In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency est...In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance.展开更多
This paper concerns with efficient projection onto the ordered weighted l_(1)norm ball,which is equivalent to the problem of finding projector onto the intersection of the monotone nonnegative cone and an affine subsp...This paper concerns with efficient projection onto the ordered weighted l_(1)norm ball,which is equivalent to the problem of finding projector onto the intersection of the monotone nonnegative cone and an affine subspace.Based on Lagrangian relaxation and secant approximation method,we propose an easily implementable yet efficient algorithm to solve the projection problem which is proved to terminate after a finite number of iterations.Furthermore,we design efficient implementations for our algorithm and compare it with a semismooth Newton(SSN)algorithm and a root-finding(Root-F)algorithm.Numerical results on a diversity of test problems show that our algorithm is superior than SSN and Root-F.展开更多
In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research interest.By assigning different weights to singular values,the weighted nuclear norm minim...In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research interest.By assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many applications.However,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise assumption.In this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from WNNM.We apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this model.We exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse noise.Numerical results show that our method can effectively remove impulse noise.展开更多
随着复杂储层地震资料特征筛选的机器学习技术的进步,如何有效地对参与地震属性优选和储层反演的地震样本进行采集和分析,成为目前智能地震预测领域的一个研究热点。目前的方法多着重于模型分类算法的改进,在标签的制作和采集方面不仅...随着复杂储层地震资料特征筛选的机器学习技术的进步,如何有效地对参与地震属性优选和储层反演的地震样本进行采集和分析,成为目前智能地震预测领域的一个研究热点。目前的方法多着重于模型分类算法的改进,在标签的制作和采集方面不仅耗费大量时间进行人工标注,还存在标签不平衡情况下类内可靠性、类间平衡性不强等问题。为此,提出基于稀疏强特征提取的三维地震数据完备方法。首先,基于多数决原则的样本分割(Sample Segmentation Based on Majority Rule,SSMR)寻迹多尺度、多标签三维地震样本,进行采集、自动标注;然后,改进标签洗牌平衡方法(Improved Label Shuffling Balance Method,ILSB),通过“2+1”的样本增广平衡策略进行数据完备处理,改善样本采样不平衡性导致的模型训练偏向性;最后,利用基于最小L_(1)范数稀疏表示对奇异值分解结果进行强特征提取(Minimum L_(1)-norm Based Sparse Representation for Feature Extraction,L_(1)-SRFE)和可视化表示。实际资料应用表明,实钻井与验证井预测结果吻合度高,该方法具有较高的标签分类准确率。展开更多
Quantum coherence is a fundamental feature of quantum physics and plays a significant role in quantum information processing.By generalizing the resource theory of coherence from von Neumann measurements to positive o...Quantum coherence is a fundamental feature of quantum physics and plays a significant role in quantum information processing.By generalizing the resource theory of coherence from von Neumann measurements to positive operatorvalued measures(POVMs),POVM-based coherence measures have been proposed with respect to the relative entropy of coherence,the l_(1) norm of coherence,the robustness of coherence and the Tsallis relative entropy of coherence.We derive analytically the lower and upper bounds on these POVM-based coherence of an arbitrary given superposed pure state in terms of the POVM-based coherence of the states in superposition.Our results can be used to estimate range of quantum coherence of superposed states.Detailed examples are presented to verify our analytical bounds.展开更多
In this paper, the L_1-norm estimators and the random weighted statistic fora semiparametric regression model are constructed, the strong convergence rates of estimators areobtain under certain conditions, the strong ...In this paper, the L_1-norm estimators and the random weighted statistic fora semiparametric regression model are constructed, the strong convergence rates of estimators areobtain under certain conditions, the strong efficiency of the random weighting method is shown. Asimulation study is conducted to compare the L_1-norm estimator with the least square estimator interm of approximate accuracy, and simulation results are given for comparison between the randomweighting method and normal approximation method.展开更多
文摘In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance.
