SENSITIVITY ANALYSIS FOR ROLLING PROCESS BASED ON SUPPORT VECTOR MACHINE

A method for the calculation of the sensitivity factors of the rollingprocess has been obtained by differentiating the roll force model based on support vector machine.It can eliminate the algebraic loop of the analytical model of the rolling process. The simulationsin the first stand of five stand cold tandem rolling mill indicate that the calculation forsensitivities by this proposed method can obtain a good accuracy, and an appropriate adjustment onthe control variables determined directly by the sensitivity has an excellent compensation accuracy.Moreover, the roll gap has larger effect on the exit thickness than both front tension and backtension, and it is more efficient to select the roll gap as the control variable of the thicknesscontrol system in the first stand.

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

Support Vector Machine Cost Estimation Model for Road Projects

A cost estimate is one of the most important steps in road project management. There are ranges of factors that mostly affect the final project cost. Many approaches were used to estimate project cost, which took into consideration probable project performance and risks. The aim is to improve the ability of construction managers to predict a parametric cost estimate for road projects using SVM （support vector machine）. The work is based on collecting historical road executed cases. The 12 factors were identified to be the most important factors affecting the cost-estimating model. A total of 70 case studies from historical data were divided randomly into three sets： training set includes 60 cases, cross validation set includes three cases and testing set includes seven cases. The built model was successfully able to predict project cost to the AP （accuracy performance） of 95%.

Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.

Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects

In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.

近红外无创血糖浓度的Label Sensitivity算法和支持向量机回归

近红外光谱分析技术在生物医学工程领域具有广阔应用前景。无创且持续性地测量能实时监控人体血糖水平,给糖尿病患者带来极大便利性、提高生存质量、降低糖尿病并发症发生率具有很大的社会效益。无创血糖监测的想法提出较早,但仍然存在预测精度低、预测值与标签值相关性不高等难点,至今没有达到临床要求。近年来,光谱检测技术发展迅猛且机器学习技术在智能信息处理方面具有明显优势,两者结合可以有效提高人体无创血糖医学监测模型的精度和普适性。提出了一种标签敏感度算法(LS),并结合支持向量机方法建立了人体血糖含量预测模型。使用近红外光谱仪采集了4名志愿者食指处动态血液光谱数据(每名志愿者28组数据),并使用多元散射矫正(MSC)方法消除了部分光散射的影响。考虑血糖对不同波长光的吸收有差异,提出了基于血糖浓度标签差的特征波长挑选方法,并构建了标签敏感度支持向量机(LSSVR)预测模型。设计实验,对比该模型与偏最小二乘回归(PLSR)和区分度支持向量机(FSSVR)算法。结果表明,LS算法的最佳特征波长数为32,经特征波长选择后的LSSVR表现最佳,其均方误差降低至0.02 mmol·L^(-1),明显优于全谱段PLSR模型,血糖浓度的预测值与标签值的相关系数提升至99.8%,预测值全部位于可容许误差的克拉克网格A区内。LSSVR模型的优异表现为早日实现血糖的无创监测提供了新思路。

基于CS-SVM的氧化铝蒸发过程故障检测

针对氧化铝蒸发过程样本集中的类不平衡和故障难以实时检测问题,提出线性权重递减粒子群-代价敏感支持向量机故障检测方法。深入分析氧化铝蒸发过程机理,选择合适输入条件、操作参数、状态参数作为代价敏感支持向量机的输入向量,工况样本类别作为其输出。代价敏感支持向量机以最小化误分类代价为目标,利用线性权重递减粒子群优化代价敏感支持向量机核参数和误分类代价参数。实验结果表明所提出的方法能有效地提高故障识别率和减少平均误分类代价。

基于MCS-SVM的建筑工程造价建模与预测

工程造价的建模与预测对工程管理具有十分重要的研究意义,为了提高工程造价预测准确性,针对当前工程造价预测模型的局限性,设计了改进布谷鸟搜索算法优化支持向量机的工程造价预测模型(MSC-SVM)。对当前工程造价预测建模的现状进行分析,指出当前存在的主要问题,引入支持向量机建立工程造价预测模型,并通过改进布谷鸟搜索算法估计支持向量机参数,采用具体工程造价数据对模型性能进行分析。测试结果表明,提出的模型获得了较可靠的工程造价预测结果,可以为工程管理决策提供有价值的参考信息。

基于CS-SVM与Bagging的垃圾邮件过滤算法研究

针对邮件过滤中正常邮件与垃圾邮件误分类代价的不对称性,提出了基于代价敏感支持向量机(CS-SVM)与Bagging的垃圾邮件过滤算法.通过对每个样本赋予不同的代价,利用最小化误分类代价来获得最优分类器,提高了垃圾邮件过滤的正确率.实验结果表明,该算法具有正确率高、能有效降低将正常邮件误判为垃圾邮件的比率等优点。

