the environment and cost and have some limitations. In this paper, a method of recognizing the age of locusts in small samples based on spectral analysis and chemical analysis was proposed. Firstly, the hyperspectral scanning of 180 group locusts (including the 3rd age larva of locust, the 5th age larva of locust and the adult of locust) was carried out during the experiment. The data processing was performed by The Environment for Visualizing Images (ENVI) soft to extract the reflectivity data of the region of interest (ROI) in 400–1000 nm band. Secondly, we extracted and analyzed the chemical composition of the surface material of locusts, and finally found 2,4-ditert-butylphenol which can be used to distinguish different age locusts. We also obtained the content range of 2,4-ditert-butylphenol in the surface substances of three different age locusts through experiments. Finally, 180 sets of spectral data and material composition data were analyzed and processed to build a regression prediction model named MLRSVR between them. The model only needs to input the corresponding spectral data of characteristic bands to predict the content of 2,4-ditert-butylphenol on the locust body surface, so as to recognize the age of locust according to the age range of the composition content. The experiment in this paper shows that the regression prediction model MLRSVR constructed under the spectral data of the characteristic bands of 744 nm, 820 nm, 830 nm, and 885 nm has a good effect, in which R2 reaches 0.97. Compared with the spectral data processed in 400–1000 nm, the data amount is greatly reduced and the computing speed of the model is accelerated. In addition, the range of 2,4-ditert-butylphenol content on the body surface of three different age locusts was (0.16, 0.27), (0.34, 0.91), (1.1, 2.1), respectively. Therefore, the model MLRSVR has excellent learning ability and prediction ability of small sample data. This study provides some help for the monitoring of locusts in the field and some reference for the monitoring of other insects.

Numerous experimental observations and data analyses were conducted to simulate the growth of jujube trees. Accordingly, the growth model of jujube was simulated using the logistic model, which was obtained by building this particular growth model. Simultaneously, the branch model based on the turtle interpretation, which simulates the curved shape of the jujube branch and enhances the effect of 3D visualization, was proposed to effectively simulate the morphological structure of the jujube tree. Thereafter, a novel L-system based on the substructure algorithm is developed when jujube is visualized. A substructure algorithm is introduced in an L-system, thereby solving the problem of the same multiple calculation and multi-construction of the structure. Lastly, the morphological structure and growth process of jujube can be controlled through the relevant parameter settings based on the Visual C++ development environment and OpenGL functions. Consequently, a virtual jujube tree modeling and mapping can be achieved.