基于Stacking融合模型的PHEV复合储能系统实时能量分配策略

为了解决插电式混合动力汽车单一电池低比功率、无法响应暂态功率需求的问题,设计了电池和超级电容并联式复合储能系统。同时针对采用动态规划法优化负载电流分配时缺乏实时性的问题,利用不同驱动工况下动态规划优化的结果构成训练集进行训练,并综合GRU网络以及XGBoost算法,提出了一种Stacking集成学习框架下多模型融合的能量分配策略。仿真结果表明,与仅使用单一电池的储能系统相比,基于Stacking融合模型的实时能量分配系统在UDDS和US06两种循环工况下,电池峰值电流分别降低了48.7%和50.8%,有效削弱了电池的峰值电流,提升了电池的整体性能。

Transformation of long-period stacking ordered structures in Mg-Gd-Y-Zn alloys upon synergistic characterization of first-principles calculation and experiment and its effects on mechanical properties

Based on experiments and first-principles calculations,the microstructures and mechanical properties of as-cast and solution treated Mg-10Gd-4Y-xZn-0.6Zr(x=0,1,2,wt.%)alloys are investigated.The transformation process of long-period stacking ordered(LPSO)structure during solidification and heat treatment and its effect on the mechanical properties of experimental alloys are discussed.Results reveal that the stacking faults and 18R LPSO phases appear in the as-cast Mg-10Gd-4Y-1Zn-0.6Zr and Mg-10Gd-4Y-2Zn-0.6Zr alloys,respectively.After solution treatment,the stacking faults and 18R LPSO phase transform into 14H LPSO phase.The Enthalpies of formation and reaction energy of 14H and 18R LPSO are calculated based on first-principles.Results show that the alloying ability of 18R is stronger than that of 14H.The reaction energies show that the 14H LPSO phase is more stable than the 18R LPSO.The elastic properties of the 14H and 18R LPSO phases are also evaluated by first-principles calculations,and the results are in good agreement with the experimental results.The precipitation of LPSO phase improves the tensile strength,yield strength and elongation of the alloy.After solution treatment,the Mg-10Gd-4Y-2Zn-0.6Zr alloy has the best mechanical properties,and its ultimate tensile strength and yield strength are 278.7 MPa and 196.4 MPa,respectively.The elongation of Mg-10Gd-4Y-2Zn-0.6Zr reaches 15.1,which is higher than that of Mg-10Gd-4Y0.6Zr alloy.The improving mechanism of elastic modulus by the LPSO phases and the influence on the alloy mechanical properties are also analyzed.

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's information.To avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and efficiency.Based on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is proposed.In order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are considered.The hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the URL.The simulation results show that the proposed hybrid model is more efficient than the existing detection methods.It produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS attacks.In order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high capacity.However,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage performance.Herein,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking faults.This structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p orbitals.Moreover,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than Cf-Ls.This study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field data.We show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

Predicting depression in patients with heart failure based on a stacking model

BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.

Correlation of work function and stacking fault energy through Kelvin probe force microscopy and nanohardness in diluteα-magnesium

Electronic interactions of the Group 2A elements with magnesium have been studied through the dilute solid solutions in binary Mg-Ca,Mg-Sr and Mg-Ba systems.This investigation incorporated the difference in the‘Work Function'(ΔWF)measured via Kelvin Probe Force Microscopy(KPFM),as a property directly affected by interatomic bond types,i.e.the electronic structure,nanoindentation measurements,and Stacking Fault Energy values reported in the literature.It was shown that the nano-hardness of the solid-solutionα-Mg phase changed in the order of Mg-Ca>Mg-Sr>Mg-Ba.Thus,it was shown,by also considering the nano-hardness levels,that SFE of a solid-solution is closely correlated with its‘Work Function'level.Nano-hardness measurements on the eutectics andΔWF difference between eutectic phases enabled an assessment of the relative bond strength and the pertinent electronic structures of the eutectics in the three alloys.Correlation withΔWF and at least qualitative verification of those computed SFE values with some experimental measurement techniques were considered important as those computational methods are based on zero Kelvin degree,relatively simple atomic models and a number of assumptions.As asserted by this investigation,if the results of measurement techniques can be qualitatively correlated with those of the computational methods,it can be possible to evaluate the electronic structures in alloys,starting from binary systems,going to ternary and then multi-elemental systems.Our investigation has shown that such a qualitative correlation is possible.After all,the SFE values are not treated as absolute values but rather become essential in comparative investigations when assessing the influences of alloying elements at a fundamental level,that is,free electron density distributions.Our study indicated that the principles of‘electronic metallurgy'in developing multi-elemental alloy systems can be followed via practical experimental methods,i.e.ΔWF measurements using KPFM and nanoindentation.

