Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning thi...Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.展开更多
[ Objective] The study aimed to analyze the expression and polymorphism of KAP6.1 gene in various tissues of sheep, as well as the correlation between KAP6.1 gene and wool traits, so as to provide scientific reference...[ Objective] The study aimed to analyze the expression and polymorphism of KAP6.1 gene in various tissues of sheep, as well as the correlation between KAP6.1 gene and wool traits, so as to provide scientific references for the further study on the functions of KAP6.1 gene and molecular breeding of fine wool sheep. [Method] By using real-time fluorescence quantitative PCR, the expression map of KAP6.1 gene in various tissues was analyzed, and then PCR-SSCP technology, cloning and sequencing were adopted to analyze the polymorphism of KAP6.1 gene in 693 Chinese merino sheep （Xinjiang Junken type）, finally the correlation between KAPS. 1 gene and wool traits was discussed. [ Result] A high -level expression of KAP6.1 gene appeared in sheep skin, very significantly higher than that in muscle, small intestines, ovaries, hearts, lungs, livers, spleens, kidneys and rumen （ P 〈0.01 ）. In addition, there was C159T base substitution in KAF6.1 gene sequence, and analysis of the least squares model showed that the mean wool fleece weight of BB genotype at C159T site was significantly higher than that of AA and AB genotype （ P 〈0.05）, while there was no significant difference in average wool fiber diameter, curvature, length, clean fleece rate and density among hA, BB and AB genotype of KAP6.1 gene at C159T site （P〉0.05）. [Conclusion] KAP6.1 gene could be as the candidate gene of wool yield of sheep, and BB genotype could be used as the important molecular marker of fine wool sheep for high wool yield.展开更多
Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive...Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.展开更多
Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candid...Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candidates hold high posterior probability although these are not recognized correctly. We investigate the Word Activation Force (WAF) model that compatibly encodes syntactical and semantic information into sparse coding directed networks. A high-level confidence feature Keyword Activation Force (KAF) based on WAF is proposed. KAF can be used for detecting false alarms by considering information about the neighbors to provide a more reliable and accurate keyword affinity. Compared with the baseline system, a relative reduction of 30.94% in average error rate could be achieved when KAF is combined with the posterior probability and the language model score.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants No.62027803,No.61601096,No.61971111,and No.61801089in part by the Science and Technology Program under Grants No.8091C24,No.2021JCJQJJ0949,and No.2022JCJQJJ0784in part by the Industrial Technology Development Program under Grant No.2020110C041.
文摘Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.
基金supported by Genetically Modified Organisms Breeding Major Projects ( 2009ZX08009-160B) Science and Technology Guidance Plan of Xinjiang Academy of Agricultural and Reclamation Sciences (YYD2010-8)+1 种基金 Science and Technology Key Project of Bintuan Agriculture (2009GG17,2011BA006) Doctoral Foundation of Bintuan (2011BB015)
文摘[ Objective] The study aimed to analyze the expression and polymorphism of KAP6.1 gene in various tissues of sheep, as well as the correlation between KAP6.1 gene and wool traits, so as to provide scientific references for the further study on the functions of KAP6.1 gene and molecular breeding of fine wool sheep. [Method] By using real-time fluorescence quantitative PCR, the expression map of KAP6.1 gene in various tissues was analyzed, and then PCR-SSCP technology, cloning and sequencing were adopted to analyze the polymorphism of KAP6.1 gene in 693 Chinese merino sheep （Xinjiang Junken type）, finally the correlation between KAPS. 1 gene and wool traits was discussed. [ Result] A high -level expression of KAP6.1 gene appeared in sheep skin, very significantly higher than that in muscle, small intestines, ovaries, hearts, lungs, livers, spleens, kidneys and rumen （ P 〈0.01 ）. In addition, there was C159T base substitution in KAF6.1 gene sequence, and analysis of the least squares model showed that the mean wool fleece weight of BB genotype at C159T site was significantly higher than that of AA and AB genotype （ P 〈0.05）, while there was no significant difference in average wool fiber diameter, curvature, length, clean fleece rate and density among hA, BB and AB genotype of KAP6.1 gene at C159T site （P〉0.05）. [Conclusion] KAP6.1 gene could be as the candidate gene of wool yield of sheep, and BB genotype could be used as the important molecular marker of fine wool sheep for high wool yield.
基金National Natural Science Foundation of China(No.51467008)
文摘Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.
基金supported by National Natural Science Foundation of China under Grants No.61005004,No.61175011,No.61171193 the Next-Generation Broadband Wireless Mobile Communications Network Technology Key Project under Grant No.2011ZX03002-005-01+1 种基金 the 111 Project under Grant No.B08004 Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
文摘Many Spoken Term Detection (STD) systems use query expansion to return an increased number of keyword candidates and make posterior probability a confidence feature to reject false alarms. However, some keyword candidates hold high posterior probability although these are not recognized correctly. We investigate the Word Activation Force (WAF) model that compatibly encodes syntactical and semantic information into sparse coding directed networks. A high-level confidence feature Keyword Activation Force (KAF) based on WAF is proposed. KAF can be used for detecting false alarms by considering information about the neighbors to provide a more reliable and accurate keyword affinity. Compared with the baseline system, a relative reduction of 30.94% in average error rate could be achieved when KAF is combined with the posterior probability and the language model score.
