Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

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.

基于Regression GAN的原油总氢物性预测方法

针对生成对抗网络(Generative adversarial network,GAN)不适用于原油物性回归预测的问题,本文提出一种回归生成对抗网络(Regression GAN,RGAN)结构,该结构由生成模型G、判别模型D及回归模型R组成.通过判别模型D与生成模型G间的对抗学习,D提取原油物性核磁共振氢谱(~1H NMR)谱图的潜在特征.首层潜在特征是样本空间的浅层表示利于解决回归问题,采用首层潜在特征建立回归模型R,提高了预测的精度及稳定性.通过增加条件变量和生成样本间的互信息约束,并采用回归模型R的MSE损失函数估计互信息下界,生成模型G产生更真实的样本.实验结果表明,RGAN有效地提高了原油总氢物性回归预测精度及稳定性,同时加快了生成模型的收敛速度,提高了谱图的生成质量.

核泄漏事故风险评估中的概率分析及预测

文章利用逻辑回归模型(logistic regression model,LRM)、线性判别模型(linear discriminant model,LDM)和支持向量机(support vector machine,SVM)3种统计模型,从核反应堆的内部和外部因素2个方面评估其在核泄漏事故中所体现的相关安全性能。针对每种模型,利用数理统计理论探究核反应堆相关影响因素与其发生核泄漏事故的概率。研究发现核反应堆外部因素有主导内部因素的趋势并在整个核泄漏事故风险中占有举足轻重的地位。文章提供的模型分析与预测结果可为核反应堆工程师及其相关决策者在核反应堆的选址、设计及建设运营等方面提供参考。

Lasso-Logistic回归模型拟合临床因素、NF-κB/NLRP3信号通路预测心肌梗死后缺血性心肌病价值

目的基于Lasso-Logistic回归分析心肌梗死后缺血性心肌病(ICM)影响因素,探讨临床因素、核因子-κB(NF-κB)/核苷酸结合寡聚结构域样受体家族3(NLRP3)信号通路及Lasso-Logistic回归模型对心肌梗死后ICM的预测价值,为本病防治提供参考。方法选取2020年9月—2023年9月秦皇岛市第一医院收治的342例心肌梗死患者为研究对象进行前瞻性研究,按照7∶3比例分为建模组239例、验证组103例,依据经皮冠状动脉介入术(PCI)术后6个月内是否发生ICM分为ICM亚组、非ICM亚组。采用Lasso筛选心肌梗死后ICM发生相关变量,以有统计学意义变量构建临床因素模型,以NF-κB/NLRP3信号通路构建NF-κB/NLRP3信号通路模型,以临床因素、NF-κB/NLRP3联合建立混合模型(Lasso-Logistic回归模型)。对比不同预测模型对心肌梗死后ICM的预测价值。结果建模组ICM发生率为27.97%,验证组ICM发生率为26.47%;Lasso筛选出5个预测变量为NF-kB mRNA、NLRP3 mRNA、Gensini评分、LVEF、饮酒,Logistic回归分析显示,Gensini评分、NLRP3 mRNA、NF-κB mRNA、饮酒是心肌梗死后ICM影响因素(P<0.05);混合模型预测心肌梗死后ICM的AUC、敏感度、特异度分别为0.921、80.30%、88.82%,临床因素模型分别为0.886、78.79%、85.29%,NF-κB/NLRP3信号通路模型分别为0.873、74.24%、87.06%,混合模型的AUC高于临床因素模型、NF-κB/NLRP3信号通路模型(P<0.05)。结论Gensini评分、NLRP3 mRNA、NF-κB mRNA、饮酒是心肌梗死后ICM危险因素,联合上述影响因素建立Lasso-Logistic回归模型,该模型对心肌梗死后ICM具有一定预测效能,有助于临床早期筛查高危人群,并予以相应干预措施,以降低ICM发生风险。

核电厂磷酸铁锂蓄电池热老化鉴定研究

参考某核电厂磷酸铁锂蓄电池系统预期使用场景,设计了大容量磷酸铁锂蓄电池热加速老化模型和热加速老化流程。选取三组蓄电池作为试验对象,分别开展了25℃、45℃和60℃下的热加速老化试验。通过数据分析,发现该蓄电池容量衰减率与储存时间的0.5次方呈线性变化规律。通过ln(c)~1/T的线性回归分析,得到了该蓄电池的活化能相关参数。进一步分析发现,该蓄电池在60℃下试验18.7天可等效在25℃下存储1年的规律。该研究成果可用于指导核电厂开展大容量磷酸铁锂蓄电池加速热老化试验。

