Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squ...Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squares programming(SLSQP)algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV.These algorithms were further examined using 200 sample mass formulae derived from theδE term of the E_(isospin) mass model.The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed.This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics.展开更多
The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morph...The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.展开更多
The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal lik...The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.展开更多
The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large...The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition.展开更多
A quantitative structure-activity relationships （QSAR） study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calcul...A quantitative structure-activity relationships （QSAR） study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the solubility of thiazolidine- 4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares （PLS）. The subset of descriptors, which resulted in the low prediction error, was selected by genetic algorithm. This model was applied for the prediction of the solubility of some thiazolidine-4-carboxylic acid derivatives, which were not in the modeling procedure. The relative errors of prediction lower that -4% was obtained by using GA-PLS method. The resulted model showed high prediction ability with RMSEP of 3.8836 and 2.9500 for PLS and GA-PLS models, respectively.展开更多
The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite an...The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite and high False Alert Risk(FAR)caused by a small-slope faulty satellite.In this paper,the LSR algorithm is improved to reduce the MDR for a large-slope faulty satellite and the FAR for a small-slope faulty satellite.Based on the analysis of the vertical critical slope,the optimal decentralized factor is defined and the optimal test statistic is conceived,which can minimize the FAR with the premise that the MDR does not exceed its allowable value of all three directions.To construct a new test statistic approximating to the optimal test statistic,the Optimal Decentralized Factor weighted LSR(ODF-LSR)algorithm is proposed.The new test statistic maintains the sum of pseudo-range residual squares,but the specific pseudo-range residual is weighted with a parameter related to the optimal decentralized factor.The new test statistic has the same decentralized parameter with the optimal test statistic when single faulty satellite exists,and the difference between the expectation of the new test statistic and the optimal test statistic is the minimum when no faulty satellite exists.The performance of the ODFLSR algorithm is demonstrated by simulation experiments.展开更多
As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit...As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.展开更多
Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually re...Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.展开更多
In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS ...In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS algorithm based on logarithmic function(PLFxLMS)is proposed,and the genetic algorithm are introduced to optimize the parameters of logarithmic variable step size FxLMS(LFxLMS),improved logarithmic variable step size Films(IFxLMS),and PLFxLMS algorithms.Bandlimited white noise is used as the input signal,FxLMS,LFxLMS,ILFxLMS,and PLFxLMS algorithms are used to conduct active noise control simulation,and the convergence speed and steady-state characteristic of four algorithms are comparatively analyzed.Compared with the other three algorithms,the PLFxLMS algorithm proposed in this paper has the fastest convergence speed,and small steady-state error.The PLFxLMS algorithm can effectively improve the convergence speed and steady-state error of the FxLMS algorithm that cannot be controlled at the same time,and achieve the optimal effect.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U2267205 and 12475124)a ZSTU intramural grant(22062267-Y)Excellent Graduate Thesis Cultivation Fund(LW-YP2024011).
文摘Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squares programming(SLSQP)algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV.These algorithms were further examined using 200 sample mass formulae derived from theδE term of the E_(isospin) mass model.The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed.This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics.
基金Supported by the National Natural Science Foundation of China(61571063)Key Scientific Research Projects of Colleges and Universities in Henan Province(20A510014)Key Scientific and Technological Projects in Henan Province。
文摘The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.
文摘The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.
基金co-supported by the National Natural Science Foundation of China (Nos. 41804024, 41804026)the Open Fund of Shaanxi Key Laboratory of Integrated and Intelligent Navigation of China (No. SKLIIN-20190205)
文摘The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition.
文摘A quantitative structure-activity relationships （QSAR） study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the solubility of thiazolidine- 4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares （PLS）. The subset of descriptors, which resulted in the low prediction error, was selected by genetic algorithm. This model was applied for the prediction of the solubility of some thiazolidine-4-carboxylic acid derivatives, which were not in the modeling procedure. The relative errors of prediction lower that -4% was obtained by using GA-PLS method. The resulted model showed high prediction ability with RMSEP of 3.8836 and 2.9500 for PLS and GA-PLS models, respectively.
文摘The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite and high False Alert Risk(FAR)caused by a small-slope faulty satellite.In this paper,the LSR algorithm is improved to reduce the MDR for a large-slope faulty satellite and the FAR for a small-slope faulty satellite.Based on the analysis of the vertical critical slope,the optimal decentralized factor is defined and the optimal test statistic is conceived,which can minimize the FAR with the premise that the MDR does not exceed its allowable value of all three directions.To construct a new test statistic approximating to the optimal test statistic,the Optimal Decentralized Factor weighted LSR(ODF-LSR)algorithm is proposed.The new test statistic maintains the sum of pseudo-range residual squares,but the specific pseudo-range residual is weighted with a parameter related to the optimal decentralized factor.The new test statistic has the same decentralized parameter with the optimal test statistic when single faulty satellite exists,and the difference between the expectation of the new test statistic and the optimal test statistic is the minimum when no faulty satellite exists.The performance of the ODFLSR algorithm is demonstrated by simulation experiments.
基金supported by the National Natural Science Foundation of China (61074127)
文摘As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
文摘Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.
文摘In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS algorithm based on logarithmic function(PLFxLMS)is proposed,and the genetic algorithm are introduced to optimize the parameters of logarithmic variable step size FxLMS(LFxLMS),improved logarithmic variable step size Films(IFxLMS),and PLFxLMS algorithms.Bandlimited white noise is used as the input signal,FxLMS,LFxLMS,ILFxLMS,and PLFxLMS algorithms are used to conduct active noise control simulation,and the convergence speed and steady-state characteristic of four algorithms are comparatively analyzed.Compared with the other three algorithms,the PLFxLMS algorithm proposed in this paper has the fastest convergence speed,and small steady-state error.The PLFxLMS algorithm can effectively improve the convergence speed and steady-state error of the FxLMS algorithm that cannot be controlled at the same time,and achieve the optimal effect.
