This paper studies the passive localization and control of two-dimensional planar UAV swarm under velocity information and azimuth information. We propose a UAV swarm control algorithm based on azimuth information and velocity information. This paper establishes a positioning model through azimuth information. On this basis, the IMU information is fused for attitude calculation. Moreover, the inertial navigation positioning is established by using the attitude angle and velocity information, so as to explore and use the optimal estimation theory. Through Kalman filter technology, the Gaussian noise interference in UAV positioning is greatly eliminated, and the positioning algorithm of UAV swarm is established. In addition, using the azimuth information, the particle swarm optimization is used for path planning, and the target path planning results with higher accuracy are obtained. Through computer simulation analysis, the algorithm has the characteristics of high positioning accuracy and fast iteration speed. The algorithm can better meet the practical engineering application requirements.
Liaoning Province, as a national traditional heavy industry area, contains 30 cities facing varying degrees of urban contraction. We classify these 30 cities by K-means clustering analysis. Then, the grey correlation model is established. By solving the grey correlation coefficient between the economic and social indicators and the total population of shrinking cities, the factors affecting the shrinking urban population are obtained. Finally, we establish a comprehensive evaluation system of shrinking cities with population, indicators and GDP as evaluation indicators. The entropy weight method model is established to determine the weight of each evaluation index in order to obtain the contraction comprehensive index of each city. The change rate of the comprehensive index of each city is calculated respectively, and then the development and evolution trend of each shrinking city is analyzed.
In this study, the chemical composition data of ancient glass were sorted out and analyzed. Based on the training principle of BP neural network, BP neural network was established to solve the problem. After several iterations, 14 input layers, 5 neurons and the training method of radial basis function were finally determined. The data before weathering of weathered relics were finally obtained, so as to predict and restore the missing value of ancient glass chemical elements. In order to verify the rationality and sensitivity of the results, certain parameters were determined to process the data, and cluster analysis was performed again. By comparing the two results, we found that the difference between the two results was small, which verified the rationality and stability of the classification results. Then, through k-means algorithm, the types of glass were subclassified based on the different chemical composition content. For example, high-potassium glass was divided into high-potassium high-calcium glass and high-potassium low-calcium glass, and lead-barium glass was divided into lead-barium high-calcium glass and lead-barium low-calcium glass.
In this paper, we construct a prediction and classification model based on neural network and K-means clustering algorithm to complete the prediction of glass composition before weathering and the classification of unknown glass. In the process of studying glass relics, we discussed the important chemical components and analyzed their influence on the properties of glass. At the same time, through the collected data, the neural network algorithm is used to train and test the data to analyze the composition content of glass before and after weathering. Then, the classification simulation of the data is carried out according to the K-means clustering algorithm. Finally, the classification results of glass relics with different chemical composition in the classification model are analyzed. We conclude that there are six classification systems for high-potassium glass and lead-barium glass. Effective classification can not only reduce the difficulty of scholars' archaeological classification, but also effectively improve the research value of glass relics.
The utilization and allocation of water resources has always been a hot issue of global concern. In order to more reasonably solve the problem of inter-industry competition and uneven distribution of water resources, this study takes five states in the United States and Mexico as examples. We improved the goal programming model and calculated the correlation between dam level difference and total water supply. Then, based on the existing data, we simplify and improve the bankruptcy theoretical model to evaluate the importance of each industry. Based on the highest rate of return of the industry, the weighted planning of water resources utilization of each industry is carried out. Finally, the sensitivity of the model is analyzed by python program fitting curve, and the feasibility of the model is verified. We conclude that the total amount of water that the dam needs to pump is closely related to the water level difference before and after pumping. Relevant departments need to delineate the reasonable water level range of the dam to ensure that the dam can continue to use water supply in the future.
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