应用于程序化操作的隔离开关位置自动确认算法

采用目前算法确认隔离开关的位置时,未对采集的图像进行相关预处理,导致算法存在时间复杂度高、定位误差大和定位检测率方差大的问题。提出应用于程序化操作的隔离开关位置自动确认算法,考虑连续函数的基础上,采用直方图均衡法对图像进行均衡处理,采用灰度变换方法,根据灰度直方图对隔离开关图像进行灰度拉伸变换,利用脊回归问题描述隔离开关位置的自动确认问题,采用目标样本的循环位移,通过二维高斯函数获取隔离开关图像对应的标签,并采用核相关滤波方法实现隔离开关位置的自动确认。实验结果表明,所提算法的时间复杂度低、定位误差小、定位检测率方差小。

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

Cold Nuclear Fusion in the Unitary Quantum Theory

The interaction of the charged particles in the new Unitary Quantum theory isconsidered. It is shown that the distance of approachment of deuterons to each other verystrongly depends on the phase of the wave function and not only upon the energy. This thesis isnot discussed in the conventional quantum theory. It can easily explain the experiments on thecold nuclear fusion.

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.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

人因对核电厂事故应急疏散行为的影响

通过问卷调查分析,获得核电站周围不同人群对核应急疏散的认知情况和行为特征,并且通过Logistic回归模型对人群疏散时可能发生的行为作出预测。采用卡方检验法和Logistic回归模型对该地区的不同人群的疏散心理和疏散行为等因素进行相关分析和影响判断,并且通过最优分配算法对模型预测正确性进行判断。结果表明:是否购买过核安全相关保险、参加过的核应急疏散演习次数、对核应急疏散的了解情况、家中有无核应急防护措施等因素对人群疏散意识和心理有显著影响;性别、对核应急疏散的了解情况、参加过的核应急疏散演习次数、家中是否有核应急防护措施和是否购买过核安全保险等因素对人群疏散行为有显著影响;仿真表明,具有一定核疏散经验的人群能做出相对正确的疏散行为,减少疏散时间。

Improved phenomenological nuclear charge radius formulae with kernel ridge regression

The kernel ridge regression(KRR)method with Gaussian kernel is used to improve the description of the nuclear charge radius by several phenomenological formulae.The widely used A^(1/3)A^(1/3),N^(1/3)N^(1/3)and Z^(1/3)Z^(1/3)formulae,and their improved versions by considering the isospin dependence are adopted as examples.The parameters in these six formulae are refitted using the Levenberg-Marquardt method,which give better results than the previous ones.The radius for each nucleus is predicted with the KRR network,which is trained with the deviations between experimental and calculated nuclear charge radii.For each formula,the resultant root-mean-square deviations of 884 nuclei with proton number Z≥8 Z≥8 and neutron number N≥8 N≥8 can be reduced to about 0.017fm after considering the modification of the KRR method.The extrapolation ability of the KRR method for the neutron-rich region is examined carefully and compared with the radial basis function method.It is found that the improved nuclear charge radius formulae by KRR method can avoid the risk of overfitting and have a good extrapolation ability.The influence of the ridge penalty term on the extrapolation ability of the KRR method is also discussed.At last,the nuclear charge radii of several recently observed K and Ca isotopes have been analyzed.

基于氢谱核磁共振的岭回归算法快速预测煎炸油氧化指标

通过氢谱核磁共振技术检测并分析大豆油煎炸时的氧化产物,经主成分分析后确定Z,E-共轭结构、E,E-共轭结构、直链饱和醛、(E,E)-2,4-二烯醛、(E)-2-烯醛5种特征基团作为特征矩阵,采用岭回归算法构建了煎炸油同时检测过氧化值与极性组分的快速预测模型,结果表明,交叉验证下正则化参数α为0.077,过氧化值与极性组分模型的决定系数(R2)分别为0.968与0.957,均方根残差(RMSE)分别为0.208与5.249,经重现性检验,预测值与国标值相对标准偏差小于5%,说明预测法重现性和精度良好。同时,采用两种方法检测时没有显著性差异,说明基于氢谱核磁共振和机器学习算法联用,构建氧化指标的预测模型,可为煎炸油品质监测指标提供一种新思路。

Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

基于Logistic-Nomogram构建创伤性脊髓损伤预后预测模型

目的:基于多因素Logistic回归分析创伤性脊髓损伤(TSCI)预后不良相关因素,构建Nomogram预测模型并进行验证。方法:选取2020年3月至2022年9月中国人民解放军联勤保障部队第九二〇医院收治的250例TSCI患者为研究对象,按照7∶3比例随机分为训练组(n=175)和验证组(n=75)。分别于治疗前及治疗后6个月采用日本骨科学会(JOA)量表评估患者预后,以JOA评分改善率≥60%为预后良好组,改善率<60%为预后不良组。采用单因素和多因素Logistic回归法分析预后不良的影响因素,根据影响因素构建Nomogram预测模型,并验证该模型预测效能及临床效用。结果:椎管侵占率≥50%、损伤严重程度为完全损伤、损伤至治疗时间≥8 h、外周血纤维蛋白原(FIB)水平降低、血清高迁移率族蛋白B1(HMGB1)和细胞核因子-κB(NF-κB)水平升高以及外周血中性粒细胞计数与淋巴细胞计数比值(NLR)增高均为预后不良的独立危险因素(P<0.05)。Nomogram预测模型预测预后不良的曲线下面积(AUC)为0.944,且具有正向净收益。结论:椎管侵占率、损伤程度、治疗时间及外周血HMGB1、NF-κB、NLR、FIB水平均为TSCI患者预后不良的危险因素,基于上述因素构建的Nomogram模型对预后不良有较好预测效能,有助于临床筛查高危人群并制定治疗方案。

汽油辛烷值损失的数学模型分析

降低汽油中的硫、烯烃含量,同时尽量保持其辛烷值是汽油清洁化的重点。由于炼油工艺过程的操作变量之间具有高度非线性及相互强耦联的关系,因此,本文在提高脱硫率的前提下,降低辛烷值损失的比例。采用3σ准则剔除异常值,用均值进行填充。再用核主成分分析方法,筛选与产品辛烷值和硫含量关联度高的30个变量,考虑降维的结果,赋予不同权重,建立量化评价模型,根据量化得分,确定出影响辛烷值预测的主要变量。最后利用岭回归和随机森林的方法,分别建立辛烷值损失预测模型,并对预测的结果进行了对比分析,发现岭回归方法的判定系数R2可达到0.927,预测精度较好。

Kinetics of thermal decomposition of lanthanum oxalate hydrate

Lanthanum oxalate hydrate La2（C2O4）3·10H2O,the precursor of La2O3 ultrafine powders,was prepared by impinging stream reactor method with PEG 20000 as surfactant.Thermal decomposition of La2（C2O4）3·10H2O from room temperature to 900 °C was investigated and intermediates and final solid products were characterized by FTIR and DSC-TG.Results show that the thermal decomposition process consists of five consecutive stage reactions.Flynn-Wall-Ozawa（FWO） and Kissinger-Akahira-Sunose（KAS） methods were implemented for the calculation of energy of activation（E）,and the results show that E depends on α,demonstrating that the decomposition reaction process of the lanthanum oxalate is of a complex kinetic mechanism.The most probable mechanistic function,G（α）=[1-（1＋α）1/3]2,and the kinetic parameters were obtained by multivariate non-linear regression analysis method.The average E-value that is compatible with the kinetic model is close to value which was obtained by FWO and KAS methods.The fitting curve matches the original TG curve very well.

核工业某研究院职工死亡危险因素的logistic回归研究

目的 对我国核工业某研究院职工的全死因及恶性肿瘤死亡进行多因素分析。方法 采用FoxPro建立关系型流行病学数据库 ,使用SPSS 9.0统计软件 ,对该研究院职工全死因和恶性肿瘤死亡进行非条件logistic回归模型分析。结果 1980年底的累积剂量未被选入方程 ;随访终止时的年龄、吸烟同全死因死亡呈正相关 (P均 <0 .0 5 ) ;性别 (P >0 .0 5 )、放射工龄 (P<0 .0 5 )同全死因死亡呈负相关。随访终止时的年龄、吸烟同恶性肿瘤死亡呈正相关 (P均 <0 .0 5 ) ;性别同恶性肿瘤死亡呈负相关 (P >0 .0 5 )。结论 从本文的多因素回归分析中 ,未见小剂量外照射所致的有害效应 ,但吸烟对该研究院职工全死因和恶性肿瘤死亡所带来的危险是不容忽视的。