Nuclear charge radius predictions by kernel ridge regression with odd-even effects

The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.

Ridge regression energy levels calculation of neutral ytterbium(Z=70)

In view of the difficulty in calculating the atomic structure parameters of high-Z elements,the Hartree–Fock with relativistic corrections(HFR)theory in combination with the ridge regression(RR)algorithm rather than the Cowan code’s least squares fitting(LSF)method is proposed and applied.By analyzing the energy level structure parameters of the HFR theory and using the fitting experimental energy level extrapolation method,some excited state energy levels of the Yb I(Z=70)atom including the 4f open shell are calculated.The advantages of the ridge regression algorithm are demonstrated by comparing it with Cowan code’s LSF results.In addition,the results obtained by the new method are compared with the experimental results and other theoretical results to demonstrate the reliability and accuracy of our approach.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.

A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy

In the spectral analysis of laser-induced breakdown spectroscopy,abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral data.Here,a feature selection method called recursive feature elimination based on ridge regression(Ridge-RFE)for the original spectral data is recommended to make full use of the valid information of spectra.In the Ridge-RFE method,the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic,the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination(RFE),and the selected features were used as inputs of the partial least squares regression(PLS)model.The Ridge-RFE method based PLS model was used to measure the Fe,Si,Mg,Cu,Zn and Mn for 51 aluminum alloy samples,and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as input.The overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features,make PLS model for better quantitative analysis results and improve model generalization ability.

Novel Pilot-aided Ridge Regression Channel Estimation for SC-FDE System on Time-varying Frequency Selective Fading Channel

A novel pilot-aided ridge regression (RR) channel estimation for SC-FDE system on time-varying frequency selective fading channel is derived. Previous least square (LS) channel estimation, which does not consider and utilize the influence of noise, has poor performance when the observed signal is corrupted abnormally by noise. In order to overcome the inherent disadvantage of LS estimation, the proposed RR estimation uses the influence of noise to get better performance. The performance of this new estimator is examined. The numerical results are presented to show that the new estimation improves the accuracy of estimation especially in low channel signal-to-noise ratio (CSNR) level and outperforms LS estimation. In addition, the proposed RR estimation can get the gains of about 1dB compared with LS estimation.

The Simultaneous Determination of Five Components Including Acetaminophen by Ridge Regression Spectrophotometry

Ridge regression spectrophotometry(LHG)is used for thesimultaneous determination of five components(acetaminophen,p-aminophenol, caffeine, chlorphenamine maleate and guaifenesin)incough syr- up. The computer program of LHG is based on VB language.The difficulties in overlapping of absorption spectrums of fivecompounds are overcome by this procedure. The experimental resultsshow that the recovery of each component is in the range from97.9/100 to 103.3/100 and each component obtains satisfactory resultswithout any pre-separation.

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level.Accurate prediction of diabetes patients is an important research area.Many researchers have proposed techniques to predict this disease through data mining and machine learning methods.In prediction,feature selection is a key concept in preprocessing.Thus,the features that are relevant to the disease are used for prediction.This condition improves the prediction accuracy.Selecting the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high accuracy.In this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data set.Overfitting is a major problem in feature selection,where the new data are unfit to the model because the training data are small.Ridge regression is mainly used to overcome the overfitting problem.The features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected features.This work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed algorithm.The accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

The Application of Ridge Regression in Dynamic Balancing of Flexible Rotors Based on Influence Coefficient Method

Based on the model structure of the influence coefficient method analyzed in depth by matrix theory,it is explained the reason why the unreasonable and instable correction masses with bigger MSE are obtained by LS influence coefficient method when there are correlation planes in the dynamic balancing.It also presened the new ridge regression method for solving correction masses according to the Tikhonov regularization theory,and described the reason why the ridge regression can eliminate the disadvantage of the LS method. Applying this new method to dynamic balancing of gas turbine, it is found that this method is superior to the LS method when influence coefficient matrix is ill-conditioned,the minimal correction masses and residual vibration are obtained in the dynamic balancing of rotors.

