Journal articles: 'Kernel component analysis (KPCA/KICA)'

1

Xu, Jie, Jin Zhao, Baoping Ma, and Shousong Hu. "Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/987345.

Full text

Abstract:

New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA) and sparse support vector machine (SVM). The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA). The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE) chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

APA, Harvard, Vancouver, ISO, and other styles

2

Wang, Tianzhen, Jingjing Dong, Tao Xie, Demba Diallo, and Mohamed Benbouzid. "A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion." Information 10, no. 3 (March 17, 2019): 116. http://dx.doi.org/10.3390/info10030116.

Full text

Abstract:

This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%.

APA, Harvard, Vancouver, ISO, and other styles

3

Xiao, Ying Wang, and Ying Du. "Combination Method of Kernel Principal Component Analysis and Independent Component Analysis for Process Monitoring." Applied Mechanics and Materials 249-250 (December 2012): 153–58. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.153.

Full text

Abstract:

A combination method of kernel principal component analysis (KPCA) and independent component analysis (ICA) for process monitoring is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

APA, Harvard, Vancouver, ISO, and other styles

4

Liu, Mingguang, Xiangshun Li, Chuyue Lou, and Jin Jiang. "A Fault Detection Method Based on CPSO-Improved KICA." Entropy 21, no. 7 (July 9, 2019): 668. http://dx.doi.org/10.3390/e21070668.

Full text

Abstract:

In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR).

APA, Harvard, Vancouver, ISO, and other styles

5

Liu, Xiao Zhi, and Jing Li. "Multi-User Detection Based on Improved KICA with Bat Algorithm." Applied Mechanics and Materials 336-338 (July 2013): 1867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1867.

Full text

Abstract:

In this paper, an improved kernel independent component analysis (KICA) algorithm is proposed for multi-user detection (MUD). In this algorithm, a new hybrid kernel function is adopted. In addition, the bat algorithm is applied to the optimizing process of independent component separation. Simulation results show that the new hybrid kernel function performs better in MUD than other kernel functions, and the improved KICA with bat algorithm has the smallest bit error rate (BER) when compared with classical FastICA and KICA algorithms.

APA, Harvard, Vancouver, ISO, and other styles

6

Bawotong, Vitawati, Hanny Komalig, and Nelson Nainggolan. "Plot Multivariate Menggunakan Kernel Principal Component Analysis (KPCA) dengan Fungsi Power Kernel." d'CARTESIAN 4, no. 1 (February 10, 2015): 95. http://dx.doi.org/10.35799/dc.4.1.2015.8106.

Full text

Abstract:

Kernel PCA merupakan PCA yang diaplikasikan pada input data yang telah ditransformasikan ke feature space. Misalkan F: Rn®F fungsi yang memetakan semua input data xiÎRn, berlaku F(xi)ÎF. Salah satu dari banyak fungsi kernel adalah power kernel. Fungsi power kernel K(xi, xj) = –|| xi – xj ||b dengan 0 < b ≤ 1. Tujuan dari penelitian ini yaitu mempelajari penggunaan Kernel PCA (KPCA) dengan fungsi Power Kernel untuk membantu menyelesaikan masalah plot multivariate nonlinier terutama yang berhubungan dalam pengelompokan. Hasil menunjukkan bahwa Penggunaan KPCA dengan fungsi Power Kernel sangat membantu dalam menyelesaikan masalah plot multivariate yang belum dapat dikelompokan dengan garis pemisah yang linier. Kata kunci : Kernel Principal Component Analysis (KPCA), Plot Multivariate, Power Kernel

APA, Harvard, Vancouver, ISO, and other styles

7

Xie, Shengkun, Anna T. Lawniczak, Sridhar Krishnan, and Pietro Lio. "Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification." ISRN Computational Mathematics 2012 (July 29, 2012): 1–13. http://dx.doi.org/10.5402/2012/197352.

Full text

Abstract:

We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, X. H., H. L. Mao, C. M. Zhu, and Z. F. Huang. "Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 2 (December 1, 2008): 525–29. http://dx.doi.org/10.1243/09544062jmes1296.

