A Multiple Model Approach to Modeling Based on Fuzzy Support Vector Machines

A new multiple models(MM) approach was proposed to model complex industrial process by using Fuzzy Support Vector Machines(F -SVMs). By applying the proposed approach to a pH neutralization titration experiment, F -SVMs MM not only provides satisfactory approximation and generalization property, but also achieves superior performance to USOCPN multiple modeling method and single modeling method based on standard SVMs.

Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through Support Vector Machine (SVM) Algorithm

Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the Support Vector Machine(SVM)algorithm.This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran.The participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each group.The study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the models.The collected data were fed into SPSS 18,followed by conducting linear regression and paired samples t-test.It was revealed that,in the first model(SPL<70 dBA),the frequency of 8 KHz had the greatest impact(with a weight of 33%),while noise had the smallest influence(with a weight of 5%).The accuracy of this model was 100%.In the second model(70<SPL<80 dBA),the frequency of 4 KHz had the most profound effect(with a weight of 21%),whereas the frequency of 250 Hz had the lowest impact(with a weight of 6%).The accuracy of this model was 100%too.In the third model(SPL>85 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%too.In the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the frequency of 500 Hz had the smallest effect(with a weight of 4%).The accuracy of this model was found to be 94%.According to the modeling conducted using the SVM algorithm,the frequency of 4 KHz has the most profound effect on predicting changes in hearing loss.Given the high accuracy of the obtained model,this algorithm is an appropriate and powerful tool to predict and model hearing loss.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification

To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.

Using Optimized Distributional Parameters as Inputs in a Sequential Unsupervised and Supervised Modeling of Sunspots Data

Detecting naturally arising structures in data is central to knowledge extraction from data. In most applications, the main challenge is in the choice of the appropriate model for exploring the data features. The choice is generally poorly understood and any tentative choice may be too restrictive. Growing volumes of data, disparate data sources and modelling techniques entail the need for model optimization via adaptability rather than comparability. We propose a novel two-stage algorithm to modelling continuous data consisting of an unsupervised stage whereby the algorithm searches through the data for optimal parameter values and a supervised stage that adapts the parameters for predictive modelling. The method is implemented on the sunspots data with inherently Gaussian distributional properties and assumed bi-modality. Optimal values separating high from lows cycles are obtained via multiple simulations. Early patterns for each recorded cycle reveal that the first 3 years provide a sufficient basis for predicting the peak. Multiple Support Vector Machine runs using repeatedly improved data parameters show that the approach yields greater accuracy and reliability than conventional approaches and provides a good basis for model selection. Model reliability is established via multiple simulations of this type.

基于深度自回归模型的电网异常流量检测算法

针对电网中行为种类复杂多样且数量众多的问题,提出了一种基于自回归模型的电网异常流量检测算法。该算法利用深度自编码网络自动提取网络流量数据的特征,降低异常流量检测的分析周期,并自动挖掘数据的层次关系。通过支持向量机对提取的特征进行分类,实现对异常流量的检测。仿真实验结果表明,所提算法可以分析不同攻击向量,避免噪声数据的干扰,进而提高电网异常流量检测的精度,对于流量数据处理具有重要意义。

基于双信号融合的主轴/刀柄结合面刚度退化程度预测

为了预测主轴/刀柄结合面刚度退化程度,提出了一种基于激励和响应信号融合的主轴/刀柄结合面刚度退化程度预测方法。首先进行钛合金矩形工件侧铣实验,采集瞬时铣削力信号和主轴/刀柄结合面附近的响应振动信号,构建反映主轴/刀柄结合面刚度退化的数据库。然后根据数据库中瞬时铣削力和振动信号各方向的时域、频域和时频域特征,基于相关性分析优选出瞬时铣削力信号和振动信号的时域均值、频域中心频率、时频域一阶小波包能量3个特征,分别使用低频滤波卷积核和高频滤波卷积核对优选后的特征矩阵进行双通道卷积池化处理,获取深度融合的主轴/刀柄结合面刚度退化程度特征向量。最后以支持向量机模型(SVM)的概率模式转化为朴素贝叶斯分类器(NBC)的条件概率,构建混合分类器模型(NBC-SVM),提高了分类器的分类性能。在主轴/刀柄结合面刚度退化数据库的基础上,基于双通道卷积池化的特征融合方法(CP-FF)和NBC-SVM模型实现了主轴/刀柄结合面刚度退化程度的预测,预测精度达96%。