Aeromagnetic compensation method based on ridge regression algorithm

With the development of UAV technology,UAV aerial magnetic survey plays an important role in the airborne geophysical prospecting.In the aeromagnetic survey,the magnetic field interferences generated by the magnetic components on the aircraft greatly affect the accuracy of the survey results.Therefore,it is necessary to use aeromagnetic compensation technology to eliminate the interfering magnetic field.So far,the aeromagnetic compensation methods used are mainly linear regression compensation methods based on the T-L equation.The least square is one of the most commonly used methods to solve multiple linear regressions.However,considering that the correlation between data may lead to instability of the algorithm,we use the ridge regression algorithm to solve the multicollinearity problem in the T-L equation.Subsequently this method is applied to the aeromagnetic survey data,and the standard deviation is selected as the index to evaluate the compensation effect to verify the effectiveness of the method.

Examination of machine learning for assessing physical effects:Learning the relativistic continuum mass table with kernel ridge regression

The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the binding energies of 9035 nuclei,the KRR method achieves a root-mean-square deviation of 0.96 MeV,and the KRRoe method remarkably reduces the deviation to 0.17 MeV.By investigating the shell effects,one-nucleon and twonucleon separation energies,odd-even mass differences,and empirical proton-neutron interactions extracted from the learned binding energies,the ability of the machine learning tool to grasp the known physics is discussed.It is found that the shell effects,evolutions of nucleon separation energies,and empirical proton-neutron interactions are well reproduced by both the KRR and KRRoe methods,although the odd-even mass differences can only be reproduced by the KRRoe method.

Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

We propose a novel indoor positioning algorithm based on the received signal strength(RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering(AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

Application of kernel ridge regression in predicting neutron-capture reaction cross-sections

This article provides the first application of the machine-learning approach in the study of the cross-sections for neutron-capture reactions with the kernel ridge regression(KRR)approach.It is found that the KRR approach can reduce the root-mean-square(rms)deviation of the relative errors between the experimental data of the Maxwellian-averaged(n,γ)cross-sections and the corresponding theoretical predictions from 69.8%to 35.4%.By including the data with different temperatures in the training set,the rms deviation can be further significantly reduced to 2.0%.Moreover,the extrapolation performance of the KRR approach along different temperatures is found to be effective and reliable.

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making process in patient treatment.Since manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so far.With this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)technique.The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data.Besides,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of features.In addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of disease.The novelty of the current work is its designing of CMBO-FS model for data classification.Moreover,CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy.The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

Soft Sensing Modelling Based on Optimal Selection of Secondary Variables and Its Application

The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression （KRR） to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor＇s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.

Potato absorption and phytoavailability of Cd, Ni, Cu, Zn and Pb in sierozem soils amended with municipal sludge compost

Effects of sludge utilization on the mobility and phytoavailability of heavy metals in soil-plant systems have attracted broad attention in recent years. In this study, we analyzed the effects of municipal sludge compost （MSC） on the solubility and pIant uptake of Cd, Ni, Cu, Zn and Pb in a soil-potato system to explore the mobility, potato plant uptake and enrichment of these five heavy metals in sierozem soils amended with MSC through a potato cultivation trial in Lanzhou University of China in 2014. Ridge regression analysis was conducted to investigate the phytoavailability of heavy metals in amended soils. Furthermore, CaC12, CH3COONH4, CH3COOH, diethylene triamine pentacetic acid （DTPA） and ethylene diamJne tetraacetic acid （EDTA） were used to extract the labile fraction of heavy metals from the amended soils. The results show that the MSC could not only improve the fertility but also increase the dissolved organic carbon （DOC） content of sierozem soils. The total concentrations and labile fraction proportions of heavy metals increase with increasing MSC percentage in sierozem soils. In amended soils, Cd has the highest solubility and mobility while Ni has the lowest solubility and mobility among the five heavy metals. The MSC increases the concentrations of heavy metals in the root, stem, peel and tuber of the potato plant, with the concentrations being much higher in the stem and root than in the peel and tuber. Among the five heavy metals, the bioconcentration factor value of Cd is the highest, while that of Ni is the lowest. The complexing agent （DTPA and EDTA） extractable fractions of heavy metals are the highest in terms of phytoavailability. Soil properties （including organic matter, pH and DOC） have important impacts on the phytoavailability of heavy metals. Our results suggest that in soil-potato systems, although the MSC may improve soil fertility, it can also increase the risk of soils exposed to heavy metals.