Full text

Abstract:

A hydraulic turbine runner has a complex structure, and traditional source location methods do not have the higher accuracy to meet engineering requirements. The source location of crack acoustic emission (AE) signals in hydraulic turbine blades has been researched by combining it with kernel-independent component analysis (KICA) as feature extraction, with support vector machines (SVMs) as position recognition. This method is compared with those applied SVMs with feature extraction using kernel principal components analysis without feature extraction. The results show that the recognition rate in the crack region is 100 per cent by using both original AE parameters and feature parameters. Support vector regression by feature extraction using KICA can perform better than the other methods. As a result, it is a better method for source location of complex big size structures to combine KICA with SVM. It decreases the dimensionality of input signals and also improves the accuracy of location.

APA, Harvard, Vancouver, ISO, and other styles

9

Li, Zhi Chun. "New Detection Method for Gear Faults Based on Kernel Independent Component Analysis and BP Neural Network." Advanced Materials Research 909 (March 2014): 371–74. http://dx.doi.org/10.4028/www.scientific.net/amr.909.371.

Full text

Abstract:

Gearboxes are widely used in various kinds of applications. The normal operation of the gears contributes important roles on the machine performance. Due to harsh environment the rolling bearings are prone to failures. Hence, it is essential to detect the gear faults. However, the vibration signals of the gearbox are often contaminated, leading to deterioration of the fault diagnosis performance. To address this issue, a new approach is proposed based on the kernel independent component analysis (KICA) and BP neural network (BPNN). The KICA was used to extract sensitive signals to eliminate noise signals. Then a BPNN was adopted to detect the gear fault. To improve the fault identification, the Genetic Algorithm (GA) was adopted to optimize the BP parameters. Experiment tests using the gearbox fault simulator have been implemented. The test results show that the noise signals have been eliminated by the KICA and the GA-BPNN can detect the gear fault accurately. Moreover, through comparison with other existing methods, the proposed KICA-GA-BPNN produced the best detection rate of 93.7%.

APA, Harvard, Vancouver, ISO, and other styles

10

Liu, Yi, Hong Ying Deng, Zeng Liang Gao, and Ping Li. "Soft Sensor Modeling via Support Vector Regression with KICA-Based Feature Extraction." Advanced Materials Research 186 (January 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amr.186.560.

Full text

Abstract:

A novel two-level integrated soft sensor modeling method using kernel independent component analysis (KICA) and support vector regression (SVR) is proposed for chemical processes. In the first level, the KICA approach is adopted to extract information of input variables in the high dimensional feature space. Based on this strategy, the correlation of input variables can be eliminated and thus the complexity is reduced. Then, the model is established using SVR in the second level. The KICA-SVR soft sensor modeling method is applied to estimate product compositions in the Tennessee Eastman process. The obtained results show that it can exhibit better performance, compared to the traditional ICA, principal component analysis (PCA) and kernel PCA based information extraction methods, under different operating conditions.

APA, Harvard, Vancouver, ISO, and other styles

11

Lin, Lu Hui, Jie Ma, and Xiao Li Xu. "The Turbine Machine Fault Prediction Based on Kernel Principal Component Analysis." Advanced Materials Research 383-390 (November 2011): 4787–91. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4787.

Full text

Abstract:

Kernel principal component analysis (KPCA) is presented and is applied to predict the huge electro-mechanical system fault. Take the gas turbine set of Beijing Yanshan Petrochemical Refinery as the research object. KPCA uses the historical normal data of vibration intensity value to establish a prediction system. And then it is used to forecast the collected data for judging whether the turbine set is in normal. The simulation experiment result indicates the effectiveness of the method and the running state can be judged as normal or not from the result. And the experiment also shows KPCA can obtain a satisfactory prediction result.

APA, Harvard, Vancouver, ISO, and other styles

12

Binol, Hamidullah. "Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery." Mathematical Problems in Engineering 2018 (September 6, 2018): 1–14. http://dx.doi.org/10.1155/2018/9632569.

Full text

Abstract:

Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

APA, Harvard, Vancouver, ISO, and other styles

13

Li, Wei Hua, Kang Ding, Tie Lin Shi, and Guang Lan Liao. "Gear Fault Classification Using Kernel Discriminant Analysis." Key Engineering Materials 321-323 (October 2006): 1556–59. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1556.

