Zimian问题实质上是叙拉古算子方程 xi = S xi(xi >1)的解的存在性问题,Erd?s给出长度 m i =1 i = 1= 8的一组解.本文不仅给出了叙拉古算子方程有解的一个必要条件,指出了方程不存在长度 m = 1的解,还给出了方程在长度 m = 18, m...Zimian问题实质上是叙拉古算子方程 xi = S xi(xi >1)的解的存在性问题,Erd?s给出长度 m i =1 i = 1= 8的一组解.本文不仅给出了叙拉古算子方程有解的一个必要条件,指出了方程不存在长度 m = 1的解,还给出了方程在长度 m = 18, m = 13, = 10, = 8 和 m = 5 的若干组解.展开更多
In this paper we consider the Cauchy problem for the singular semilinear parabolic equation u t-Δu+V 1(x)u=V 2(x)u p,x∈R n\{0},t>0, where V 1(x),V 2(x) may have singularities at the origin. Using functions...In this paper we consider the Cauchy problem for the singular semilinear parabolic equation u t-Δu+V 1(x)u=V 2(x)u p,x∈R n\{0},t>0, where V 1(x),V 2(x) may have singularities at the origin. Using functions of the Kato class and the Green tight functions we got the existence of the positive solution being singular at the origin.展开更多
Based on surfaced-related multiple elimination （SRME） , this research has derived the methods on multiples elimination in the inverse data space. Inverse data processing means moving seismic data from forwar...Based on surfaced-related multiple elimination （SRME） , this research has derived the methods on multiples elimination in the inverse data space. Inverse data processing means moving seismic data from forward data space （FDS） to inverse data space （ IDS） . The surface-related multiples and primaries can then be sepa-rated in the IDS, since surface-related multiples wi l l form a focus region in the IDS. Muting the multiples ener-gy can achieve the purpose of multiples elimination and avoid the damage to primaries energy during the process of adaptive subtraction. Randomized singular value decomposition （ RSYD） is used to enhance calculation speed and improve the accuracy in the conversion of FDS to IDS. The synthetic shot record of the salt dome model shows that the relationship between primaries and multiples is simple and clear, and RSVD can easily eliminate multiples and save primaries energy. Compared with conventional multiples elimination methods and ordinary methods of multiples elimination in the inverse data space, this technique has an advantage of high cal-culation speed and reliable outcomes.展开更多
A new interpolation algorithm for Head-Related Transfer Function （HRTF） is proposed to realize 3D sound reproduction via headphones in arbitrary spatial direction. HRTFs are modeled as a weighted sum of spherical ha...A new interpolation algorithm for Head-Related Transfer Function （HRTF） is proposed to realize 3D sound reproduction via headphones in arbitrary spatial direction. HRTFs are modeled as a weighted sum of spherical harmonics on a spherical surface. Truncated Singular Value Decomposition （SVD） is adopted to calculate the weights of the model. The truncation number is chosen according to Frobenius norm ratio and the partial condition number. Compared with other interpolated methods, our proposed approach not only is continuous but exploits global information of available directions. The HRTF from any desired direction can be and interpolated results demonstrate that our obtained more accurately and robustly. Reconstructed proposed algorithm acquired better performance.展开更多
The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functio...The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.展开更多
An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 50...An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.展开更多
文摘Zimian问题实质上是叙拉古算子方程 xi = S xi(xi >1)的解的存在性问题,Erd?s给出长度 m i =1 i = 1= 8的一组解.本文不仅给出了叙拉古算子方程有解的一个必要条件,指出了方程不存在长度 m = 1的解,还给出了方程在长度 m = 18, m = 13, = 10, = 8 和 m = 5 的若干组解.
文摘In this paper we consider the Cauchy problem for the singular semilinear parabolic equation u t-Δu+V 1(x)u=V 2(x)u p,x∈R n\{0},t>0, where V 1(x),V 2(x) may have singularities at the origin. Using functions of the Kato class and the Green tight functions we got the existence of the positive solution being singular at the origin.
文摘Based on surfaced-related multiple elimination （SRME） , this research has derived the methods on multiples elimination in the inverse data space. Inverse data processing means moving seismic data from forward data space （FDS） to inverse data space （ IDS） . The surface-related multiples and primaries can then be sepa-rated in the IDS, since surface-related multiples wi l l form a focus region in the IDS. Muting the multiples ener-gy can achieve the purpose of multiples elimination and avoid the damage to primaries energy during the process of adaptive subtraction. Randomized singular value decomposition （ RSYD） is used to enhance calculation speed and improve the accuracy in the conversion of FDS to IDS. The synthetic shot record of the salt dome model shows that the relationship between primaries and multiples is simple and clear, and RSVD can easily eliminate multiples and save primaries energy. Compared with conventional multiples elimination methods and ordinary methods of multiples elimination in the inverse data space, this technique has an advantage of high cal-culation speed and reliable outcomes.
基金Supported by Shanghai Natural Science Foundation, Shanghai Leading Academic Discipline Project, and STCSM of China (No. 08ZR1408300, S30108, and 08DZ2231100)
文摘A new interpolation algorithm for Head-Related Transfer Function （HRTF） is proposed to realize 3D sound reproduction via headphones in arbitrary spatial direction. HRTFs are modeled as a weighted sum of spherical harmonics on a spherical surface. Truncated Singular Value Decomposition （SVD） is adopted to calculate the weights of the model. The truncation number is chosen according to Frobenius norm ratio and the partial condition number. Compared with other interpolated methods, our proposed approach not only is continuous but exploits global information of available directions. The HRTF from any desired direction can be and interpolated results demonstrate that our obtained more accurately and robustly. Reconstructed proposed algorithm acquired better performance.
基金Supported by the National"863"Project(No.2014AA06A605)
文摘The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.
基金supported by the China Meteorological Special Project(GYHY201206016)the National Basic Research Program of China(2010CB950304)the Innovation Key Program of the Chinese Academy of Sciences(KZCX2-YW-QN202)
文摘An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.