基于改进FOA优化的CS-SVM轴承故障诊断研究

针对故障诊断中的小样本及样本类不平衡问题。建立基于代价敏感支持向量机(CS-SVM)的故障诊断模型,提出采用改进FOA算法(IFOA)对规则化常数C+,C-和核函数参数g进行优化选取,通过增大对故障类样本错分的惩罚代价,提升对故障类的诊断正确率;以IMS航空轴承试验数据为对象,结合随机共振、KPCA特征提取方法对所提IFOA优化的CS-SVM模型进行了验证。结果表明,该方法能有效处理误分类代价不同的轴承故障诊断问题,提高了故障类样本的诊断正确率,可拓展应用至其它故障诊断领域。

Design,development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine

In this study,an intelligent system based on combined machine vision(MV)and Support Vector Machine(SVM)was developed for sorting of peeled pistachio kernels and shells.The system was composed of conveyor belt,lighting box,camera,processing unit and sorting unit.A color CCD camera was used to capture images.The images were digitalized by a capture card and transferred to a personal computer for further analysis.Initially,images were converted from RGB color space to HSV color ones.For segmentation of the acquired images,H-component in the HSV color space and Otsu thresholding method were applied.A feature vector containing 30 color features was extracted from the captured images.A feature selection method based on sensitivity analysis was carried out to select superior features.The selected features were presented to SVM classifier.Various SVM models having a different kernel function were developed and tested.The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy(99.17%)and then was selected to use in online decision-making unit of the system.By launching the online system,it was found that limiting factors of the system capacity were related to the hardware parts of the system(conveyor belt and pneumatic valves used in the sorting unit).The limiting factors led to a distance of 8 mm between the samples.The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h,respectively.

Entire Solution Path for Support Vector Machine for Positive and Unlabeled Classification

Support vector machines （SVMs） aim to find an optimal separating hyper-plane that maximizes separation between two classes of training examples （more precisely, maximizes the margin between the two classes of examples）. The choice of the cost parameter for training the SVM model is always a critical issue. This analysis studies how the cost parameter determines the hyper-plane; especially for classifications using only positive data and unlabeled data. An algorithm is given for the entire solution path by choosing the ＇best＇ cost parameter while training the SVM model. The performance of the algorithm is compared with conventional implementations that use default values as the cost parameter on two synthetic data sets and two real-world data sets. The results show that the algorithm achieves better results when dealing with positive data and unlabeled classification.

Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches

In this paper, we extend our previous study of addressing the important problem of automatically identifying question and non-question segments in Arabic monologues using prosodic features. We propose here two novel classification approaches to this problem: one based on the use of the powerful type-2 fuzzy logic systems (type-2 FLS) and the other on the use of the discriminative sensitivity-based linear learning method (SBLLM). The use of prosodic features has been used in a plethora of practical applications, including speech-related applications, such as speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. In this paper, we continue to specifically focus on the Arabic language, as other languages have received a lot of attention in this regard. Moreover, we aim to improve the performance of our previously-used techniques, of which the support vector machine (SVM) method was the best performing, by applying the two above-mentioned powerful classification approaches. The recorded continuous speech is first segmented into sentences using both energy and time duration parameters. The prosodic features are then extracted from each sentence and fed into each of the two proposed classifiers so as to classify each sentence as a Question or a Non-Question sentence. Our extensive simulation work, based on a moderately-sized database, showed the two proposed classifiers outperform SVM in all of the experiments carried out, with the type-2 FLS classifier consistently exhibiting the best performance, because of its ability to handle all forms of uncertainties.

基于变量敏感度筛选的回归型支持向量机的数控机床热误差预测

随着机械制造行业的迅猛发展,对于数控机床的定位精度要求越来越高。为了提高机床定位精度,建立了基于变量敏感度筛选与回归型支持向量机(SVR)混合模型,并将其用于数控机床热误差预测方法。该方法基于对变量敏感度分析,筛选掉敏感度低的干扰自变量。本方法与基本SVR模型对数控机床热误差预测值进行对比,结果表明基本SVR受到敏感度低的干扰自变量影响,预测结果与实测热误差结果偏差较大;经过变量敏感度筛选之后的SVR混合模型预测值具有更高的准确度,验证了此模型的可行性。

基于改进SVM的电力工程造价预测

针对支持向量机求解速度较慢且用于预测电力工程造价的性能不理想等问题,提出了一种基于改进SVM的电力工程造价预测模型。该模型全面考虑了电力工程成本的组成要素并进行参数归一化处理,利用最小二乘估计改进SVM模型,同时采用遗传算法求解LSSVM的参数最优值,并通过优化后的GA-LSSVM模型实现对电力工程成本的预测。基于MATLAB仿真平台的仿真实验结果表明,模型预测的工程成本与实际值较为接近,归一化均方误差与平均绝对百分比误差分别为18.34万元和3.58%,且预测时间仅为256 ms,证明了其整体性能优于其他对比模型。

