Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.

基于Pearson相关性的VAR模型煤电水煤比寻优应用

本文为解决350MW燃煤机组在煤质频繁变化情况下水煤比失衡的问题,使用向量自回归(VAR)模型在机组参数波动工况下进行水煤比寻优。通过使用Pearson相关系数分析机组运行参数,确定寻优模型的输入数据,计算VAR模型最优阶数,并建立VAR模型从多个参数的时间序列寻找对应工况下水煤比最优值,同时使用三种不同分析方法确保该最优值的准确性和安全性。实践证明,该方法能有效解决机组由于煤质频繁变化导致锅炉给水策略不适配引发的主蒸汽温度和主蒸汽压力偏差大等问题。

Molecular Dynamics, Diffusion Coefficients and Activation Energy of the Electrolyte (Anode) in Lithium (Li and Li+), Sodium (Na and Na+) and Potassium (K and K+)

This work is a simulation modelling with the LAMMPS calculation code of an electrode based on alkali metals (lithium, sodium and potassium) using the MEAM potential. For different multiplicities, two models were studied;with and without gap. In this work, we present the structural, physical and chemical properties of the lithium, sodium and potassium electrodes. For the structural properties, the cohesive energy and the mesh parameters were calculated, revealing that, whatever the chemical element selected, the compact hexagonal hcp structure is the most stable, followed by the face-centred cubic CFC structure, and finally the BCC structure. The most stable structure is lithium, with a cohesion energy of -6570 eV, and the lowest bcc-hcp transition energy of -0.553 eV/atom, followed by sodium. For physical properties, kinetic and potential energies were calculated for each of the sectioned chemical elements, with lithium achieving the highest value. Finally, for the chemical properties, we studied the diffusion coefficient and the activation energy. Only potassium followed an opposite order to the other two, with the quantities with lacunae being greater than those without lacunae, whatever the multiplicity. The order of magnitude of the diffusion coefficients is given by the relationship DLi > DNa > Dk for the multiplicity 6*6*6, while for the activation energy the order is reversed.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Evaluation of the coefficient of lateral stress at rest of granular materials under repetitive loading conditions

Although the internal stress state of soils can be affected by repetitive loading,there are few studies evaluating the lateral stress(or K_(0))of soils under repetitive loading.This study investigates the changes in K_(0) and directional shear wave velocity(V_(s))in samples of two granular materials with different particle shapes during repetitive loading.A modified oedometer cell equipped with bender elements and a diaphragm transducer was developed to measure the variations in the lateral stress and the shear wave velocity,under repetitive loading on the loading and unloading paths.The study produced the following results:(1)Repetitive loading on the loading path resulted in an increase in the K_(0) of test samples as a function of cyclic loading number(i),and(2)Repetitive loading on the unloading path resulted in a decrease in K_(0) according to i.The shear wave velocity ratio(i.e.V_(s)(HH)/V_(s)(VH),where the first and second letters in parentheses corresponds to the directions of wave propagation and particle motion,respectively,and V and H corresponds to the vertical and horizontal directions,respectively)according to i supports the experimental observations of this study.However,when the tested material was in lightly over-consolidated state,there was an increase in K_(0) during repetitive loading,indicating that it was the initial K_(0),rather than the loading path,which is responsible for the change in K_(0).The power model can capture the variation in the K_(0) of samples according to i.Notably,the K_(0)=1 line acts as the boundary between the increase and decrease in K_(0) under repetitive loading.

Determination of Natural Logarithm of Diffusion Coefficient and Activation Energy of Thin Layer Drying Process of Ginger Rhizome Slices

This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy were determined. For this purpose, the experiments were done at six levels of varied temperatures: 10°C, 20°C, 30°C, 40°C, 50°C and 60°C. The values of effective diffusion coefficients obtained in this work for the variously treated ginger rhizomes closely agreed with the average effective diffusion coefficients of other notable authors who determined the drying kinetics and convective heat transfer coefficients of ginger slices.

Thermomechanical Dynamics (TMD) and Bifurcation-Integration Solutions in Nonlinear Differential Equations with Time-Dependent Coefficients

The new independent solutions of the nonlinear differential equation with time-dependent coefficients (NDE-TC) are discussed, for the first time, by employing experimental device called a drinking bird whose simple back-and-forth motion develops into water drinking motion. The solution to a drinking bird equation of motion manifests itself the transition from thermodynamic equilibrium to nonequilibrium irreversible states. The independent solution signifying a nonequilibrium thermal state seems to be constructed as if two independent bifurcation solutions are synthesized, and so, the solution is tentatively termed as the bifurcation-integration solution. The bifurcation-integration solution expresses the transition from mechanical and thermodynamic equilibrium to a nonequilibrium irreversible state, which is explicitly shown by the nonlinear differential equation with time-dependent coefficients (NDE-TC). The analysis established a new theoretical approach to nonequilibrium irreversible states, thermomechanical dynamics (TMD). The TMD method enables one to obtain thermodynamically consistent and time-dependent progresses of thermodynamic quantities, by employing the bifurcation-integration solutions of NDE-TC. We hope that the basic properties of bifurcation-integration solutions will be studied and investigated further in mathematics, physics, chemistry and nonlinear sciences in general.