Analysis and Response of Steel Stacking Accidents in Hot Rolling Coilers

Through long-term production and maintenance practice,various types of stacking steel in the 2250mm hot rolling coiler of Ma Steel were tracked and analyzed,and the causes of stacking steel were summarized.Cor-responding measures were formulated to effectively reduce the probability of stacking steel.

基于Stacking集成学习的热轧带钢凸度诊断模型

在热连轧生产过程中,凸度是重要的质量指标,过程数据的非平衡性限制了数据驱动模型的预测效果,为提高模型的预测精度,提出一种融合SMOTE和Stacking集成算法的热轧带钢凸度诊断模型。首先,采用SMOTE过采样方法处理凸度相关数据集,降低数据非平衡分布导致的影响;然后,构建以轻量级梯度提升机(LightGBM)、支持向量机(SVM)、K近邻(KNN)和随机森林(RF)为基学习器,逻辑回归(LR)为元学习器的Stacking集成模型,最后,使用某2160 mm热轧带钢实际生产数据进行模型验证。研究结果表明,诊断模型的准确率、少数类召回率、平衡F分数、几何平均值和ROC曲线下面积分别为0.9580、0.9595、0.9573、0.9589和0.9579,与XGBoost、LightGBM、KNN、SVM和随机森林模型对比,预测效果最优,证明了Stacking集成算法能够有效增强诊断模型的泛化能力,具有优良的诊断性能。

近红外光谱结合Stacking集成学习的猕猴桃糖度检测研究

利用近红外光谱技术Stacking集成学习对猕猴桃糖度的无损检测。以湖北“云海一号”猕猴桃为研究对象,采用红外分析仪获取了280个样本的光谱数据,包含了4000~10000cm^(-1)范围内的1557个波长数据,使用折射仪测量糖度值。通过蒙特卡洛随机采样结合T检验的奇异样本识别算法筛除异常值样本。利用SPXY算法按照4∶1的比例划分训练集和测试集。使用多元散射校正(MSC)、SG平滑滤波(SG)、趋势校正(DT)、矢量归一化(VN)、标准正态变换(SNV)五种方法对数据进行预处理。使用无信息变量消除法(UVE)、竞争性自适应重加权算法(CARS)和区间变量迭代空间收缩特征选择算法(iVISSA)提取特征波长,使用连续投影算法(SPA)进行二次提取,消除共线性变量。由于单一模型的泛化能力有限,为了扩大建模能力,设计了一种基于Stacking算法的集成学习模型。选择贝叶斯岭回归(BRR)、偏最小二乘回归(PLSR)、支持向量机回归(SVR)以及人工神经网络(ANN)作为基学习器,线性回归(LR)作为元学习器建立集成模型,比较不同组合下集成模型的性能。使用Pearson相关系数分析基学习器与集成模型之间的关系。结果表明:在五种预处理方法之中,矢量归一化的效果最佳。对预处理后的光谱进行特征波长提取,结果显示VN-CARS-PLSR模型效果最好,在测试集上的RP2为0.805,RMSEP为0.498。模型提取了177个特征波长,数据量相比于原始光谱减少了88.6%。通过Stacking算法对基学习器进行融合,对比不同的组合方式,发现PLS+SVR+ANN集成模型预测精度最高,RP2达到了0.853,RMSEP下降至0.433。通过Pearson相关系数分析了基学习器对集成模型性能的影响。研究表明,与单一模型相比,Stacking集成模型能够进行更加全面的建模,具有更高的泛化能力,该方法为猕猴桃糖度品质的无损检测提供了技术支持。

