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 copyright protection scheme for digital content is presented, which is a private watermarking scheme based on the watermark embedding in the DCT domain and watermark extraction Using independent component anal...A novel copyright protection scheme for digital content is presented, which is a private watermarking scheme based on the watermark embedding in the DCT domain and watermark extraction Using independent component analysis （ICA）. The system includes the key for watermark extraction and the host image. The algorithm splits the original image into blocks and classifies these blocks based on visual masking, that is, noise visibility function （NVF）. Watermark components with different strength are inserted into chosen direct current components of DCT coefficients according to the classifier. The watermark extraction is based on the characteristic of the statistic independence of the host image, watermark and key. Principle component analysis （PCA） whitening process and FastICA techniques are introduced to ensure a blind watermark extraction without requiring the original image. Experirnental results show the proposed technique is robust under attacks such as image filtering and adding noise, cropping and resizing. In addition, the proposed private watermarking system can be improved to the application of the DTV content protection system.展开更多
Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code （STBC） systems is proposed by us...Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code （STBC） systems is proposed by using the ordinary independent component analysis （ICA）. This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA （MICA） instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding information than channel estimation based algorithms.展开更多
When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model ...When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.展开更多
A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhanc...A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.展开更多
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This project was supported by the Digital TV Special Foundation of National Development and Reform Commission ofChina (040313) Home Coming Scholars Science Activity Foundation of Ministry of Personnel (20041231) the Graduate In-novation Foundation of Xidian University (innovaion 0509)
文摘A novel copyright protection scheme for digital content is presented, which is a private watermarking scheme based on the watermark embedding in the DCT domain and watermark extraction Using independent component analysis （ICA）. The system includes the key for watermark extraction and the host image. The algorithm splits the original image into blocks and classifies these blocks based on visual masking, that is, noise visibility function （NVF）. Watermark components with different strength are inserted into chosen direct current components of DCT coefficients according to the classifier. The watermark extraction is based on the characteristic of the statistic independence of the host image, watermark and key. Principle component analysis （PCA） whitening process and FastICA techniques are introduced to ensure a blind watermark extraction without requiring the original image. Experirnental results show the proposed technique is robust under attacks such as image filtering and adding noise, cropping and resizing. In addition, the proposed private watermarking system can be improved to the application of the DTV content protection system.
基金supported by the National Natural Science Foundation of China (61201282)
文摘Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code （STBC） systems is proposed by using the ordinary independent component analysis （ICA）. This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA （MICA） instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding information than channel estimation based algorithms.
文摘When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.
基金Projects(81460273,61265006)supported by the National Natural Science Foundation of ChinaProject(2013GXNSFAA019325)supported by Guangxi Natural Science Foundation,ChinaProject(1348020-10)supported by Guangxi Science and Technology Program,China
文摘A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.
