Classification of Hailstone Trajectories in a Hail Cloud over a Semi-Arid Region in China

The growth trajectory of hailstones in clouds determines the ground intensity and spatial distribution of hailfall.A systematic study of hail trajectories can help improve the current scientific understanding of the mechanisms by which hail forms in semi-arid regions of China and,in doing so,improve the quality of hail forecasts and warnings and help to prevent and mitigate disasters.In this study,the WRFv3.7.1 model was employed to provide the background field to drive the hailstone trajectory model.Cluster analysis was then used to classify hail trajectories to investigate the characteristics of different types of hail trajectories and the microphysical characteristics of hail formation.The differences in hail trajectories might be mainly due to differences in the background flow fields and microphysical fields of hail clouds in different regions.Comparative analysis revealed that as the maximum particle size of ground hailfall increased,the maximum supercooled cloud water content and the maximum updraft velocity for the formation and growth of hailstone increased.The larger the size when the hailstone reaches its maximum height,the larger the ground hailstone formed.Overall,the formation and growth of hailstone are caused by the joint action of the dynamical flow field and cloud microphysical processes.The physical processes of hailstone growth and main growth regions differ for different types of hail trajectories.Therefore,different catalytic schemes should be adopted in artificial hail prevention operations for different hail clouds and trajectories due to differences in hail formation processes and ground hailfall characteristics.

Frequent Trajectory Patterns Mining for Intelligent Visual Surveillance System

A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.

A parallel algorithm for detecting traffic patterns using stay point features and moving features

In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.

Computational Geometric Analysis for <i>C. elegans</i>Trajectories on Thermal and Salinity Gradient

Elegans are one of the best model organisms in neural researches, and tropism movement is a typical learning and memorizing activity. Based on one imaging technique called Fast Track-Capturing Microscope (FTCM), we investigated the movement regulation. Two movement patterns are extracted from various trajectories through analysis on turning angle. Then we applied this classification on trajectory regulation on the compound gradient field, and theoretical results corresponded with experiments well, which can initially verify the conclusion. Our breakthrough is performed computational geometric analysis on trajectories. Several independent features were combined to describe movement properties by principal composition analysis (PCA) and support vector machine (SVM). After normalizing all data sets, no-supervising machine learning was processed along with some training under certain supervision. The final classification results performed perfectly, which indicates the further application of such computational analysis in biology researches combining with machine learning.

Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research.In the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was proposed.First,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data.Second,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps.Then,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory point.To validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories.On the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.