Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In thi...Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.展开更多
Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assum...Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.展开更多
We developed a species-specific PCR method to identify species among dehydrated products of 10 sea cucumber species.Ten reverse species-specific primers designed from the 16 S rRNA gene,in combination with one forward...We developed a species-specific PCR method to identify species among dehydrated products of 10 sea cucumber species.Ten reverse species-specific primers designed from the 16 S rRNA gene,in combination with one forward universal primer,generated PCR fragments of ca.270 bp length for each species.The specificity of the PCR assay was tested with DNA of samples of 21 sea cucumber species.Amplification was observed in specific species only.The species-specific PCR method we developed was successfully applied to authenticate species of commercial products of dehydrated sea cucumber,and was proven to be a useful,rapid,and low-cost technique to identify the origin of the sea cucumber product.展开更多
We examined salt tolerance responsive genes in Pak-choi under salt stress and analyze their potential function. The LRNA differential display was used to screen the transcript derived fragments （TDFs） related to sal...We examined salt tolerance responsive genes in Pak-choi under salt stress and analyze their potential function. The LRNA differential display was used to screen the transcript derived fragments （TDFs） related to salinity tolerance in tolerant and Loderately tolerant Pak-choi germplasm. Seventy-eight primer combinations generated 101 differential eDNA fragments, which ere divided into 10 expression types. Seven cDNA sequences （GenBank accession Nos. DQ006915-DQ006921） obtained and ,~quenced were highly homologous to some known expression genes or the genes related to the signaling pathways in plants under ifferent abiotic stress.展开更多
The coronavirus disease 2019(COVID-19)coronavirus is a new strain of coronavirus that had not been previously detected in humans.As its severe pathogenicity is concerned,it is important to study it thoroughly to aid i...The coronavirus disease 2019(COVID-19)coronavirus is a new strain of coronavirus that had not been previously detected in humans.As its severe pathogenicity is concerned,it is important to study it thoroughly to aid in the discovery of a cure.In this study,the microRNAs(miRNAs)of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus.We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database(miRbase),and then used the miRNA matures sequences of the virus to perform the Basic Local Alignment Search Tool(BLAST)analysis in the coronavirus genome,extending the matched regions of approximately 20 bp to two segments by 200 bp.Six sequences were obtained after deleting redundant sequences.Then,the hairpin structures of the mature miRNAs were determined using RNAfold.The mature sequence on one hairpin arm was selected into a total of 4 sequences,and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus.The miRNAs of the novel coronavirus were annotated by our newly developed method,which will lay the foundation for further study of this virus.展开更多
The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddi...The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.展开更多
Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a co...Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.展开更多
False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,whi...False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.展开更多
基金the National Natural Science Foundation of China(No.61004088)the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)
文摘Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.
基金This work was supported by the National Key R&D Program of China(No.2018YFB1003203)National Natural Science Foundation of China(Nos.61672528,61773392,61772561)+1 种基金Educational Commission of Hu Nan Province,China(No.14B193)the Key Research&Development Plan of Hunan Province(No.2018NK2012).
文摘Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.
基金Supported by the National Natural Science Foundation of China(No.31201999)the Natural Science Foundation of Guangdong Province,China(No.S2011040000463)+4 种基金the Foundation for Distinguished Young Talents in Higher Education of Guangdong,China(No.LYM11086)the Key Laboratory Program of Tropical Marine Bio-Resources and Ecology,Chinese Academy of Science(No.LMB111004)the China Spark Program(Nos.2012GA780007,2012GA780020,2012GA780008)the National Students'Innovation and Entrepreneurship Training Project(No.201210579031)the Zhanjiang Foundation for Science and Technology,China(Nos.2011C3104009,2011D0244,2012C3102018)
文摘We developed a species-specific PCR method to identify species among dehydrated products of 10 sea cucumber species.Ten reverse species-specific primers designed from the 16 S rRNA gene,in combination with one forward universal primer,generated PCR fragments of ca.270 bp length for each species.The specificity of the PCR assay was tested with DNA of samples of 21 sea cucumber species.Amplification was observed in specific species only.The species-specific PCR method we developed was successfully applied to authenticate species of commercial products of dehydrated sea cucumber,and was proven to be a useful,rapid,and low-cost technique to identify the origin of the sea cucumber product.
基金Project supported by the National "the Tenth Five-Year-Plan" Key Program (No. 2004BA525B08)China and the Key Laboratory of Vegetable Genetics and Physiology, Ministry of Agriculture, China
文摘We examined salt tolerance responsive genes in Pak-choi under salt stress and analyze their potential function. The LRNA differential display was used to screen the transcript derived fragments （TDFs） related to salinity tolerance in tolerant and Loderately tolerant Pak-choi germplasm. Seventy-eight primer combinations generated 101 differential eDNA fragments, which ere divided into 10 expression types. Seven cDNA sequences （GenBank accession Nos. DQ006915-DQ006921） obtained and ,~quenced were highly homologous to some known expression genes or the genes related to the signaling pathways in plants under ifferent abiotic stress.
基金the National Natural Science Foundation of China under Grants No.31572618 and No.31972791.
文摘The coronavirus disease 2019(COVID-19)coronavirus is a new strain of coronavirus that had not been previously detected in humans.As its severe pathogenicity is concerned,it is important to study it thoroughly to aid in the discovery of a cure.In this study,the microRNAs(miRNAs)of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus.We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database(miRbase),and then used the miRNA matures sequences of the virus to perform the Basic Local Alignment Search Tool(BLAST)analysis in the coronavirus genome,extending the matched regions of approximately 20 bp to two segments by 200 bp.Six sequences were obtained after deleting redundant sequences.Then,the hairpin structures of the mature miRNAs were determined using RNAfold.The mature sequence on one hairpin arm was selected into a total of 4 sequences,and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus.The miRNAs of the novel coronavirus were annotated by our newly developed method,which will lay the foundation for further study of this virus.
基金supported by National Natural Science Foundation of China(Grant No.51075323)
文摘The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.
基金Supported by the Major State Basic Research Development Program of China(2012CB720500)the National Natural Science Foundation of China(6133301021276078)+3 种基金the National Science Fund for Outstanding Young Scholars(61222303)the Fundamental Research Funds for the Central Universities,Shanghai Rising-Star Program(13QH1401200)the Program for New Century Excellent Talents in University(NCET-10-0885)Shanghai R&D Platform Construction Program(13DZ2295300)
文摘Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.
基金supported by the Funds of the National Key Research and Development Program of China(Grant No.2020YFE0201100)the Funds of National Science of China(Grant nos.61973062,61973068)the Fundamental Research Funds for the Central Universities(Grant nos.N2004010,N2104021,N182008004).
文摘False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.
