The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling u...The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.展开更多
Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method,...Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.展开更多
The comfort satisfaction of basic facilities of the rail transit transfer station will influence pedestrian choice of vehicle. Aiming at the problem of traffic jams in Changchun in China, we designed a satisfaction qu...The comfort satisfaction of basic facilities of the rail transit transfer station will influence pedestrian choice of vehicle. Aiming at the problem of traffic jams in Changchun in China, we designed a satisfaction questionnaire to investigate the factors which might affect the pedestrian satisfaction in rail transit transfer station in Changchun. By using the statistical methods, including correlation analysis, factor analysis and comparative analysis of satisfaction and importance, we analyzed the survey data, and get the results of analysis. Some suggestions for rail transit transfer station based on the results are given.展开更多
In this work, a deep belief neural network model (DBN) was developed to classify doves, chickens, mice and sheep blood samples, which have many similarities in composition causing their spectra to look almost identica...In this work, a deep belief neural network model (DBN) was developed to classify doves, chickens, mice and sheep blood samples, which have many similarities in composition causing their spectra to look almost identical by visual comparison alone. The DBN model was formulated for the feature extraction from the pretreated fluorescence spectroscopy. Then, cross-validation results showed that the application of deep learning method made it possible to classify the blood fluorescence spectroscopy in a more precise way than previous methods. Especially, the classification accuracy of whole blood with 1% of concentration was up to 97.5%.展开更多
文摘The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.
文摘Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.
文摘The comfort satisfaction of basic facilities of the rail transit transfer station will influence pedestrian choice of vehicle. Aiming at the problem of traffic jams in Changchun in China, we designed a satisfaction questionnaire to investigate the factors which might affect the pedestrian satisfaction in rail transit transfer station in Changchun. By using the statistical methods, including correlation analysis, factor analysis and comparative analysis of satisfaction and importance, we analyzed the survey data, and get the results of analysis. Some suggestions for rail transit transfer station based on the results are given.
文摘In this work, a deep belief neural network model (DBN) was developed to classify doves, chickens, mice and sheep blood samples, which have many similarities in composition causing their spectra to look almost identical by visual comparison alone. The DBN model was formulated for the feature extraction from the pretreated fluorescence spectroscopy. Then, cross-validation results showed that the application of deep learning method made it possible to classify the blood fluorescence spectroscopy in a more precise way than previous methods. Especially, the classification accuracy of whole blood with 1% of concentration was up to 97.5%.