基于全范围头部姿态估计的教师注意力识别算法

探究教师注意力对于评估课堂教师行为具有极其重要的研究价值。然而,现有的教师注意力识别算法存在无法应对极端头部姿态角度等问题。为此,提出一种基于6DRep Net360模型的教师注意力状态识别算法,提升极端角度中头部姿态估计算法的准确性。相较于传统的依赖条件判断来分类教师注意力状态的方法,设计一种基于支持向量机(SVM)的教师注意力分类模型,对复杂头部姿态角度进行注意力状态的精准识别。为进一步解决算法稳定性和准确性带来的误差数据,提出基于滑动窗口的数据清洗算法,有效提高整体识别结果的真实性和可靠性。通过在构建的CCNUTeacherS tat e数据集上进行一系列的算法评估,实验结果表明,所提出的教师注意力识别算法在CCNUTeacherS tate数据集上达到了90.67%的准确率。

基于多元统计分析的小样本数据预测模型设计

若小样本数据预测误差较大,会直接影响数据应用效果,为提升小样本数据预测精度,提出基于多元统计分析的小样本数据预测模型设计方法。将小样本数据放入SPSS软件中,结合自助法完成小样本数据的经验分布分析。基于样本数据经验分布特征,结合具备学习能力的Fisherface算法对小样本上数据实施预分类,建立测试样本类别标签,实现小样本数据的特征提取。通过多元统计分析数据特征的主元成分,确定模型回归函数,结合支持向量机构建数据预测模型,通过上述模型完成小样本数据的精准预测。实验结果表明,使用上述方法开展小样本数据预测时,预测误差较低,效率较高,说明其预测效果较好。

EHDE和WHO-SVM模型在齿轮箱故障诊断中的应用

针对现有齿轮箱故障诊断方法对数据长度敏感的缺陷,提出了一种基于增强层次多样性熵(EHDE)和野马算法(WHO)优化支持向量机(SVM)的齿轮箱故障诊断模型。首先,传统熵值特征提取方法在特征提取阶段对数据样本的长度比较敏感,为此提出了增强层次多样性熵,并将其作为特征提取指标用于提取齿轮箱的故障特征;其次,采用WHO算法对SVM模型的参数进行了优化,建立了参数最优的WHO-SVM分类器;最后,将故障特征样本输入至WHO-SVM分类器中进行了训练和识别,完成了样本的故障识别;利用齿轮箱数据集分别从数据长度敏感性、算法特征提取时间、模型诊断性能三种角度对EHDE、精细复合多尺度样本熵、精细复合多尺度模糊熵、精细复合多尺度排列熵、精细复合多尺度散布熵、精细复合多尺度波动散布熵进行了对比研究。研究结果表明:EHDE方法对数据长度的要求较低,在数据长度为512时即可以取得99.1%的平均识别准确率,在诊断稳定性和诊断精度方面均优于其他对比方法;在算法的泛化性实验中,EHDE方法能够以98%的准确率识别齿轮箱的不同故障类型,具有明显的泛化性和通用性。

