In the image steganalysis,the training samples often determine the performance of the model when the features and classification are in the same condition.However the existing research on steganalysis lacks the in-dep...In the image steganalysis,the training samples often determine the performance of the model when the features and classification are in the same condition.However the existing research on steganalysis lacks the in-depth study of the classifier's training method which may deeply influence the detection performance.This paper provides an optimization of universal steganalysis based on the boundary samples classification concerning about image steganalysis.This paper proposes a strategy of selecting boundary samples in steganalysis and divides the training samples into good samples,poor samples and boundary samples three categories and then chose the optimal threshold to get boundary samples through experiments.The experimental results show the effectiveness of boundary sample,which dramatically improve detection capability especially for the low embedding rate Stego-image.展开更多
The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security regio...The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant challenge.To address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree.First,to better characterize the SSRB,boundary samples are generated using the proposed sampling method.These samples are distributed within a limited distance near the SSRB.Then,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear converter.Con-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree.Finally,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.展开更多
This paper shows that for DEM simulations of triaxial tests using samples with a grading that is repre- sentative of a real soil, the sample size significantly influences the observed material response. Four DEM sampl...This paper shows that for DEM simulations of triaxial tests using samples with a grading that is repre- sentative of a real soil, the sample size significantly influences the observed material response. Four DEM samples with identical initial states were produced： three cylindrical samples bounded by rigid wails and one bounded by a cubical periodic cell, When subjected to triaxial loading, the samples with rigid boundaries were more dilative, stiffer and reached a higher peak stress ratio than the sample enclosed by periodic boundaries. For the rigid-wall samples, dilatancy increased and stiffness decreased with increasing sample size, The periodic sample was effectively homogeneous, The void ratio increased and the contact density decreased close to the rigid walls, This heterogeneity reduced with increasing sample size. The positions of the critical state lines （CSLs） of the overall response in e-log p＇ space were sensitive to the sample size, although no difference was observed between their slopes. The critical states of the interior regions of the rigid-wall-bounded samples approached that of the homogeneous periodic sample with increasing sample size. The ultimate strength of the material at the critical state is independent of sample size.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61373169 and 61272453)Doctoral Fund of Ministry of Education of China(Grant No.0110141130006)
文摘In the image steganalysis,the training samples often determine the performance of the model when the features and classification are in the same condition.However the existing research on steganalysis lacks the in-depth study of the classifier's training method which may deeply influence the detection performance.This paper provides an optimization of universal steganalysis based on the boundary samples classification concerning about image steganalysis.This paper proposes a strategy of selecting boundary samples in steganalysis and divides the training samples into good samples,poor samples and boundary samples three categories and then chose the optimal threshold to get boundary samples through experiments.The experimental results show the effectiveness of boundary sample,which dramatically improve detection capability especially for the low embedding rate Stego-image.
基金This work was supported by the National Key Research and Development Program of China(No.2018AAA0101504)the Science and Technology Project of State Grid Corporation of China"fundamental theory of human inthe-loop hybrid-augmented intelligence for power grid dispatch and control".
文摘The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and fluctuation.However,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant challenge.To address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree.First,to better characterize the SSRB,boundary samples are generated using the proposed sampling method.These samples are distributed within a limited distance near the SSRB.Then,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear converter.Con-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree.Finally,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.
基金funding from the Royal Commission for the Exhibition of 1851provided as part of grant EP/1006761/1 from the Engineering and Physical Sciences Research Council
文摘This paper shows that for DEM simulations of triaxial tests using samples with a grading that is repre- sentative of a real soil, the sample size significantly influences the observed material response. Four DEM samples with identical initial states were produced： three cylindrical samples bounded by rigid wails and one bounded by a cubical periodic cell, When subjected to triaxial loading, the samples with rigid boundaries were more dilative, stiffer and reached a higher peak stress ratio than the sample enclosed by periodic boundaries. For the rigid-wall samples, dilatancy increased and stiffness decreased with increasing sample size, The periodic sample was effectively homogeneous, The void ratio increased and the contact density decreased close to the rigid walls, This heterogeneity reduced with increasing sample size. The positions of the critical state lines （CSLs） of the overall response in e-log p＇ space were sensitive to the sample size, although no difference was observed between their slopes. The critical states of the interior regions of the rigid-wall-bounded samples approached that of the homogeneous periodic sample with increasing sample size. The ultimate strength of the material at the critical state is independent of sample size.
