In order to study the relationship between rainfall and related meteorological elements in rainy weather, find out the changes of related meteorological elements before and after rainfall, a multi-dimensional time series data mining model is proposed. The model first performs dimension selection pre-processing on the time series of meteorological elements to remove irrelevant and redundant dimensions, then uses the proposed extreme slope piecewise linear fitting method to segment the time series, data compression and eigenvalue extraction, and finally uses k-means clustering algorithm to symbolize the processed multi-dimensional sequence, and uses rules to extract the rainfall weather model. Experimental results show that the model has good practical value.
Magnetic microrobots have tremendous potential applications due to their wireless actuation and fast response in confined spaces. Herein, inspired by fish, a magnetic microrobot working at liquid surfaces was proposed...
Traditional data mining methods usually need to visit the database repeatedly to determine the frequent item set, which makes the data server burden heavier and reduces the efficiency of data mining. To solve this problem, this paper combines the immune mechanism and genetic algorithm dynamically to improve the traditional genetic algorithm (GA), and proposes a data association rule mining method based on improved immune genetic algorithm (IIGA), which realize the effective analysis of big data. The experimental results show that the algorithm we proposed is better than immune genetic algorithm and Apriori algorithm in data mining time and association rules mining accuracy, which can be better applied to data analysis. The research results have positive reference significance for the field of data mining.
We design a controller based on generalized predictive control (GPC), which is mainly used to intelligently regulate the temperature in a constant temperature warehouse with uncertain parameters. We model it in a generalized control system and use the properties of GPC to make this controller adaptive to different parameters and resistant to random disturbances. Since the warehouse has high requirements for temperature control, we extend the GPC method appropriately numerically and use its numerical equivalent proposition to construct a fast solution algorithm, which is applied in specific simulation experiments. The simulation results prove that the controller we constructed is effective with high adaptability to different parameters and random disturbances and can resist destructive disturbances to a certain extent.
In the field of image classification, graph neural network (GNN) is a kind of structured data modeling architecture with larger functions. However, there are still some problems, such as low efficiency of updating nodes, fixed network parameters and the inability to effectively model the information features of some edges in the graph. In order to solve these problems, this paper introduces attention mechanism on the basis of GNN to improve it, proposes a graph attention network (GAT), establishes a double-layer GAT model, and uses regularization method in model iterative training to achieve image classification. The model is applied to three datasets for experiments. The experimental results show that the average classification accuracy of the proposed model is high and it has good application performance.
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