Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposi...Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.展开更多
Tribologists often rely on triboexperiments to investigate factors that affect a tribosystem.The inherent dynamic behavior of the respective tribometer setups and its effect on data interpretation remain often unknown...Tribologists often rely on triboexperiments to investigate factors that affect a tribosystem.The inherent dynamic behavior of the respective tribometer setups and its effect on data interpretation remain often unknown.In this study,a comprehensive analysis of sensor data obtained from lubricated and dry triboexperiments is performed.Data are generated on a pin-on-disc test rig with a silicon nitride ball on a steel disc contact with a rotation frequency of~3 Hz.High-speed acquisition of sensor data up to 5 kHz is performed to resolve changes in the data within individual cycles.The characteristic frequencies of the system and their temporal evolution are determined via time--frequency analysis,which reveals periodic patterns in the sensor data.Cycle-based data evaluation allows the detection of localized events and changes during an operation and considerably reduces the apparent measurement uncertainty,as compared with an unreduced dataset.The data analysis and visualization routines presented herein may serve as a prototype for further application to tribometer setups.展开更多
One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model selection.In a complementary role,the paper ...One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model selection.In a complementary role,the paper provides further insights from another perspective that a co-dimensional matrix pair(shortly co-dim matrix pair)forms a building unit and a hierarchy of such building units sets up the BYY system.The BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair,which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse learning.Besides updating typical algorithms of factor analysis(FA),binary FA(BFA),binary matrix factorization(BMF),and nonnegative matrix factorization(NMF)to share such a mechanism,we are also led to(a)a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA(LFA);(b)an alternative formulation of graph Laplacian based linear manifold learning;(c)a codecomposition of data and covariance for learning regularization and data integration;and(d)a co-dim matrix pair based generalization of temporal FA and state space model.Moreover,with help of a co-dim matrix pair in Hadamard product,we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space model.Furthermore,we address that these advances provide with new tools for network biology studies,including learning transcriptional regulatory,Protein-Protein Interaction network alignment,and network integration.展开更多
基金The work described in this paper was supported by a grant of the General Research Fund(GRF)from the Research Grant Council of the Hong Kong SAR(No.CUHK4180/10E)the National Natural Science Foundation of China(Grant Nos.60901067 and 61001212)+1 种基金Program for New Century Excellent Talents in University(No.NCET-09-0630)Program for Changjiang Scholars and Innovative Research Team in University(No.IRT0954),and the Fundamental Research Funds for the Central Universities.
文摘Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.
基金This work was funded by the Austrian COMET Program(Project InTribology,No.872176)via the Austrian Research Promotion Agency(FFG)and the Provinces of Niederosterreich and Vorarlberg,and has been carried out within the Austrian Excellence Centre of Tribology(AC2T research GmbH).
文摘Tribologists often rely on triboexperiments to investigate factors that affect a tribosystem.The inherent dynamic behavior of the respective tribometer setups and its effect on data interpretation remain often unknown.In this study,a comprehensive analysis of sensor data obtained from lubricated and dry triboexperiments is performed.Data are generated on a pin-on-disc test rig with a silicon nitride ball on a steel disc contact with a rotation frequency of~3 Hz.High-speed acquisition of sensor data up to 5 kHz is performed to resolve changes in the data within individual cycles.The characteristic frequencies of the system and their temporal evolution are determined via time--frequency analysis,which reveals periodic patterns in the sensor data.Cycle-based data evaluation allows the detection of localized events and changes during an operation and considerably reduces the apparent measurement uncertainty,as compared with an unreduced dataset.The data analysis and visualization routines presented herein may serve as a prototype for further application to tribometer setups.
基金supported by the General Research Fund from Research Grant Council of Hong Kong(Project No.CUHK4180/10E)the National Basic Research Program of China(973 Program)(No.2009CB825404).
文摘One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model selection.In a complementary role,the paper provides further insights from another perspective that a co-dimensional matrix pair(shortly co-dim matrix pair)forms a building unit and a hierarchy of such building units sets up the BYY system.The BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair,which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse learning.Besides updating typical algorithms of factor analysis(FA),binary FA(BFA),binary matrix factorization(BMF),and nonnegative matrix factorization(NMF)to share such a mechanism,we are also led to(a)a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA(LFA);(b)an alternative formulation of graph Laplacian based linear manifold learning;(c)a codecomposition of data and covariance for learning regularization and data integration;and(d)a co-dim matrix pair based generalization of temporal FA and state space model.Moreover,with help of a co-dim matrix pair in Hadamard product,we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space model.Furthermore,we address that these advances provide with new tools for network biology studies,including learning transcriptional regulatory,Protein-Protein Interaction network alignment,and network integration.
