Bayesian empirical likelihood is a semiparametric method that combines parametric priors and nonparametric likelihoods, that is, replacing the parametric likelihood function in Bayes theorem with a nonparametric empir...Bayesian empirical likelihood is a semiparametric method that combines parametric priors and nonparametric likelihoods, that is, replacing the parametric likelihood function in Bayes theorem with a nonparametric empirical likelihood function, which can be used without assuming the distribution of the data. It can effectively avoid the problems caused by the wrong setting of the model. In the variable selection based on Bayesian empirical likelihood, the penalty term is introduced into the model in the form of parameter prior. In this paper, we propose a novel variable selection method, L<sub>1/2</sub> regularization based on Bayesian empirical likelihood. The L<sub>1/2</sub> penalty is introduced into the model through a scale mixture of uniform representation of generalized Gaussian prior, and the posterior distribution is then sampled using MCMC method. Simulations demonstrate that the proposed method can have better predictive ability when the error violates the zero-mean normality assumption of the standard parameter model, and can perform variable selection.展开更多
Nuclear Magnetic inversion is the basis of NMR Resonance （NMR） T2 logging interpretation. The regularization parameter selection of the penalty term directly influences the NMR T2 inversion result. We implemented b...Nuclear Magnetic inversion is the basis of NMR Resonance （NMR） T2 logging interpretation. The regularization parameter selection of the penalty term directly influences the NMR T2 inversion result. We implemented both norm smoothing and curvature smoothing methods for NMR T2 inversion, and compared the inversion results with respect to the optimal regular- ization parameters （（Xopt） which were selected by the dis- crepancy principle （DP）, generalized cross-validation （GCV）, S-curve, L-curve, and the slope of L-curve methods, respectively. The numerical results indicate that the DP method can lead to an oscillating or oversmoothed solution which is caused by an inaccurately estimated noise level. The （Xopt selected by the L-curve method is occa- sionally small or large which causes an undersmoothed or oversmoothed T2 distribution. The inversion results from GCV, S-curve and the slope of L-curve methods show satisfying inversion results. The slope of the L-curve method with less computation is more suitable for NMR T2 inversion. The inverted T2 distribution from norm smoothing is better than that from curvature smoothing when the noise level is high.展开更多
Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness...Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem.The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm.In this paper,we present a reconstruction method based on L_(1/2) regularization to enhance sparsity of BLT solution and solve the nonconvex L_(1/2) norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights.To assess the performance of the proposed reconstruction algorithm,simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms,including the weighted interior-point,L1 homotopy,and the Stagewise Orthogonal Matching Pursuit algorithm.Simulation results show that the proposed method yield stable reconstruction results under different noise levels.Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy,multiple-source resolving and image quality.展开更多
Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for ...Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.展开更多
The unprecedented growth of electric vehicles featuring lithium-ion batteries has led to a significant increase in the amount of waste generated,posing pressing waste management challenges for both industry professio ...The unprecedented growth of electric vehicles featuring lithium-ion batteries has led to a significant increase in the amount of waste generated,posing pressing waste management challenges for both industry professio nals and environmental regulators.To address these issues,conventio nal pyrometallurgical,hydrometallurgical,and direct recycling methods are commonly employed to promote sustainable battery development.However,these methods are often hindered by laborious purification processes and the generation of low-profit products such as Li_(2)CO_(3),CoSO_(4),NiSO_(4),etc.Herein,an upcycling technology involving a low-temperature solid-to-solid reaction and water leaching procedures is introduced to transform spent LiCoO_(2)cathode materials into value-added cobalt sulfide-based electrocatalysts.The regenerated electrocatalysts exhibit exceptional performance in the oxygen evolution reaction,surpassing that of the benchmark RuO_(2)catalyst.This proposed upcycling method provides researchers with an alternative way to convert the metallic components of waste lithium-ion batteries into high-value Co-,Ni-,Fe-,and Mn-based catalysts.展开更多
To cure pebrine using dry - hot air treatment of silkworm eggs, and control egg age within 12 hours, oviposition regularity of hybrid silkworm stock moth of Liangguang No. 2--current production variety in Guangdong Pr...To cure pebrine using dry - hot air treatment of silkworm eggs, and control egg age within 12 hours, oviposition regularity of hybrid silkworm stock moth of Liangguang No. 2--current production variety in Guangdong Province was investigated. The experiment showed that most hybrid stock female moths of Liangguang No. 2 at 25 -28℃ oviposited in the first 5 hours, and egg production declined sharply after 5 hours. 7 · Xiang × Fu · 9 achieved the oviposition peak within 1 hour after casting the moth, the oviposition amount within 5 horns accounted for 92.94% of the total oviposition amount of female moth, that within 9 hours accounted for 96.47%. Fu · 9 × 7 · Xiang achieved the oviposition peak within 2 -4 hours after casting the moth, the oviposition amount within 5 hours accounted for 85.21% of the total oviposition amount of female moth, that within 9 hours accounted for 92.78%. Oviposition regularity of the hybrid silkworm stock moth of Liangguang No. 2 meets the need of dry-hot air treatment of silkworm pebrine-control egg age within 12 hours.展开更多
文摘Bayesian empirical likelihood is a semiparametric method that combines parametric priors and nonparametric likelihoods, that is, replacing the parametric likelihood function in Bayes theorem with a nonparametric empirical likelihood function, which can be used without assuming the distribution of the data. It can effectively avoid the problems caused by the wrong setting of the model. In the variable selection based on Bayesian empirical likelihood, the penalty term is introduced into the model in the form of parameter prior. In this paper, we propose a novel variable selection method, L<sub>1/2</sub> regularization based on Bayesian empirical likelihood. The L<sub>1/2</sub> penalty is introduced into the model through a scale mixture of uniform representation of generalized Gaussian prior, and the posterior distribution is then sampled using MCMC method. Simulations demonstrate that the proposed method can have better predictive ability when the error violates the zero-mean normality assumption of the standard parameter model, and can perform variable selection.
