An experimental investigation on the residual stress in porous silicon micro-structure by means of micro-Raman spectros- copy is presented. It is shown by detecting the Raman peak shifts on the surfaces and cross-sect...An experimental investigation on the residual stress in porous silicon micro-structure by means of micro-Raman spectros- copy is presented. It is shown by detecting the Raman peak shifts on the surfaces and cross-sections of electrochemical etched porous silicon samples with different porosities that serious residual stresses distribute complicatedly within the whole porous silicon structure. It is proved that micro-Raman spectroscopy is an effective method for residual stress testing on the micro-structures applied in optoelectronics and microelectronics.展开更多
Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled r...Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation (DDR) is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor (CSTR) process verifies the effectiveness of the proposed algorithm.展开更多
In this paper, we applied RobustICA to speech separation and made a comprehensive comparison to FastICA according to the separation results. Through a series of speech signal separation test, RobustICA reduced the sep...In this paper, we applied RobustICA to speech separation and made a comprehensive comparison to FastICA according to the separation results. Through a series of speech signal separation test, RobustICA reduced the separation time consumed by FastICA with higher stability, and speeches separated by RobustICA were proved to having lower separation errors. In the 14 groups of speech separation tests, separation time consumed by RobustICA was 3.185 s less than FastICA by nearly 68%. Separation errors of FastICA had a float between 0.004 and 0.02, while the errors of RobustlCA remained around 0.003. Furthermore, compared to FastICA, RobustlCA showed better separation robustness. Experimental results showed that RohustICA was successful to apply to the speech signal separation, and showed superiority to FastlCA in speech separation.展开更多
This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite progra...This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.展开更多
基金This work was supported by the National Natural Science Foun-dation of China(10232030)
文摘An experimental investigation on the residual stress in porous silicon micro-structure by means of micro-Raman spectros- copy is presented. It is shown by detecting the Raman peak shifts on the surfaces and cross-sections of electrochemical etched porous silicon samples with different porosities that serious residual stresses distribute complicatedly within the whole porous silicon structure. It is proved that micro-Raman spectroscopy is an effective method for residual stress testing on the micro-structures applied in optoelectronics and microelectronics.
基金Supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and Key Technologies R&DProgram in the 10th Five-year Plan (No. 2001BA204B07)
文摘Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation (DDR) is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor (CSTR) process verifies the effectiveness of the proposed algorithm.
基金National Natural Science Foundation of Chinagrant number:61271082,61201029,61102094
文摘In this paper, we applied RobustICA to speech separation and made a comprehensive comparison to FastICA according to the separation results. Through a series of speech signal separation test, RobustICA reduced the separation time consumed by FastICA with higher stability, and speeches separated by RobustICA were proved to having lower separation errors. In the 14 groups of speech separation tests, separation time consumed by RobustICA was 3.185 s less than FastICA by nearly 68%. Separation errors of FastICA had a float between 0.004 and 0.02, while the errors of RobustlCA remained around 0.003. Furthermore, compared to FastICA, RobustlCA showed better separation robustness. Experimental results showed that RohustICA was successful to apply to the speech signal separation, and showed superiority to FastlCA in speech separation.
基金supported by the Key Project of the National Natural Science Foundation of China under Grant No.10631070
文摘This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.