Full text

Abstract:

This paper presents a study of KDA(kernel discriminant analysis) in gearbox failure feature extraction and classification. Experimental gearbox vibration signals measured from normal, gear small spall, gear severe spall and gear wear operating conditions are analyzed using either KPCA(kernel principal component analysis) or KDA as the feature extraction and fault classification methods. Experiment results indicate the effectiveness and thesuperiority of KDA for gear fault classification over KPCA.

APA, Harvard, Vancouver, ISO, and other styles

14

Chang, Yuchou, and Haifeng Wang. "Kernel Principal Component Analysis of Coil Compression in Parallel Imaging." Computational and Mathematical Methods in Medicine 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4254189.

Full text

Abstract:

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

APA, Harvard, Vancouver, ISO, and other styles

15

Liu, Wen Bin, Yu Xin He, Hua Qing Wang, and Jian Feng Yang. "Bearing Condition Recognition Based on Kernel Principal Component Analysis and Genetic Programming." Applied Mechanics and Materials 397-400 (September 2013): 1282–85. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1282.

Full text

Abstract:

In order to extract the fault feature validity in early fault diagnosis, method based on kernel principal component analysis and genetic programming (GP) is presented. The time domain features of the vibration signal are extracted and the initial symptom parameters (SP) are constructed. Then the combination to the initial SPs is carried on to optimize and build composite characteristics by GP. Through kernel principal component analysis (KPCA), the nonlinear principal component of the original characteristics is produced. Finally, the nonlinear principal components are selected as the feature subspace to classify the conditions of rolling bearing. Meanwhile, the within-class and among-class distance is introduced to compare and analyze the bearing condition recognition effect by using KPCA and GP plus KPCA separately. Experimental results show that the features extracted by kernel principal component analysis and genetic programming perform better ability in identifying the working states of the rolling bearing.

APA, Harvard, Vancouver, ISO, and other styles

16

Liu, Zhenqiu, Dechang Chen, and Halima Bensmail. "Gene Expression Data Classification With Kernel Principal Component Analysis." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 155–59. http://dx.doi.org/10.1155/jbb.2005.155.

Full text

Abstract:

One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

APA, Harvard, Vancouver, ISO, and other styles

17

Zhao, Feng, Islem Rekik, Seong-Whan Lee, Jing Liu, Junying Zhang, and Dinggang Shen. "Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets." Complexity 2019 (February 11, 2019): 1–17. http://dx.doi.org/10.1155/2019/5937274.

Full text

Abstract:

As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two-phase incremental KPCA (TP-IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP-IKPCA produces similar principal components as conventional batch-based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available.

APA, Harvard, Vancouver, ISO, and other styles

18

Jiang, Ling Li, Ping Li, and Si Wen Tang. "Parameter Optimization in KPCA for Rotating Machinery Feature Vector Dimensionality Reduction." Advanced Engineering Forum 2-3 (December 2011): 755–60. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.755.

Full text

Abstract:

This research aims at revealing the rules that the impact of kernel function and its parameters on the performance of kernel principle component analysis (KPCA) for dimensionality reduction. KPCA was performed on nine databases by using different kernel functions and a series of equal space kernel parameters. The relation charters between kernel parameters and the number of kernel principle components were constituted. It found that the Gussian kernel and its parameter above 25 are the best choice for rotating machinery feature vector dimensionality reduction by using KPCA. This study presents a reference and gist for the application of KPCA in rotating machinery fault diagnostic case.

APA, Harvard, Vancouver, ISO, and other styles

19

Wang, Duo, and Toshihisa Tanaka. "Kernel Principal Component Analysis Allowing Sparse Representation and Sample Selection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 1 (June 23, 2019): 9–20. http://dx.doi.org/10.37936/ecti-cit.2019131.187506.

