Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes ver...Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes very popular,but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability.This paper proposes an effective and robust descriptor of 3D models.The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace–Beltrami operator on the surface,and a spectrum method for dimensionality reduction.The distance metric of the descriptor space is learned by utilizing the joint Bayesian model,and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix.Finally,we apply the descriptor to 3D shape retrieval on a public benchmark.Experiments show that our method is robust and has good retrieval performance.展开更多
Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical ...Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.展开更多
Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equatio...Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.展开更多
为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯...为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。展开更多
A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conven...A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.展开更多
We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the s...We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61872316,61932018.
文摘Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes very popular,but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability.This paper proposes an effective and robust descriptor of 3D models.The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace–Beltrami operator on the surface,and a spectrum method for dimensionality reduction.The distance metric of the descriptor space is learned by utilizing the joint Bayesian model,and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix.Finally,we apply the descriptor to 3D shape retrieval on a public benchmark.Experiments show that our method is robust and has good retrieval performance.
文摘Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.2652017438)the National Science and Technology Major Project of China(No.2016ZX05003-003)
文摘Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.
文摘为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。
文摘A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.
文摘We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.
基金Projects(52004041,U21A2030)supported by the National Natural Science Foundation of ChinaProject(SSOP202106)supported by Shanghai Sheshan National Geophysical Observatory,ChinaProject(2011DA105287-MS202108)supported by the State Key Laboratory of Coal Mine Disaster Dynamics and Control,China。