基于水资源空间均衡的“四水四定”调控模型构建

为实现未来不同时间尺度下的水资源空间均衡与动态调控,创建了一套完整严谨、可动态调控的“四水四定”模型体系。通过模糊信息粒化窗口的支持向量机模型预测区域未来总用水量,利用基于时间序列相似性分析的自回归支持向量机模型预测区域未来分用水量,并对两类数据进行不确定性分析;构建了复杂回归函数对各类用水指标进行情景预测,经统计检验后将其作为当前用水模式下未来用水指标;构建了“四水四定”水资源承载力模型和水资源空间均衡模型,基于未来总用水量、未来各分用水量、未来用水指标,选用水资源负载系数、用水效益和水土资源匹配系数3个指标,结合基尼系数量化水资源空间均衡度,分析当前用水模式下未来水资源均衡度;构建了最优化模型,以最小化基尼系数为目标函数调整未来用水模式,实现水资源动态调控。所创建的模型体系可以实现未来不同时间尺度下的水资源空间均衡与动态调控。

基于集成特征选择的中小微企业信贷风险分类模型研究

文章以客户违约率作为中小微企业信用风险的评价标准,尝试构造基于集成特征选择的中小微企业信用风险分类模型,结合互信息矩阵、基于k折交叉验证的随机森林和支持向量机对模型进行分析。研究表明企业的信誉等级、销项有效率和最高销项对信用风险有显著影响,其他因素对信用风险的影响不显著,实验说明基于k折交叉验证的支持向量机具有可靠的信贷风险预测能力,对中小微企业信用风险评估有较强的参考价值。

基于鲸鱼优化算法-支持向量回归的汽车运动状态估计

为了不依赖动力学模型精度而准确地获取车辆运动状态信息,提出一种基于鲸鱼优化算法-支持向量回归(WOA-SVR)的车辆状态估计算法。首先通过分析车辆动力学基本特性,设计了侧向速度、横摆角速度与车速分离的支持向量回归估计架构;然后对支持向量回归(SVR)模型进行多种行驶工况组成的数据集训练,在训练过程中运用鲸鱼优化算法对松弛变量中的惩罚因子c与核函数参数g进行寻优;最后对估计算法进行单移线、扫频试验虚拟仿真和实车ABS制动、双移线试验验证。结果表明,该算法有效提高了估计精度,且对车速的变化具有鲁棒性,可以实现准确的不依赖动力学模型精度的汽车运动状态估计。

集成数据挖掘知识的可解释最优超球体支持向量机

最优超球体支持向量机(SSLM)是一种典型的黑箱模型,其运行模式不需要考察被研究对象的内部结构和机理,仅利用对象的输入输出数据即能达到认识其功能和作用机制,因此具有响应快、实时性强等优点,但也因此缺乏可解释性和透明性.鉴于此,本文研究从SSLM黑箱模型的输入端加入先验知识的方法,增强其可解释性.本文开发了基于数据的非线性圆形知识挖掘算法以及知识的离散化算法,离散后的数据点不仅包含产生知识的原始数据点,还增加了新的数据点.通过将所挖掘的圆形知识以不等式约束的形式集成至SSLM模型,构造了可解释的SSLM模型(i-SSLM).该模型在训练时要确保知识约束的数据点分类正确,因此对模型结果有一定程度的预知,表明模型具有可解释性;同时,又由于知识的离散化增加了新的数据信息,因此,模型能具有更高的精度.i-SSLM模型的有效性在10组公共样本集和2组实际高炉数据集上得到了验证.

基于数据模型协作的海上钻井溢流早期预测预警

为防止海上钻井过程中井喷事故的发生,提出基于数据模型协作的海上钻井溢流早期预测预警方法。首先,建立基于粒子群优化(PSO)-最小二乘支持向量机(LSSVM)(PSO-LSSVM)的溢流风险预测模型,预测钻井监测参数未来时长内的趋势,并分析溢流事件与表征参数之间的关联关系;然后,建立基于朴素贝叶斯方法的钻井单参数溢流概率估算模型,并通过优化的D-S方法融合多个钻井参数的概率,分级预警溢流事件。结果表明:基于PSO-LSSVM的预测模型所得的溢流表征参数,预测误差较低;因对溢流事件的敏感度不同,单钻井参数所表征的溢流事件概率存在一定偏差;融合后的预警模型能够解决单参数的预警时间不一致的问题,排除误报警的可能。