Full text

Abstract:

Kernel principal component analysis (KPCA) is a kernelized version of principal component analysis (PCA). A kernel principal component is a superposition of kernel functions. Due to the number of kernel functions equals the number of samples, each component is not a sparse representation. Our purpose is to sparsify coefficients expressing in linear combination of kernel functions, two types of sparse kernel principal component are proposed in this paper. The method for solving sparse problem comprises two steps: (a) we start with the Pythagorean theorem and derive an explicit regression expression of KPCA and (b) two types of regularization $l_1$-norm or $l_{2,1}$-norm are added into the regression expression in order to obtain two different sparsity form, respectively. As the proposed objective function is different from elastic net-based sparse PCA (SPCA), the SPCA method cannot be directly applied to the proposed cost function. We show that the sparse representations are obtained in its iterative optimization by conducting an alternating direction method of multipliers. Experiments on toy examples and real data confirm the performance and effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

20

LIU, ZHENQIU, DECHANG CHEN, HALIMA BENSMAIL, and YING XU. "CLUSTERING GENE EXPRESSION DATA WITH KERNEL PRINCIPAL COMPONENTS." Journal of Bioinformatics and Computational Biology 03, no. 02 (April 2005): 303–16. http://dx.doi.org/10.1142/s0219720005001168.

Full text

Abstract:

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

APA, Harvard, Vancouver, ISO, and other styles

21

Xiao, Ying Wang, and Chen Zhong Zhang. "Novel Nonlinear Process Monitoring Based on KPCA-ICA." Advanced Materials Research 588-589 (November 2012): 1054–57. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1054.

Full text

Abstract:

A novel nonlinear process monitoring method based on kernel principal component analysis (KPCA) - independent component analysis (ICA) is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA.

APA, Harvard, Vancouver, ISO, and other styles

22

Suo, Ning, and Hui Lin Wang. "Safety Monitoring Information System of Railway Tunnel Construction Based on KNN." Applied Mechanics and Materials 303-306 (February 2013): 815–18. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.815.

Full text

Abstract:

This paper presents a novel approach for railway tunnel deformation data analysis in Safety Monitoring Information System. The proposed work introduces a nonlinear machine learning method, Kernel Principal Component Analysis (KPCA), and K nearest neighbor classification (KNN) classifier for railway tunnel deformation data analysis. Kernel Principal Component Analysis (KPCA) is first applied to 1-dimension signals derived from a sequence of silhouette images to reduce its dimensionality. Then, we performed K nearest neighbor classification (KNN) for railway tunnel deformation data analysis. The experimental results show the KNN based railway tunnel deformation data analysis algorithm is better than that based on KPCA.

APA, Harvard, Vancouver, ISO, and other styles

23

Liu, Xiao Fang, and Chun Yang. "Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-Means Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2390–94. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2390.

Full text

Abstract:

Nonlinear feature extraction used standard Kernel Principal Component Analysis (KPCA) method has large memories and high computational complexity in large datasets. A Greedy Kernel Principal Component Analysis (GKPCA) method is applied to reduce training data and deal with the nonlinear feature extraction problem for training data of large data in classification. First, a subset, which approximates to the original training data, is selected from the full training data using the greedy technique of the GKPCA method. Then, the feature extraction model is trained by the subset instead of the full training data. Finally, FCM algorithm classifies feature extraction data of the GKPCA, KPCA and PCA methods, respectively. The simulation results indicate that the feature extraction performance of both the GKPCA, and KPCA methods outperform the PCA method. In addition of retaining the performance of the KPCA method, the GKPCA method reduces computational complexity due to the reduced training set in classification.

APA, Harvard, Vancouver, ISO, and other styles

24

Jiang, Ling Li, Zong Qun Deng, and Si Wen Tang. "KPCA Denoising and its Application in Machinery Fault Diagnosis." Applied Mechanics and Materials 103 (September 2011): 274–78. http://dx.doi.org/10.4028/www.scientific.net/amm.103.274.

Full text

Abstract:

This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, one-dimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method

APA, Harvard, Vancouver, ISO, and other styles

25

Liu, Yang, Honghong Wang, Yeqi Fei, Ying Liu, Luxiang Shen, Zilong Zhuang, and Xiao Zhang. "Research on the Prediction of Green Plum Acidity Based on Improved XGBoost." Sensors 21, no. 3 (January 30, 2021): 930. http://dx.doi.org/10.3390/s21030930.