基金funded by Shell International Exploration and Production Inc.(PT45371)the National Natural Science Foundation of China-China National Petroleum Corporation Petrochemical Engineering United Fund(U1262114)the National Natural Science Foundation of China(41272163)
文摘Nuclear Magnetic inversion is the basis of NMR Resonance （NMR） T2 logging interpretation. The regularization parameter selection of the penalty term directly influences the NMR T2 inversion result. We implemented both norm smoothing and curvature smoothing methods for NMR T2 inversion, and compared the inversion results with respect to the optimal regular- ization parameters （（Xopt） which were selected by the dis- crepancy principle （DP）, generalized cross-validation （GCV）, S-curve, L-curve, and the slope of L-curve methods, respectively. The numerical results indicate that the DP method can lead to an oscillating or oversmoothed solution which is caused by an inaccurately estimated noise level. The （Xopt selected by the L-curve method is occa- sionally small or large which causes an undersmoothed or oversmoothed T2 distribution. The inversion results from GCV, S-curve and the slope of L-curve methods show satisfying inversion results. The slope of the L-curve method with less computation is more suitable for NMR T2 inversion. The inverted T2 distribution from norm smoothing is better than that from curvature smoothing when the noise level is high.
基金supported by the National Natural Science Foundation of China(No.61401264,11574192)the Natural Science Research Plan Program in Shaanxi Province of China(No.2015JM6322)the Fundamental Research Funds for the Central Universities(No.GK201603025).
文摘Bioluminescence tomography(BLT)is an important noninvasive optical molecular imaging modality in preclinical research.To improve the image quality,reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem.The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm.In this paper,we present a reconstruction method based on L_(1/2) regularization to enhance sparsity of BLT solution and solve the nonconvex L_(1/2) norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights.To assess the performance of the proposed reconstruction algorithm,simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms,including the weighted interior-point,L1 homotopy,and the Stagewise Orthogonal Matching Pursuit algorithm.Simulation results show that the proposed method yield stable reconstruction results under different noise levels.Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy,multiple-source resolving and image quality.
基金This work was supported by the Fundamental Research Funds for the Central Universities(NE2020004)the National Natural Science Foundation of China(61901213)+3 种基金the Natural Science Foundation of Jiangsu Province(BK20190397)the Aeronautical Science Foundation of China(201920052001)the Young Science and Technology Talent Support Project of Jiangsu Science and Technology Associationthe Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(kfjj20200419).
文摘Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.
基金financial support from the National Natural Science Foundation of China(21702143,52303092)Talent Recruitment Project of Guangdong Province(No.2023QN10X078)+1 种基金Open Project of Yunnan Precious Metals Laboratory Co.,Ltd(No.YPML-2023050278)Guangdong Basic and Applied Basic Research Foundation Special Projects——GuangdongShenzhen Joint Funds(2022A1515110027)。
文摘The unprecedented growth of electric vehicles featuring lithium-ion batteries has led to a significant increase in the amount of waste generated,posing pressing waste management challenges for both industry professio nals and environmental regulators.To address these issues,conventio nal pyrometallurgical,hydrometallurgical,and direct recycling methods are commonly employed to promote sustainable battery development.However,these methods are often hindered by laborious purification processes and the generation of low-profit products such as Li_(2)CO_(3),CoSO_(4),NiSO_(4),etc.Herein,an upcycling technology involving a low-temperature solid-to-solid reaction and water leaching procedures is introduced to transform spent LiCoO_(2)cathode materials into value-added cobalt sulfide-based electrocatalysts.The regenerated electrocatalysts exhibit exceptional performance in the oxygen evolution reaction,surpassing that of the benchmark RuO_(2)catalyst.This proposed upcycling method provides researchers with an alternative way to convert the metallic components of waste lithium-ion batteries into high-value Co-,Ni-,Fe-,and Mn-based catalysts.
基金Sponsored by Guangdong Production,Study and Research Program of the Ministry of Education(2012B091100178)Scientific and Technological Promotion Program of Guangdong Provincial Comprehensive Agricultural Development(2011No.70)
文摘To cure pebrine using dry - hot air treatment of silkworm eggs, and control egg age within 12 hours, oviposition regularity of hybrid silkworm stock moth of Liangguang No. 2--current production variety in Guangdong Province was investigated. The experiment showed that most hybrid stock female moths of Liangguang No. 2 at 25 -28℃ oviposited in the first 5 hours, and egg production declined sharply after 5 hours. 7 · Xiang × Fu · 9 achieved the oviposition peak within 1 hour after casting the moth, the oviposition amount within 5 horns accounted for 92.94% of the total oviposition amount of female moth, that within 9 hours accounted for 96.47%. Fu · 9 × 7 · Xiang achieved the oviposition peak within 2 -4 hours after casting the moth, the oviposition amount within 5 hours accounted for 85.21% of the total oviposition amount of female moth, that within 9 hours accounted for 92.78%. Oviposition regularity of the hybrid silkworm stock moth of Liangguang No. 2 meets the need of dry-hot air treatment of silkworm pebrine-control egg age within 12 hours.