基于大数据分析的配电网低压故障定位研究

为了维护配电网正常运行,以提升配电网低压故障定位能力为目标,提出基于大数据分析的配电网低压故障定位方法。通过采集配电网低压故障定位数据,利用大数据分析算法中的支持向量机算法进行分类,从而实现配电网低压故障定位。使用改进粒子群算法改进支持向量机算法,优化配电网低压故障定位结果,并进行配电网低压故障定位的仿真实验。结果表明,所提方法解决了配电网低压故障定位方法存在的弊端,获得了理想的配电网低压故障定位效果,能够满足配电网低压故障定位实际要求。

湖滨绿洲土壤有机碳含量的支持向量机估算模型

【目的】利用高光谱数据快速估算土壤有机碳含量,为干旱区湖滨绿洲合理开发土地资源提供科学依据。【方法】以新疆博斯腾湖北岸湖滨绿洲为研究区,将实测的土壤有机碳含量数据与高光谱数据相结合,对原始光谱进行SG平滑(SavitzkyGolay smoothing,SG)、连续统去除(Continuum Removal,CR)、连续小波变换(Continuous Wavelet Transform,CWT)预处理,采用连续投影算法(Successive Projections Algorithm,SPA)筛选特征波段;应用支持向量机(Support Vector Machines,SVM)模型估算土壤有机碳含量。【结果】(1)研究区土壤有机碳含量为0.69~50.32 g/kg,平均值为14.15 g/kg,标准差为9.51 g/kg,呈中等变异性,变异系数为67.20%。(2)土壤原始光谱反射率在350~750 nm,光谱反射率呈上升趋势,在750~2150 nm,光谱反射率呈相对平稳趋势,在2150~2500 nm,光谱反射率逐渐下降;连续小波变换对土壤原始光谱预处理后随着分解尺度的增加,光谱局部特征明显,吸收峰和反射峰越来越平滑;采用连续投影算法筛选的光谱特征波段集中于350~952 nm、1007~1742 nm、2082~2381 nm,且特征波段仅占可见光-近红外光谱波段的0.30%。(3)连续小波变换结合连续投影算法构建的SVM模型,其训练集和验证集分别R^(2)=0.76,RMSE=4.78和R^(2)=0.94,RMSE=3.30,RPD=2.50。【结论】CWT-SPA-SVM可有效估算研究区土壤有机碳含量。

基于最小二乘支持向量机的电网企业供应链碳排放预测方法研究

电网企业供应链碳排放的预测对推动产业链供应链绿色转型具有重要意义,为此提出基于最小二乘支持向量机的电网企业供应链碳排放预测方法。首先,利用4E平衡模型获取电网企业供应链碳排放数据;其次,利用PLS-VIP算法对碳排放数据实施数据筛选,得到有效的碳排放数据变量;最后,引入最小二乘支持向量机,构建碳排放预测模型,并且采用量子粒子群优化算法对其展开优化,实现电网企业供应链碳排放高精度预测。实验结果表明,所提方法在保证预测过程较高稳定性的同时,一定程度上提高了预测精度和预测效率。

mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.

A New Detection Approach Based on the Maximum Entropy Model

The maximum entropy model was introduced and a new intrusion detection approach based on the maximum entropy model was proposed. The vector space model was adopted for data presentation. The minimal entropy partitioning method was utilized for attribute diseretization. Experiments on the KDD CUP 1999 standard data set were designed and the experimental results were shown. The receiver operating eharaeteristie（ROC） curve analysis approach was utilized to analyze the experimental results. The analysis results show that the proposed approach is comparable to those based on support vector maehine（SVM） and outperforms those based on C4.5 and Naive Bayes classifiers. According to the overall evaluation result, the proposed approach is a little better than those based on SVM.