基金supported by the National Natural Science Foundation of China(No.11871153)the Natural Science Foundation of Fujian Province of China(No.2019J01644).
文摘This paper concerns with efficient projection onto the ordered weighted l_(1)norm ball,which is equivalent to the problem of finding projector onto the intersection of the monotone nonnegative cone and an affine subspace.Based on Lagrangian relaxation and secant approximation method,we propose an easily implementable yet efficient algorithm to solve the projection problem which is proved to terminate after a finite number of iterations.Furthermore,we design efficient implementations for our algorithm and compare it with a semismooth Newton(SSN)algorithm and a root-finding(Root-F)algorithm.Numerical results on a diversity of test problems show that our algorithm is superior than SSN and Root-F.
基金supported by the National Natural Science Foundation of China under grants U21A20455,61972265,11871348 and 11701388by the Natural Science Foundation of Guangdong Province of China under grant 2020B1515310008by the Educational Commission of Guangdong Province of China under grant 2019KZDZX1007.
文摘In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research interest.By assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many applications.However,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise assumption.In this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from WNNM.We apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this model.We exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse noise.Numerical results show that our method can effectively remove impulse noise.
文摘随着复杂储层地震资料特征筛选的机器学习技术的进步,如何有效地对参与地震属性优选和储层反演的地震样本进行采集和分析,成为目前智能地震预测领域的一个研究热点。目前的方法多着重于模型分类算法的改进,在标签的制作和采集方面不仅耗费大量时间进行人工标注,还存在标签不平衡情况下类内可靠性、类间平衡性不强等问题。为此,提出基于稀疏强特征提取的三维地震数据完备方法。首先,基于多数决原则的样本分割(Sample Segmentation Based on Majority Rule,SSMR)寻迹多尺度、多标签三维地震样本,进行采集、自动标注;然后,改进标签洗牌平衡方法(Improved Label Shuffling Balance Method,ILSB),通过“2+1”的样本增广平衡策略进行数据完备处理,改善样本采样不平衡性导致的模型训练偏向性;最后,利用基于最小L_(1)范数稀疏表示对奇异值分解结果进行强特征提取(Minimum L_(1)-norm Based Sparse Representation for Feature Extraction,L_(1)-SRFE)和可视化表示。实际资料应用表明,实钻井与验证井预测结果吻合度高,该方法具有较高的标签分类准确率。
基金the National Natural Science Foundation of China(Grant Nos.12075159,12171044,and 12175147)the Natural Science Foundation of Beijing(Grant No.Z190005)+2 种基金the Academician Innovation Platform of Hainan ProvinceShenzhen Institute for Quantum Science and EngineeringSouthern University of Science and Technology(Grant No.SIQSE202001)。
文摘Quantum coherence is a fundamental feature of quantum physics and plays a significant role in quantum information processing.By generalizing the resource theory of coherence from von Neumann measurements to positive operatorvalued measures(POVMs),POVM-based coherence measures have been proposed with respect to the relative entropy of coherence,the l_(1) norm of coherence,the robustness of coherence and the Tsallis relative entropy of coherence.We derive analytically the lower and upper bounds on these POVM-based coherence of an arbitrary given superposed pure state in terms of the POVM-based coherence of the states in superposition.Our results can be used to estimate range of quantum coherence of superposed states.Detailed examples are presented to verify our analytical bounds.
基金Supported by the Natural Science Foundation of Beijing City of China (1042002)the Science and Technology Development Foundation of Education Committee of Beijing Citythe Special Expenditure of Excellent Person Education of Beijing(20041D0501515)
文摘In this paper, the L_1-norm estimators and the random weighted statistic fora semiparametric regression model are constructed, the strong convergence rates of estimators areobtain under certain conditions, the strong efficiency of the random weighting method is shown. Asimulation study is conducted to compare the L_1-norm estimator with the least square estimator interm of approximate accuracy, and simulation results are given for comparison between the randomweighting method and normal approximation method.
