Equations(2)and(6)and the corresponding discussion in the paper[Chin.Phys.Lett.42,056301(2025)]have been corrected.These modiffcations do not affect the results derived in the paper.
To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label ...To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification（ CRC）.展开更多
Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmissio...Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.展开更多
Wavelet has rapid development in the current mathematics new areas. It also has a double meaning of theory and application. In signal and image compression, signal analysis, engineering technology has a wide range of ...Wavelet has rapid development in the current mathematics new areas. It also has a double meaning of theory and application. In signal and image compression, signal analysis, engineering technology has a wide range of applications. In this paper, we use wavelet method, for estimating the density function for censoring data. We evaluate the mean integrated squared error, convergence ratio of given estimator. Also, we obtain empirical distribution of given estimator and verify the conclusion by two simulation examples.展开更多
The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,whi...The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.展开更多
In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original...In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error （MM） and the asymptotic bias （MB）, respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.展开更多
For a system of two seerningly umrelated regressions.some general results of mean square er-ror matrix comparisons are presented.A class of linear estimators and a class of two-stage estimatorsbased on a generalized u...For a system of two seerningly umrelated regressions.some general results of mean square er-ror matrix comparisons are presented.A class of linear estimators and a class of two-stage estimatorsbased on a generalized unrestricted estimate of the dispersion matrix are proposed.Some exact finitesample properties of the two-stage estimators are obtained.展开更多
For the dislribulion if mean error under independent but not identicallydislribuled conditions. its approximating dislribution whose precision reachO is obtained.
It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the rel...It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.展开更多
针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基...针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基于K-means聚类的最小二乘配置法(KLSC),并在青藏高原GNSS地壳运动实测速度场中验证该方法的有效性。结果表明:1)相较于TLSC算法,KLSC算法利用K-means算法在无监督分类中的优势,基于GNSS速度场本身特征先将研究区域划分为多个速度相似的子区域,然后在每个子区域内分别利用TLSC进行速度场拟合,避免了局部复杂地质环境对区域速度场拟合精度的影响;2)KLSC算法以各网格点到各聚类中心的距离最近为依据选取拟合参数,解决了数据稀疏区域速度场拟合结果较差的问题;3)KLSC算法利用次近距离拟合并结合卷积滤波,有效解决了块体边缘处速度场拟合结果不平滑的问题;4)KLSC算法拟合的速度场的RMSE精度和相关性均优于TLSC算法,东、北向拟合速度场RMSE精度分别提高37%~48.2%和52.1%~67.2%,相关性分别提高24.1%~24.7%和4.7%~5.2%。展开更多
In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance c...In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.展开更多
The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed ...The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed in this paper.There are some common problems, including the number of clusters assumed as a priori knowledge and initial cluster centers selected randomly for classical k-means clustering.The proposed algorithm is used to compute initial cluster centers and a new validity function is added to determine the appropriate number of clusters, then partial least squares (PLS) is used to construct the regression equation for each local cluster.Simulation results showed that multiple models using the proposed algorithm gave good performance, and the feasibility and validity of the proposed algorithm was verified.展开更多
Mean Shift跟踪算法在目标尺度变化大和被遮挡时存在较大的缺陷。针对这一问题,提出了一种基于多级正方形匹配的自适应带宽选择和分块抗遮挡的目标跟踪算法。该算法采用目标中心点的离散程度和增量试探法计算出可能的变化尺度,然后采用...Mean Shift跟踪算法在目标尺度变化大和被遮挡时存在较大的缺陷。针对这一问题,提出了一种基于多级正方形匹配的自适应带宽选择和分块抗遮挡的目标跟踪算法。该算法采用目标中心点的离散程度和增量试探法计算出可能的变化尺度,然后采用多级正方形匹配法预测目标的运动趋势,将巴氏系数最大者的尺度作为Mean Shift核函数新的带宽。同时,对前景目标进行分块,根据子块的遮挡程度自适应改变子块权重并按一定准则融合有效子块的跟踪结果。实验结果表明,该算法具有很好的鲁棒性。展开更多
文摘Equations(2)and(6)and the corresponding discussion in the paper[Chin.Phys.Lett.42,056301(2025)]have been corrected.These modiffcations do not affect the results derived in the paper.