基于Stacking融合模型的Web攻击检测方法

随着计算机技术与互联网技术的飞速发展,Web应用在人们的生产与生活中扮演着越来越重要的角色。但是在人们的日常生活与工作中带来了更多便捷的同时,却也带来了严重的安全隐患。在开发Web应用的过程中,大量不规范的新技术应用引入了很多的网站漏洞。攻击者可以利用Web应用开发过程中的漏洞发起攻击,当Web应用受到攻击时会造成严重的数据泄露和财产损失等安全问题,因此Web安全问题一直受到学术界和工业界的关注。超文本传输协议(HTTP)是一种在Web应用中广泛使用的应用层协议。随着HTTP协议的大量使用,在HTTP请求数据中包含了大量的实际入侵,针对HTTP请求数据进行Web攻击检测的研究也开始逐渐被研究人员所重视。本文提出了一种基于Stacking融合模型的Web攻击检测方法,针对每一条文本格式的HTTP请求数据,首先进行格式化处理得到既定的格式,结合使用Word2Vec方法和TextCNN模型将其转换成向量化表示形式;然后利用Stacking模型融合方法,将不同的子模型(使用配置不同尺寸过滤器的Text-CNN模型搭配不同的检测算法)进行融合搭建出Web攻击检测模型,与融合之前单独的子模型相比在准确率、召回率、F1值上都有所提升。本文所提出的Web攻击检测模型在公开数据集和真实环境数据上都取得了更加稳定的检测性能。

基于组合时域特征提取和Stacking集成学习的燃煤锅炉NO_(x)排放浓度预测

为提高火电厂锅炉出口NO_(x)排放浓度的预测精度,提出一种考虑组合时域特征的Stacking集成学习模型。首先,为挖掘数据深层信息,采用时序分析、完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise analysis,CEEMDAN)和统计学计算数据标准差、偏度等特征的方法进行组合时域特征提取以构建重构数据;其次,考虑到重构数据中存在的冗余变量对模型的精度有所影响,利用遗传算法(genetic algorithm,GA)对重构数据进行特征降维;最后,为充分发挥各个模型的优势以提高模型的预测精度,构建以极限学习机(extreme learning machines,ELM)、深度神经网络(deep neural networks,DNN)、多层感知器(multilayer perceptron,MLP)、极限梯度提升算法(extreme gradient boosting,XGBoost)为基模型和以回声状态网络(echo state network,ESN)为元模型的Stacking集成学习NOx排放浓度预测模型。实验结果表明:该预测模型在不同数据集下都有着不错的预测效果,预测误差均小于2%,能够对锅炉NOx排放浓度实现精准预测。

基于FIR-Stacking的刀具磨损预测

针对铣刀加工工件时传感器信号存在噪声、单一传统机器学习模型预测效果不理想的问题,提出一种基于自适应FIR滤波器和Stacking集成模型的刀具磨损预测方法。首先,采用自适应FIR滤波器去噪,计算时域、频域和时频域常用统计量作为信号特征,并对同一信号的多源信号特征进行拼接,经Pearson相关系数筛选保留相关系数大于0.2的特征;最后,以LightGBM、支持向量回归(support vector regression,SVR)、多层感知机(multilayer perceptron,MLP)作为基模型,Lasso作为元模型,构建Stacking集成模型进行刀具磨损预测。使用铣削加工数据集进行验证,结果表明该方法可有效提高预测准确性。

