We try to give a quantitative and global discrimination function by studying mb/MS data using Fisher method that is a kind of pattern recognition methods. The reliability of the function is also analyzed. The results ...We try to give a quantitative and global discrimination function by studying mb/MS data using Fisher method that is a kind of pattern recognition methods. The reliability of the function is also analyzed. The results show that this criterion works well and has a global feature, which can be used as first-level filtering criterions in event identification. The quantitative and linear discrimination function makes it possible to identify events automatically and achieve the goal to react the events quickly and effectively.展开更多
Although the CTBT (Comprehensive Nuclear Test Ban Treaty) was passed in 1996, it is still necessary to develop new and highly efficient methods (Wu Zhongliang, Chen Yuntai, et al., 1993; Xu Shaoxie, et al.1994; Richar...Although the CTBT (Comprehensive Nuclear Test Ban Treaty) was passed in 1996, it is still necessary to develop new and highly efficient methods (Wu Zhongliang, Chen Yuntai, et al., 1993; Xu Shaoxie, et al.1994; Richard L. Garwin, 1994) to monitor possible events. Many discrimination criteria (Xu Shaoxie, et al.,1994; Institute of Geophysics, Chinese Academy of Sciences, 1976; Richard L. Garwin, 1994) have been put forward since the 1950s. The results show that each of the existing criteria has its own limitation, but the seismological method is an important and efficient method in the discrimination between nuclear explosion and earthquake. Especially in recent years, because of the little and little equivalent as well as the increasing hiding steps used in the test, a number of more efficient seismological methods have been worked out. In this paper, a new discrimination method, the Wavelet Packet Component Ratio (WPCR) method, is put forward. This method makes full use of the difference in variation with time between the spectra of nuclear explosions and earthquakes. Its discrimination efficiency is rather high.展开更多
Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector i...Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n-k+1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covariance-orthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA.展开更多
基金Contribution No.05FE3018,Institute of Geophysics,China Earthquake Administrstion
文摘We try to give a quantitative and global discrimination function by studying mb/MS data using Fisher method that is a kind of pattern recognition methods. The reliability of the function is also analyzed. The results show that this criterion works well and has a global feature, which can be used as first-level filtering criterions in event identification. The quantitative and linear discrimination function makes it possible to identify events automatically and achieve the goal to react the events quickly and effectively.
文摘Although the CTBT (Comprehensive Nuclear Test Ban Treaty) was passed in 1996, it is still necessary to develop new and highly efficient methods (Wu Zhongliang, Chen Yuntai, et al., 1993; Xu Shaoxie, et al.1994; Richard L. Garwin, 1994) to monitor possible events. Many discrimination criteria (Xu Shaoxie, et al.,1994; Institute of Geophysics, Chinese Academy of Sciences, 1976; Richard L. Garwin, 1994) have been put forward since the 1950s. The results show that each of the existing criteria has its own limitation, but the seismological method is an important and efficient method in the discrimination between nuclear explosion and earthquake. Especially in recent years, because of the little and little equivalent as well as the increasing hiding steps used in the test, a number of more efficient seismological methods have been worked out. In this paper, a new discrimination method, the Wavelet Packet Component Ratio (WPCR) method, is put forward. This method makes full use of the difference in variation with time between the spectra of nuclear explosions and earthquakes. Its discrimination efficiency is rather high.
基金The National Natural Science Foundation of China (Grant No.60472060 ,60473039 and 60472061)
文摘Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n-k+1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covariance-orthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA.