基于Pearson相关系数与广义S变换的低压直流微电网的故障选线方法

针对低压直流微电网线路的故障选线方法的研究有限,提出一种基于Pearson相关系数与广义S变换的故障选线方法。首先,介绍了低压直流微电网的一般组成。在此基础上,研究了低压直流微电网的单极故障以及极间故障的故障特征,提出选线方法,对各线路正负极首末端电流差进行Pearson相关系数计算来确定故障线路。然后对所选故障线路正负线路首端的电流量进行广义S变换,计算出其能量和的比值判别故障极性或者极间故障。最后,在PSCAD/EMTDC中搭建出低压直流微电网模型以输出各线路电流数据,并利用Matlab对数据进行仿真。结果表明,所提选线方法直接有效,且耐受过渡电阻的能力较强。

基于Pearson系数和萤火虫算法优化BP神经网络的住宅价格预测模型

为进一步提高住宅价格预测精度,进而为有关部门提供相关数据参考,帮助其及时准确地制定相关政策,提出了基于Pearson系数和萤火虫算法优化BP神经网络的住宅价格预测模型。该模型首先使用Pearson系数对影响房价的相关因素进行特征筛选,舍弃与住宅价格关联性不强的因素;然后使用萤火虫算法对BP神经网络进行优化,建立基于萤火虫算法优化的BP神经网络预测模型;最后以上海市统计局最新发布的相关数据进行实验验证。实验结果表明,所提模型优于其他5种模型,能够实现住宅价格的有效预测。

Pearson-Ⅲ型负偏频率曲线的权函数参数估计方法

Pearson-Ⅲ型水文频率计算以正偏序列为主,但在处理水库水位、潮位序列时常出现负偏的情况。针对正偏序列的权函数参数估计方法,通过公式推导将其拓展应用到负偏序列,完善了这一水文参数估计方法体系。统计试验表明相比常规矩法,权函数参数估计方法能显著提高对Cs估计的无偏性,并在有效性方面有所提升。比较权函数框架下的三种参数估计方法,发现二阶权函数矩法(WFM2)整体优于一阶权函数矩法(WFM1),利用权函数矩结合法(WFM3)对Cv(或σ)进行估计的前提是要确保Cs的估计优于Cv,否则其不如WFM1和WFM2的参数估计结果。以某潮位站年最高潮位序列和某水文站年最高水位序列进行了实例验证,拟合优度检验表明WFM2方法的拟合误差小、精度高,是所有比较方法中应用效果最好的方法。

基于聚类分析和Pearson相关系数法的电网负荷数据清洗与去重

针对电网负荷数据存在冗余和价值密度低等问题,本文提出一种结合K-means算法与Pearson相关系数计算的集成学习方法,对负荷数据进行清洗与去重。设置仿真实验将某地区连续730日的负荷数据进行聚类、切片、排序、比对、去重等分析处理,得到清洗后的新数据集,将新数据集与原数据集代入相同的BP神经网络模型和随机森林模型进行负荷预测,实验结果表明新旧数据集具有相似的特征特性与数据挖掘潜力。与传统的数据去重方法相比,本文提出的数据清洗策略在进行训练集的预处理时,效率和准确度方面均有更好表现,可以为训练用于负荷预测的网络模型提供支持。

Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

基于Pearson-KPCA和LSTM的伺服电机滚动轴承剩余寿命预测

针对伺服电机滚动轴承的寿命预测,提出一种基于皮尔逊相关系数及核主成分分析的长短时记忆网络预测方法。提取滚动轴承的时、频域信号,通过移动平均法进一步获取相关特征,并采用皮尔逊相关系数筛选高度相关特征指标,利用KPCA提取高度相关特征指标中的若干主成分;将第一主成分作为长短时记忆网络模型的输入对滚动轴承进行剩余寿命预测。采用IMS轴承数据集进行验证,得到的轴承寿命预测RMSE值和可决策系数值分别为0.0543和0.989。将其与长短期记忆网络模型和BP神经网络的预测结果进行对比,证明所提方法具有较高的精度。

基于Pearson相关性的雷达交叠信号识别

针对复杂电磁环境下多部雷达同时到达信号频域交叠时难以分选识别的问题,分析了信号交叠对于雷达信号识别模块性能的影响;在此基础上面向识别需求,基于Pearson算法研究了交叠信号分选的处理方法。从信息论视角来说,两个信号脉冲波形相关性越弱,则其所表达的信息量越大,特征相关度越低。在信号脉冲数一定时,基于Pearson算法可以选择相关性较弱的信号脉冲载波,以削弱同时信号交叠的影响。通过实验,发现利用选择后的弱相关信号脉冲载波特征来表达信号特性,能有效降低数据维度,尽可能减少信息丢失,有利于达到鲁棒的识别效果,因此该方法可为同时到达信号交叠下的信号识别提供有效途径参考。