基于Stacking算法集成学习的页岩油储层总有机碳含量评价方法

总有机碳含量(TOC)是页岩油储层评价的重要参数,而传统总有机碳含量测井评价方法精度较低且普适性较差,机器学习模型在一定程度上提高了总有机碳含量预测精度,但结果存在不稳定性。为了进一步提高页岩油储层总有机碳含量预测精度,基于有机质岩石物理特征和不同总有机碳含量测井响应特征的深入分析,优选出深侧向电阻率、声波时差、补偿中子和密度测井曲线作为总有机碳含量的敏感测井响应,并将其作为输入特征,以岩心分析总有机碳含量作为期望输出值,分别建立了决策树模型、支持向量回归机模型、BP(Back Propagation)神经网络模型,并建立了以决策树模型为基模型、支持向量回归机模型为元模型的Stacking算法集成学习模型。利用B油田A区块的岩心样本数据和实际井数据对不同模型预测总有机碳含量结果进行了验证,结果表明,基于Stacking算法的集成学习模型的总有机碳含量预测精度最高,相较于决策树模型、支持向量回归机模型、BP神经网络模型和改进的ΔlgR法,预测精度有较大提高。因此,基于Stacking算法的集成学习模型为该研究区最有效的总有机碳含量计算方法,这为准确地评估页岩油储层的生烃潜力、确保页岩油储层的高效开采及资源利用奠定了基础。

基于Stacking集成学习的声波时差测井曲线复原研究

声波时差测井曲线在石油勘探中发挥着不可或缺的作用,但是受地质或仪器的影响,经常会出现部分甚至完整的声波测井曲线缺失的情况。针对这一问题,提出了一种基于Stacking集成学习的声波时差测井曲线复原方法,该模型使用随机森林(RF)、梯度提升决策树(GBDT)、轻量梯度提升机(LightGBM)和极限梯度提升(XGBoost)作为基学习器,支持向量回归(SVR)作为元学习器,同时采用5折交叉验证的方法。实验选取了大庆油田某区块的实际测井数据,分别进行了同井和异井间的缺失声波时差测井曲线复原实验,结果表明,所提方法比单一模型预测更加准确,验证了此方法的可行性。

基于Stacking集成算法的抛石护岸水毁破坏预测研究

抛石护岸在顶冲等极端情况下易发生水毁破坏,给人民的生命财产带来威胁。通过水槽试验获取496组样本数据,利用互信息(MI)筛选出6个关键特征属性,并采用支持向量机(SVR)、广义回归神经网络(GRNN)和随机森林(RF)等机器学习算法构建多个预测模型。然后,将这些模型作为基学习器,结合BP神经网络(BPNN)作为元学习器,采用Stacking集成学习方法构建抛石护岸破坏程度预测模型。最后,通过决定系数(R^(2))、均方根误差(R_(RMSE))及平均绝对误差(M_(MAE))等评价指标对模型性能进行评估。结果表明,Stacking模型在抛石护岸破坏高度、长度、范围上的平均R^(2)为0.98、RRMSE为0.02、M_(MAE)为0.03,相较于单一模型(SVR、GRNN、RF),Stacking模型的R_(RMSE)、M_(MAE)皆为最小,R2最高。在抛石护岸水毁破坏程度的预测中,融合的Stacking模型展现出更高的准确性与稳定性。

基于Stacking模型的学术论文多标签分类系统构建

学术论文高质量多标签自动分类是推动学术研究发展的关键程序之一。本研究利用Stacking模型将随机森林、支持向量机、极限树、极端梯度提升和神经网络五个分类器融合为一个异质集成分类器,并利用基于问题转换思想的多二分类模型将该分类器应用于学术论文多标签分类。根据学术论文的特点,依次实现了与之配套的论文特征提取模块、TF-IDF加权模块、数据预处理模块,最终构建成一个面向学术论文的多标签分类系统。仿真实验验证了本研究构建的学术论文多标签分类系统在处理学术论文多标签分类问题时,较传统的单模型分类器或同质集成模型分类器在泛化能力、稳定性与准确率方面都有一定程度的提升。图9。参考文献21。

基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型

针对风电场短期风速预测准确度不高的问题,提出一种基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型。首先,为突出短期风速的局部特征并降低建模难度,通过互补集成经验模态分解算法将短期风速分解为若干个稳定分量。然后,利用信息熵和近似熵来判定各分量的复杂度,高复杂度分量选择最小二乘支持向量机、低复杂度分量选择随机配置网络作为对应的预测模型。利用Stacking算法对每个模型的预测值进行融合,使预测精度得到提升。最后,通过一组实际的短期风速数据作为研究对象,将提出的预测模型应用于其预测。对比结果表明,所提预测模型可提高短期风速的预测精度。