A new synchronization scheme for chaotic（hyperchaotic） maps with different dimensions is presented.Specifically,given a drive system map with dimension n and a response system with dimension m,the proposed approach ...A new synchronization scheme for chaotic（hyperchaotic） maps with different dimensions is presented.Specifically,given a drive system map with dimension n and a response system with dimension m,the proposed approach enables each drive system state to be synchronized with a linear response combination of the response system states.The method,based on the Lyapunov stability theory and the pole placement technique,presents some useful features：（i） it enables synchronization to be achieved for both cases of n 〈 m and n 〉 m;（ii） it is rigorous,being based on theorems;（iii） it can be readily applied to any chaotic（hyperchaotic） maps defined to date.Finally,the capability of the approach is illustrated by synchronization examples between the two-dimensional H′enon map（as the drive system） and the three-dimensional hyperchaotic Wang map（as the response system）,and the three-dimensional H′enon-like map（as the drive system） and the two-dimensional Lorenz discrete-time system（as the response system）.展开更多
The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor...The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor wavefront imaging technique and the fine upper crustal structure was determined. The results show that the upper crustal structure is relatively well-distributed in laterally and obviously by layers vertically.From surface to 11.0 km depth, there are about four layers. The P wave velocity of top two layers range from 1.65 to 4.5 km/s and their bottom boundaries, the buried depths of which are 0.4, 2.96-3.0 km respectively, are almost horizontal; The third layer is comparatively complicated and its P wave velocity presents inhomogeneous in both laterally and vertically. The bottom boundary of third layer is crystalline basement and shows a little uplift, which seemly suggest that the upper crust had been resisted while the hard Tarim block inserting into Tianshan Mountain; The forth layer is relatively even and its P wave velocity is about 6.3 km/s. There are a lateral velocity variation at the depth of about 4.0 km, and suggest that it has something to do with the hidden Meigaiti fault and Meigaiti-Xiasuhong fault but there are no the structure features about these faults stretching to the surface and passing through the crystalline basement. The seismogenic tectonic of Jiashi strong earthquake swarm at least lies in middle or lower crust beneath 11.0 km depth.展开更多
In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to t...In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.展开更多
A compact determinantal representation for inverse difference is given. From the representation it is easy to know that the inverse difference of a function at some n + 1 points xo, x1, xn depends on th orderings of ...A compact determinantal representation for inverse difference is given. From the representation it is easy to know that the inverse difference of a function at some n + 1 points xo, x1, xn depends on th orderings of these points and it is locally independent of the permutation of first n - 1 points. Moreover we define reciprocal difference from another point of view, get the relation between inverse difference and reciprocal difference and obtain the property that the reciprocal difference is globally independent of the permutation of the points.展开更多
By means of the determinantal formulae for inverse and reciprocal differences with coincident data points, the limiting case of Thiele's interpolating continued fraction expansion is studied in this paper and give...By means of the determinantal formulae for inverse and reciprocal differences with coincident data points, the limiting case of Thiele's interpolating continued fraction expansion is studied in this paper and given numerical example shows that the limiting Thiele's continued fraction expansion can be determined once for all instead of carrying out computations for each step to obtain each convergent as done in [3].展开更多
文摘A new synchronization scheme for chaotic（hyperchaotic） maps with different dimensions is presented.Specifically,given a drive system map with dimension n and a response system with dimension m,the proposed approach enables each drive system state to be synchronized with a linear response combination of the response system states.The method,based on the Lyapunov stability theory and the pole placement technique,presents some useful features：（i） it enables synchronization to be achieved for both cases of n 〈 m and n 〉 m;（ii） it is rigorous,being based on theorems;（iii） it can be readily applied to any chaotic（hyperchaotic） maps defined to date.Finally,the capability of the approach is illustrated by synchronization examples between the two-dimensional H′enon map（as the drive system） and the three-dimensional hyperchaotic Wang map（as the response system）,and the three-dimensional H′enon-like map（as the drive system） and the two-dimensional Lorenz discrete-time system（as the response system）.
基金National Natural Science Foundation of China (40334040) and Joint Seismological Foundation (106076).
文摘The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor wavefront imaging technique and the fine upper crustal structure was determined. The results show that the upper crustal structure is relatively well-distributed in laterally and obviously by layers vertically.From surface to 11.0 km depth, there are about four layers. The P wave velocity of top two layers range from 1.65 to 4.5 km/s and their bottom boundaries, the buried depths of which are 0.4, 2.96-3.0 km respectively, are almost horizontal; The third layer is comparatively complicated and its P wave velocity presents inhomogeneous in both laterally and vertically. The bottom boundary of third layer is crystalline basement and shows a little uplift, which seemly suggest that the upper crust had been resisted while the hard Tarim block inserting into Tianshan Mountain; The forth layer is relatively even and its P wave velocity is about 6.3 km/s. There are a lateral velocity variation at the depth of about 4.0 km, and suggest that it has something to do with the hidden Meigaiti fault and Meigaiti-Xiasuhong fault but there are no the structure features about these faults stretching to the surface and passing through the crystalline basement. The seismogenic tectonic of Jiashi strong earthquake swarm at least lies in middle or lower crust beneath 11.0 km depth.
基金Supported by the Major Program of National Natural Science Foundation of China (No. 70890080 and No. 70890083)
文摘In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.
文摘A compact determinantal representation for inverse difference is given. From the representation it is easy to know that the inverse difference of a function at some n + 1 points xo, x1, xn depends on th orderings of these points and it is locally independent of the permutation of first n - 1 points. Moreover we define reciprocal difference from another point of view, get the relation between inverse difference and reciprocal difference and obtain the property that the reciprocal difference is globally independent of the permutation of the points.
基金Supported by the Foundation for Excellent Young Teachers of the Ministry of Education of China and inpart by the Foundation f
文摘By means of the determinantal formulae for inverse and reciprocal differences with coincident data points, the limiting case of Thiele's interpolating continued fraction expansion is studied in this paper and given numerical example shows that the limiting Thiele's continued fraction expansion can be determined once for all instead of carrying out computations for each step to obtain each convergent as done in [3].