Hydrodynamic Modeling with Grey-Box Method of A Foil-Like Underwater Vehicle

In this study, a dynamic modeling method for foil-like underwater vehicles is introduced and experimentally verified in different sea tests of the Hadal ARV. The dumping force of a foil-like underwater vehicle is sensitive to swing motion. Some foil-like underwater vehicles swing periodically when performing a free-fall dive task in experiments. Models using conventional modeling methods yield solutions with asymptotic stability, which cannot simulate the self-sustained swing motion. By improving the ridge regression optimization algorithm, a grey-box modeling method based on 378 viscous drag coefficients using the Taylor series expansion is proposed in this study. The method is optimized for over-fitting and convergence problems caused by large parameter matrices. Instead of the PMM test data, the unsteady computational fluid dynamics calculation results are used in modeling. The obtained model can better simulate the swing motion of the underwater vehicle. Simulation and experimental results show a good consistency in free-fall tests during sea trials, as well as a prediction of the dive speed in the swing state.

Quantification of restricting factors of agricultural development in Min County of Gansu, China

Agricultural development in povertystricken areas is a major problem affecting agricultural modernization in China. This study discusses the restrictive factors affecting agricultural development in impoverished areas in China. A typical impoverished mountainous area, Min County,was selected for a case study. A regression analysis on the factors and characteristics of agricultural development in Min County between 1982 and 2017 was performed in this study. Taking agricultural output as the dependent variable, we selected nine main inputs of agricultural production in impoverished mountainous areas as the independent variables. Ridge regression analysis was carried out by testing for unit root and co-integration to verify the equilibrium relationship of the data. The results showed that the real Gross domestic product(GDP)per capita, the non-grain area ratio, the proportion of government expenditure on agriculture support to total expenditure, and the amount of chemical fertilizer applied in unit cultivated land area were the four most significant factors. The proportion of government expenditure on agriculture support to total expenditure was a negative influence, whereas the other three significant factors had a positive influence on agricultural output. This study highlights about the most significant factors affecting the agricultural development of impoverished mountainous regions in China.

Clean-energy utilization technology in the transformation of existing urban residences in China

Clean-energy substitution technology for existing residential buildings in cities is an inevitable choice for sustainable development and low-carbon ecological city construction.In this paper,the current status of energy-saving renovation and renewable-energy applications for existing residential buildings in various cities in China was summarized by using statistical methods.The geographical distribution of clean-energy power generation in primary energy production in China was explored in depth.According to different climatic divisions for existing urban residences,clean-energy production and consumption were analyzed and predicted based on the STIRPAT model.The results show that the energy consumption of urban residential buildings in 2016 increased by 43.6%compared with 2009,and the percentage of clean energy also increased from 7.9%to 13.4%.Different climatic regions have different advantages regarding clean energy:nuclear power generation leads in the region that experiences hot summers and warm winters,whereas wind and solar power generation lead in the cold and severely cold regions.The present results provide basic data support for the planning and implementation of clean-energy upgrading and transformation systems in existing urban residences in China.

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Rainfall prediction becomes popular in real time environment due to the developments of recent technologies.Accurate and fast rainfall predictive models can be designed by the use of machine learning(ML),statistical models,etc.Besides,feature selection approaches can be derived for eliminating the curse of dimensionality problems.In this aspect,this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression(CSMO-OKRR)model for accurate rainfall prediction.The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data.The proposed CSMO-OKRR technique encompasses three major processes namely feature selection,prediction,and parameter tuning.Initially,the CSMO algorithm is employed to derive a useful subset of features and reduce the computational complexity.In addition,the KRR model is used for the prediction of rainfall based on weather data.Lastly,the symbiotic organism search(SOS)algorithm is employed to properly tune the parameters involved in it.A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different measures.The simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.

Optimal decoupling control system using kernel method

A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the application of kernel method in decoupling multivariable output feedback controllers. Simulation results are presented to show the feasibility of the proposed technique.