Advances in joint roughness coefficient (JRC) and its engineering applications

The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has the advantages of its simple form, easy estimation, and explicit consideration of scale effects, which make it the most widely accepted parameter for roughness quantification since it was proposed. As a result, JRC has attracted the attention of many scholars who have developed JRC-related methods in many areas, such as geological engineering, multidisciplinary geosciences, mining mineral processing, civil engineering, environmental engineering, and water resources. Because of such a developing trend, an overview of JRC is presented here to provide a clear perspective on the concepts, methods, applications, and trends related to its extensions. This review mainly introduces the origin and connotation of JRC, JRC-related roughness measurement, JRC estimation methods, JRC-based roughness characteristics investigation, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications. Moreover, the representativeness of the joint samples and the determination of the sampling interval for rock joint roughness measurements are discussed. In the future, the existing JRC-related methods will likely be further improved and extended in rock engineering.

Prediction models of the ionization coefficient and ionization cross-section based on multi-layer molecular parameters

Prediction models were proposed to estimate the reduced Townsend ionization coefficient and ionization cross-section.A shape function of the reduced Townsend ionization coefficient curves was derived from the ionization collision probability model.The function had three parameters:the first ionization potential energy,A_(α),and B_(α).A_(α)and B_(α)were related to the molecule symmetry and size.The polarization of molecules could characterize the molecule symmetry.The multi-layer molecular cross-section(MMCS)was proposed to describe the contributions of electrons and molecule radius on different molecule surfaces to collisions.A prediction model of the ionization cross-section was also proposed based on Aα.The molecule parameters were calculated by the Becke3–Lee–Yang–Parr(B3LYP)method and the 6–311G**basis set.We used available data of 30 and 23 gases,respectively,to build the prediction models of reduced Townsend ionization coefficients and ionization cross-sections.The relationships between the molecular parameters Aαand Bαand the ionization cross-section were built up via nonlinear fittings.The determination coefficients R^(2)of Aα,Bα,and the ionization cross-section were 0.877,0.887,and 0.838,respectively.The results showed that the accuracy of models was positively correlated with the molecule symmetry and reduced electric field.This was mainly related to the accuracy of the MMCS model in predicting Aα.The MMCS model needed to be improved to describe the collision direction selectivity caused by the molecule asymmetry.Under a high reduced electric field,that error of Aαhad less influence on the prediction results.However,the prediction results for single atoms with high symmetry were poor.This may be due to the absolute error of the model close to single atoms’reduced Townsend ionization coefficients.The models could provide the basis for gas insulation prediction and discharge calculations,especially for symmetric molecules under a high electric field.

Analysis of burnup performance and temperature coefficient for a small modular molten‑salt reactor started with plutonium

In a thorium-based molten salt reactor(TMSR),it is difficult to achieve the pure 232Th–^(233)U fuel cycle without sufficient^(233)U fuel supply.Therefore,the original molten salt reactor was designed to use enriched uranium or plutonium as the starting fuel.By exploiting plutonium as the starting fuel and thorium as the fertile fuel,the high-purity^(233)U produced can be separated from the spent fuel by fluorination volatilization.Therefore,the molten salt reactor started with plutonium can be designed as a^(233)U breeder with the burning plutonium extracted from a pressurized water reactor(PWR).Combining these advantages,the study of the physical properties of plutonium-activated salt reactors is attractive.This study mainly focused on the burnup performance and temperature reactivity coefficient of a small modular molten-salt reactor started with plutonium(SM-MSR-Pu).The neutron spectra,^(233)U production,plutonium incineration,minor actinide(MA)residues,and temperature reactivity coefficients for different fuel salt volume fractions(VF)and hexagon pitch(P)sizes were calculated to analyze the burnup behavior in the SM-SMR-Pu.Based on the comparative analysis results of the burn-up calculation,a lower VF and larger P size are more beneficial for improving the burnup performance.However,from a passive safety perspective,a higher fuel volume fraction and smaller hexagon pitch size are necessary to achieve a deep negative feedback coefficient.Therefore,an excellent burnup performance and a deep negative temperature feedback coefficient are incompatible,and the optimal design range is relatively narrow in the optimized design of an SM-MSR-Pu.In a comprehensive consideration,P=20 cm and VF=20%are considered to be relatively balanced design parameters.Based on the fuel off-line batching scheme,a 250 MWth SM-MSR-Pu can produce approximately 29.83 kg of ^(233)U,incinerate 98.29 kg of plutonium,and accumulate 14.70 kg of MAs per year,and the temperature reactivity coefficient can always be lower than−4.0pcm/K.

Improvement of Similarity Measure： Pearson Product-Moment Correlation Coefficient

Aim To study the reason of the insensitiveness of Pearson preduct-moment correlation coefficient as a similarity measure and the method to improve its sensitivity. Methods Experimental and simulated data sets were used. Results The distribution range of the data sets influences the sensitivity of Pearson product-moment correlation coefficient. Weighted Pearson product-moment correlation coefficient is more sensitive when the range of the data set is large. Conclusion Weighted Pearson product-moment correlation coefficient is necessary when the range of the data set is large.