基于改进PSO-SVM法的斜拉桥可靠度分析

对于斜拉桥结构体系复杂、结构功能函数难以显现等问题导致实际应用中往往难以准确、高效地评估斜拉桥的可靠性,本文提出了基于改进PSO-SVM(particle swarm optimization-support vector machine)的可靠度分析方法。该方法通过引入非线性递减惯性权值和异步线性变化的学习因子2种策略的粒子群算法,其目的在于提高全局的搜索能力并对支持向量机参数进行优化,从而得到挠度钢混组合梁跨中位移超限失效和单根斜拉索强度失效的隐式功能函数代理模型,结合Monte-Carlo对其抽样获取概率分布及统计参数,并进一步求解可靠度指标。通过算例比较,该方法在整体计算时长和精度方面表现出较好的效果,相比于传统方法有明显的优势。采用该方法对银洲湖大桥进行可靠度分析,结果显示:在汽车荷载作用下,主梁跨中位移超限失效的斜拉桥可靠度指标为4.203,各个斜拉索强度失效的可靠度指标为4.623~5.812,均满足规范要求;斜拉索的弹性模量和容重分别对跨中位移超限失效和斜拉索强度失效的斜拉桥可靠度指标影响最大,并且它们的变量均值与可靠度指标基本呈线性正相关。主梁跨中位移超限失效的斜拉桥可靠度指标随着斜拉索弹性模量均值系数的增大而降低、斜拉索41#强度失效的斜拉桥可靠度指标随着斜拉索容重均值系数的增大而下降。

基于KPCA-WOA-SVM的住宅工程造价预测

在项目决策阶段,准确预测住宅工程造价对提高工程项目决策的科学性至关重要,引入人工智能及机器技术能进一步提高预测的精准度。通过文献梳理,确定决策阶段住宅工程造价的影响指标,用核主成分分析(KPCA)对影响指标进行降维,利用鲸鱼优化算法(WOA)确定支持向量机(SVM)的惩罚参数与核参数,最终构建基于KPCA-WOA-SVM的住宅工程造价预测模型。采用江苏省近5年的70组住宅工程造价数据对模型进行验证,结果表明:与BP神经网络模型、SVM模型和WOA-SVM模型相比,KPCA-WOA-SVM模型预测精准度更高,适用性更好。

基于PSO-SVM的Φ-OTDR系统模式识别研究

针对相位敏感光时域反射仪(phase sensitive optical time domain reflectometer,Φ-OTDR)系统中误报率高的问题,提出一种多域特征提取与粒子群算法优化支持向量机(particle swarm optimization-support vector machine,PSO-SVM)相结合的模式识别算法。首先,对原始信号进行差分处理后提取时域特征,并利用小波包分解方法,通过验证不同分解层数下的事件分类准确率,设定最优分解层数为6层,提取差分信号的能量特征。然后以SVM分类器为基础,利用PSO算法优化SVM分类器参数,提高光纤振动信号识别准确率。最后利用Φ-OTDR事件数据集进行验证,实验结果表明,该模式识别算法达到了95.6%的振动事件分类准确率。

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NGO-CNN-SVM的高标准农田灌溉工程施工成本预测

为提高高标准农田项目施工成本的预测精度,控制施工成本在合理范围,减少投资风险,该研究从单体灌溉工程施工成本预测角度出发,通过随机森林(random forest,RF)筛选出高标准农田灌溉工程施工成本的关键影响因素,结合卷积神经网络(convolutional neural networks,CNN)和支持向量机(support vector machine,SVM)两种模型的优点,通过北方苍鹰优化算法(northern goshawk optimization,NGO)对模型里的惩罚因子和核参数进行寻优,构建基于NGO-CNN-SVM的施工成本预测模型。通过辽宁省2018—2023年高标准农田工程中灌溉工程的施工成本数据,选取样本决定系数R^(2)、平均绝对误差MAE、平均绝对百分比误差MAPE和均方根误差RMSE作为精度指标进行分析,结果表明:基于NGO-CNN-SVM的施工成本预测模型在渠道工程中MAE低于0.615万元,RMSE低于0.512万元,R^(2)达到0.968以上,相对误差小于4.210%;在进水闸工程中MAE低于0.610万元,RMSE低于0.536万元,R^(2)达到0.966以上,相对误差小于4.410%;在桥涵工程中MAE低于0.494万元,RMSE低于0.477万元,R^(2)达到0.970以上,相对误差小于3.548%,并相比较于反向传播神经网络,CNN和CNN-SVM模型,NGO-CNN-SVM模型的预测结果均最优。通过特征选择、模型融合、算法优化以及不同模型对比表明NGO-CNN-SVM模型具有更高的预测准确率和泛化性,可为高标准农田灌溉工程施工成本预测提供理论依据。