Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless fore...Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.展开更多
Due to electronic jamming transmitted by hostile electromc jamming equtpmcnts tional jamming from other illuminating sources in the complex electromagnetic environment, the per- formance of non-cooperative passive det...Due to electronic jamming transmitted by hostile electromc jamming equtpmcnts tional jamming from other illuminating sources in the complex electromagnetic environment, the per- formance of non-cooperative passive detection systems may degrade it significantly. To solve the problem, a receiving frame with multiple channels for signal preprocessing is designed and a theoret- ical analysis to the received signals in the complex electromagnetic environment is provided. Fur- thermore, a scheme for jamming removal using independent component analysis is proposed. Simula- tion results demonstrate the proposed scheme appears as a very appealing solution for removal of jam- ming and an approximate lOdB signal to distortion ratio over traditional schemes is obtained.展开更多
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t...A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.展开更多
A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis ...A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.展开更多
A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-fre...A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.展开更多
The superconducting nanowire single photon detector（SNSPD） draws much attention because of its attractive performance at ultra violet, visible, and nearinfrared wavelengths, and it can be widespread in quantum infor...The superconducting nanowire single photon detector（SNSPD） draws much attention because of its attractive performance at ultra violet, visible, and nearinfrared wavelengths, and it can be widespread in quantum information technologies. However, how to increase the absorption which can dramatically increase the quantum efficiency of the SNSPD is still a top research issue. In this study, the effect of incident medium and cavity material on the optical absorptance of cavity-integrated SNSPDs was systematically investigated using finite-element method. The simulation results demonstrate that for photons polarized parallel to nanowire orientation, even though the maximum absorptance of the nanowire is insensitive to cavity material,it does increase when the refractive index of incident medium decreases. For perpendicularly polarized photons, both incident medium and cavity material play significant roles,and the absorptance curves get closer to the parallel case as the refractive index of cavity material increases. Based on these results, two cavity-integrated SNSPDs with frontillumination structure which can enhance the absorptance for both parallel and perpendicular photons are proposed.Finally, a design to realize polarization-independent SNSPDs with high absorptance is presented.展开更多
基金National Natural Science Foundations of China(Nos.61374097,61601108)the Fundamental Research Funds for the Central Universities,China(No.N130423006)the Foundation of Northeastern University at Qinhuangdao,China(No.XNK201403)
文摘Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.
基金Supported by the National High Technology Research and Development Programme of China(No.2012AA7014061,2013AA7014061)
文摘Due to electronic jamming transmitted by hostile electromc jamming equtpmcnts tional jamming from other illuminating sources in the complex electromagnetic environment, the per- formance of non-cooperative passive detection systems may degrade it significantly. To solve the problem, a receiving frame with multiple channels for signal preprocessing is designed and a theoret- ical analysis to the received signals in the complex electromagnetic environment is provided. Fur- thermore, a scheme for jamming removal using independent component analysis is proposed. Simula- tion results demonstrate the proposed scheme appears as a very appealing solution for removal of jam- ming and an approximate lOdB signal to distortion ratio over traditional schemes is obtained.
基金Project(217/s/458)supported by Azarbaijan Shahid Madani University,Iran
文摘A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.
基金The National Natural Science Foundation ofChina(No60504033)
文摘A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.
基金Supported by the National Natural Science Foundation of China(61072135)
文摘A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.
基金financially supported by the China State Key Program for Basic Research (No. 2011CBA00304)Tsinghua University Initiative Scientific Research Program (No. 2010Z01010)the National Natural Science Foundation of China (Nos. 61106121 and 61174084)
文摘The superconducting nanowire single photon detector（SNSPD） draws much attention because of its attractive performance at ultra violet, visible, and nearinfrared wavelengths, and it can be widespread in quantum information technologies. However, how to increase the absorption which can dramatically increase the quantum efficiency of the SNSPD is still a top research issue. In this study, the effect of incident medium and cavity material on the optical absorptance of cavity-integrated SNSPDs was systematically investigated using finite-element method. The simulation results demonstrate that for photons polarized parallel to nanowire orientation, even though the maximum absorptance of the nanowire is insensitive to cavity material,it does increase when the refractive index of incident medium decreases. For perpendicularly polarized photons, both incident medium and cavity material play significant roles,and the absorptance curves get closer to the parallel case as the refractive index of cavity material increases. Based on these results, two cavity-integrated SNSPDs with frontillumination structure which can enhance the absorptance for both parallel and perpendicular photons are proposed.Finally, a design to realize polarization-independent SNSPDs with high absorptance is presented.
