Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on Principal Components Analysis and Support Vector Machine

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

Predicting configuration performance of modular product family using principal component analysis and support vector machine

A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.

Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.

Fast Tensor Principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor Principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-

Dynamic Spatial Discrimination Maps of Discriminative Activation between Different Tasks Based on Support Vector Machines

As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.

Support vector machine with mixture of kernels for automatic image annotation

Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.

Coal and gas outburst prediction model based on principal component analysis and improved support vector machine

In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys,principal component analysis(PCA)combined with support vector machine(SVM)and Knearest neighbor(KNN)was used.The intensity and intensity ratio of fifteen lines of six elements(Fe,Si,Mg,Cu,Zn,and Mn)in the FIBS spectrum were selected.The distances between the focusing lens and the target surface in the pre-filament,filament,and post-filament were 958 mm,976 mm,and 1000 mm,respectively.The source data set was fifteen spectral line intensity ratios,and the cumulative interpretation rates of PC1,PC2,and PC3 were 97.22%,98.17%,and 95.31%,respectively.The first three PCs obtained by PCA were the input variables of SVM and KNN.The identification accuracy of the different positions of focusing lens and target surface was obtained,and the identification accuracy of SVM and KNN in the filament was 100%and 90%,respectively.The source data set of the filament was obtained by PCA for the first three PCs,which were randomly selected as the training set and test set of SVM and KNN in 3:2.The identification accuracy of SVM and KNN was 97.5%and 92.5%,respectively.The research results can provide a reference for the identification of aluminum alloys by FIBS.

Rapid bacteria identification using structured illumination microscopy and machine learning

Traditionally,optical microscopy is used to visualize the morphological features of pathogenic bacteria,of which the features are further used for the detection and ident ification of the bacteria.However,due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification,the ffectiveness of this optical microscopy-based method is limited.Here,we reported a pilot study on a combined use of Structured Illumination Microscopy(SIM)with machine learning for rapid bacteria identification.After applying machine learning to the SIM image datasets from three model bacteria(including Escherichia coli,Mycobacterium smegmatis,and Pseudomonas aeruginosa),we obtained a classifcation accuracy of up to 98%.This study points out a promising possibility for rapid bacterial identification by morphological features.

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

基于HHT的绝缘子泄漏电流分析及放电状态分类识别

泄漏电流是污秽绝缘子在线监测参数,能动态地反映绝缘子表面的放电状态。文中开展了瓷绝缘子人工污秽放电试验,利用Hilbert-Huang变换分析了不同污闪阶段的泄漏电流固有模态函数分量、Hilbert边际谱与时频熵,从时频域及波形细节提取了15个特征量,使用主成分分析法与最小二乘支持向量机分类器对污秽放电状态进行识别。结果表明:起始放电阶段与闪络阶段的泄漏电流固有模态函数分量较多;泄漏电流的Hilbert边际谱上频率主要分布在0~150 Hz、200~250 Hz范围内;闪络前泄漏电流的时频熵值总是大于闪络后的;当训练样本数为测试样本数5倍及以上时,分类器的综合评判准确率可达99%,准确实现了污秽放电状态的分类识别。文中研究结果可为建立绝缘子污闪预警系统提供依据。

An Improved SVM Based Wind Turbine Multi-fault Detection Method

A fault detection method bases on wind turbines(WTs)supervisory control and data acquisition(SCADA)is proposed,principal component analysis(PCA)was used to reduce the dimension of target features to 1-D,so that PCA output 1-D data can be used as label of support vector machine(SVM).Thus on the premise of not losing the prediction correctness,one model can detect the fault of 2 to 4 features,largely reduce the complexity of model building.Different experiments are present to show the effectiveness of the proposed method.

多策略改进黏菌算法阶段优化HSVM变压器故障辨识

为解决变压器故障诊断精度较低的问题,提出了一种多策略改进黏菌算法(ISMA)阶段优化混合核支持向量机(HSVM)的变压器故障诊断新方法。首先,利用主成分分析(PCA)来消除变量之间的信息冗余并降低数据集维度。其次,引入黏菌算法(SMA),并结合Logistic混沌映射、二次插值、自适应权重多策略改进SMA,以提高SMA算法收敛速度和局部搜索能力;然后,与原始SMA、WHO和GWO算法进行寻优测试,对比验证改进后SMA算法的优越性;最后,使用改进SMA算法分阶段对混合核支持向量机参数寻优,构建ISMA-HSVM变压器故障诊断模型。将降维后的特征数据输入HSVM模型与BPPN、ELM和SVM进行比较,HSVM模型的诊断准确性分别提高了5.55%、8.89%、5.55%。使用ISMA优化HSVM模型参数,与WHO、GWO、SMA算法优化效果比较,结果准确性提高了13.33%、12.22%、5.55%。其中,ISMA-HSVM模型的诊断精度为93.33%。实验结果表明,所提模型有效提升故障诊断分类性能,且具有较高的故障诊断精度。

通信干扰下无线传感器网络中微弱信号检测

微弱信号检测是保证无线传感器网络高效使用的重要环节,但检测过程易受噪声信号、传感器性能、虚拟信号等因素的干扰,从而导致误检。为了解决上述问题,提出一种通信干扰下无线传感器网络微弱信号检测方法。通过局部投影降噪法剔除信号中的噪声,避免噪声对检测过程产生影响。采用主分量分析算法提取去噪信号的特征,并根据遗传算法优化支持向量参数,将提取的特征输入到向量机中,通过特征的分类完成通信干扰下无线传感器网络微弱信号的检测。实验结果表明,所提方法的信号检测结果与实际结果基本一致,检测时间在30ms内,且抗噪性能强。