基金The Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the National Natural Science Foundation of China(No.61572258,61103141,51405241)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20151530)Overseas Training Programs for Outstanding Young Scholars of Universities in Jiangsu Province
文摘To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification（ CRC）.
基金supported by the 2011 China Aerospace Science and Technology Foundationthe Certain Ministry Foundation under Grant No.20212HK03010
文摘Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.
文摘Wavelet has rapid development in the current mathematics new areas. It also has a double meaning of theory and application. In signal and image compression, signal analysis, engineering technology has a wide range of applications. In this paper, we use wavelet method, for estimating the density function for censoring data. We evaluate the mean integrated squared error, convergence ratio of given estimator. Also, we obtain empirical distribution of given estimator and verify the conclusion by two simulation examples.
基金supported by the National Key R.D Program of China(2021YFB2401904)the Joint Fund project of the National Natural Science Foundation of China(U21A20485)+1 种基金the National Natural Science Foundation of China(61976175)the Key Laboratory Project of Shaanxi Provincial Education Department Scientific Research Projects(20JS109)。
文摘The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.
基金The NSF(11271155) of ChinaResearch Fund(20070183023) for the Doctoral Program of Higher Education
文摘In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error （MM） and the asymptotic bias （MB）, respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.
基金Suppported in part by Henan Natural Setence Foundatron(004051300)
文摘For a system of two seerningly umrelated regressions.some general results of mean square er-ror matrix comparisons are presented.A class of linear estimators and a class of two-stage estimatorsbased on a generalized unrestricted estimate of the dispersion matrix are proposed.Some exact finitesample properties of the two-stage estimators are obtained.
文摘For the dislribulion if mean error under independent but not identicallydislribuled conditions. its approximating dislribution whose precision reachO is obtained.
基金funding from the National Natural Science Foundation of China (Grant Nos. 41375110 and 41522502)
文摘It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.
文摘针对传统最小二乘配置(traditional least squares collocation,TLSC)算法在大尺度区域地壳运动速度场高精度拟合中,在监测站点稀疏区域与块体边缘处速度场拟合结果会出现异常与不平滑的问题,结合K-means聚类算法与TLSC算法发展了一种基于K-means聚类的最小二乘配置法(KLSC),并在青藏高原GNSS地壳运动实测速度场中验证该方法的有效性。结果表明:1)相较于TLSC算法,KLSC算法利用K-means算法在无监督分类中的优势,基于GNSS速度场本身特征先将研究区域划分为多个速度相似的子区域,然后在每个子区域内分别利用TLSC进行速度场拟合,避免了局部复杂地质环境对区域速度场拟合精度的影响;2)KLSC算法以各网格点到各聚类中心的距离最近为依据选取拟合参数,解决了数据稀疏区域速度场拟合结果较差的问题;3)KLSC算法利用次近距离拟合并结合卷积滤波,有效解决了块体边缘处速度场拟合结果不平滑的问题;4)KLSC算法拟合的速度场的RMSE精度和相关性均优于TLSC算法,东、北向拟合速度场RMSE精度分别提高37%~48.2%和52.1%~67.2%,相关性分别提高24.1%~24.7%和4.7%~5.2%。
基金supported by the National Natural Science Foundation of China(No.42174011)。
文摘In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.
文摘The modeling and control of pH neutralization processes is a difficult problem in the field of process control.A multi-modeling method using an improved k-means clustering based on a new validity function is proposed in this paper.There are some common problems, including the number of clusters assumed as a priori knowledge and initial cluster centers selected randomly for classical k-means clustering.The proposed algorithm is used to compute initial cluster centers and a new validity function is added to determine the appropriate number of clusters, then partial least squares (PLS) is used to construct the regression equation for each local cluster.Simulation results showed that multiple models using the proposed algorithm gave good performance, and the feasibility and validity of the proposed algorithm was verified.
