The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric metho...The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).展开更多
Artificial earthquake catalogue simulation is one of the ways to effectively improve the incompleteness of the existing earthquake catalogue,the scarcity of large earthquake records and the improvement of seismologica...Artificial earthquake catalogue simulation is one of the ways to effectively improve the incompleteness of the existing earthquake catalogue,the scarcity of large earthquake records and the improvement of seismological research. Based on the Poisson distribution model of seismic activity and the Gutenberg-Richter magnitude-frequency relationship,the Monte Carlo method which can describe the characteristics of the stochastic nature and the physical experiment process is used. This paper simulates the future seismic catalogues of the Fenhe-Weihe seismic belt of different durations and conducts statistical tests on them.The analysis shows that the simulation catalogue meets the set seismic activity parameters and meets the Poisson distribution hypothesis,which can obtain a better simulated earthquake catalogues that meets the seismic activity characteristics. According to the simulated earthquake catalogues,future earthquake trends in this region are analyzed to provide reference for seismic hazard analysis.展开更多
AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the ...AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.展开更多
基金the National Natural Science Foundation of China (Grant Nos. 10473013, 10778724 and 90412016)
文摘The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).
基金supported by the National Key R&D Program of China(No.2017YFB0504104)
文摘Artificial earthquake catalogue simulation is one of the ways to effectively improve the incompleteness of the existing earthquake catalogue,the scarcity of large earthquake records and the improvement of seismological research. Based on the Poisson distribution model of seismic activity and the Gutenberg-Richter magnitude-frequency relationship,the Monte Carlo method which can describe the characteristics of the stochastic nature and the physical experiment process is used. This paper simulates the future seismic catalogues of the Fenhe-Weihe seismic belt of different durations and conducts statistical tests on them.The analysis shows that the simulation catalogue meets the set seismic activity parameters and meets the Poisson distribution hypothesis,which can obtain a better simulated earthquake catalogues that meets the seismic activity characteristics. According to the simulated earthquake catalogues,future earthquake trends in this region are analyzed to provide reference for seismic hazard analysis.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10778724 and 11033001)the Natural Science Foundation of Education Department of Hebei Province (GrantNo. ZD2010127) the Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences
文摘AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.
