First, the image is normalized to improve the clarity of the fingerprint image, and then the fingerprint image is cut to improve the accuracy of feature extraction. Then the fingerprint image is smoothed to reduce the noise. Based on the obvious directionality of fingerprint image orientation, the trend of striations was analyzed by the slicing method, and based on orientation, the binarization refinement operation was carried out to eliminate the skeleton of the fingerprint. When the detail feature points are determined on the fingerprint skeleton image, the relationship between the feature points and the core points can be compared to determine whether the fingerprint matches. Before matching, the effective feature points are obtained by two methods: edge removal and distance removal. After the coordinates of feature points are obtained, a series of simplified and abstract operations are carried out to convert the coordinates into hexadecimal numbers and arrange them according to certain rules. Finally, the "fingerprint password" of less than 200 bytes is obtained.
Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established theclassification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.we meshed the area provided by the title, assigned values to the location of Vespa mandarinia(VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.we set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network.
In order to assess the economic impact of the different policies of the Trump and Biden candidates, we formulate metrics on five aspects: Covid-19 prevention and control measures, environmental protection policies, taxation, health care reform, foreign trade. Moreover, each metric is subdivided into several secondary metrics, making for a three-tier hierarchical structure. Take environmental protection policy as an example: Without direct data under Biden's policies, we collected data on U.S. CO2 emissions and U.S. oil consumption during Obama's presidency as Biden's legacy.First, use the analytic hierarchy process (AHP) to select indicators that can reflect the U.S. economy and determine the weight of each indicator. For the U.S. economy, Biden scored 2.6498, Trump 2.3502, suggesting that the election of Biden might make things better for the economy. For China's economy, Biden scored 0.6810 and Trump 0.3245, meaning Biden could give the Chinese economy more room to grow.To reduce the influence of AHP subjectivity on the results, Pearson correlation coefficient is introduced to establish P-AHP model. Take the impact on China's economy. Biden scored 0.5846 and Trump 0.4154.
<p><span style="font-family: 'Times New Roman';">Aiming at the trading problem of gold and bitcoin in the financial market, this paper establishes a trading strategy based on KDJ and MACD indicators, and establishes an effective frontier curve model based on the change of mean variance to determine the investment ratio, and uses Lagrange multiplier method to maximize the trader's return rate.</span></p>
Various random effects models have been developed for clustered binary data; however, traditional approaches to these models generally rely heavily on the specification of a continuous random effect distribution such as Gaussian or beta distribution. In this article, we introduce a new model that incorporates nonparametric unobserved random effects on unit interval (0,1) into logistic regression multiplicatively with fixed effects. This new multiplicative model setup facilitates prediction of our nonparametric random effects and corresponding model interpretations. A distinctive feature of our approach is that a closed‐form expression has been derived for the predictor of nonparametric random effects on unit interval (0,1) in terms of known covariates and responses. A quasi‐likelihood approach has been developed in the estimation of our model. Our results are robust against random effects distributions from very discrete binary to continuous beta distributions. We illustrate our method by analyzing recent large stock crash data in China. The performance of our method is also evaluated through simulation studies.
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