Full text

Abstract:

The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.

APA, Harvard, Vancouver, ISO, and other styles

26

Zhang, Lian, Xiao Qian Hu, and Shan Li. "Comfort Fusion Evaluation of the Indoor Thermal Environment Based on KPCA and Genetic Neural Network." Applied Mechanics and Materials 448-453 (October 2013): 204–8. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.204.

Full text

Abstract:

For the problem of complicated nonlinear relationships among the parameters of heat comfort index PMV, KPCA (Kernel Principal Component Analysis) is used to do the feature extraction. On the basis, KPCA+BP and KPCA+GNN are utilized to forecast the heat comfort level. Simulation results show that KPCA can extract the nonlinear uncorrelated sample data, and KPCA+GNN are evaluated best with high accuracy.

APA, Harvard, Vancouver, ISO, and other styles

27

He, Fei, Quan Yang, and Bao Jian Wang. "Hot Rolling State Analysis Based on Kernel Principal Component Analysis and Information Entropy." Advanced Materials Research 572 (October 2012): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.572.7.

Full text

Abstract:

With more and more process data acquired from manufacturing process, extracting useful information to build empirical models of past successful operations is urgently required to get higher product quality. Clustering is the important data mining methods, where feature extraction is a significant factor to ensure the accurate rate of clustering and classification. As a common non-linear feature extraction method, kernel principal component analysis (KPCA) uses the variance as the information metric, but the variance is not always effective in some cases. Since information entropy is nonlinear and can effectively represent the dependencies of features, the Renyi entropy is used as the information metric to extract the feature in this paper. Simulation data, Tennessee Eastman and hot rolling process data are used for model validation. As a result the proposed method has better performance on feature extraction, compared with traditional KPCA.

APA, Harvard, Vancouver, ISO, and other styles

28

Dongyao, Jia, Ai Yanke, and Zou Shengxiong. "Reduction of Multidimensional Image Characteristics Based on Improved KICA." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/256206.

Full text

Abstract:

The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. Dimensionality reduction and optimization of characteristic parameter model based on improved kernel independent component analysis are proposed in this paper; the independent primitives are obtained by KICA (kernel independent component analysis) algorithm to construct an independent group subspace, while using 2DPCA (2D principal component analysis) algorithm to complete the second order related to data and further reduce the dimension in the above method. Meanwhile, the optimization effect evaluation method based on Amari error and average correlation degree is presented in this paper. Comparative simulation experiments show that the Amari error is less than 6%, the average correlation degree is stable at 97% or more, and the parameter optimization method can effectively reduce the dimension of multidimensional characteristic parameters.

APA, Harvard, Vancouver, ISO, and other styles

29

Xu, Ke Jia, Bin Chen, and Li Zeng. "On-Line Defect Detecting Method Based on Kernel Method." Key Engineering Materials 474-476 (April 2011): 858–63. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.858.

Full text

Abstract:

The conflict between accuracy and speed is one of the most well-known dilemmas of the real-time defect detecting system. This paper presents a real-time defect detecting algorithm based on Kernel principal component analysis (KPCA). KPCA-based feature extraction have recently shown to be very effective for image denoising, however the Normal KPCA method is time-consuming. In our method, we propose a progressive algorithm to speed up the reconstruct process while improve accuracy. Experimental results demonstrate that our method is dramatically better than Normal KPCA Pre-image method in terms of speed and performance.

APA, Harvard, Vancouver, ISO, and other styles

30

Yang, Qian, Qiang Yang, Miaoying Huang, and Wenjun Yan. "Particle swarm optimization-based empirical mode decomposition–kernel independent component analysis joint approach for diagnosing wind turbine gearbox with multiple faults." Transactions of the Institute of Measurement and Control 40, no. 6 (March 15, 2017): 1836–45. http://dx.doi.org/10.1177/0142331217691336.

Full text

Abstract:

Under-determined blind source separation (BSS) of nonlinear mixed signals in multiple-fault detection of wind turbine gearbox has been considered a challenging issue for years. The paper addresses this problem and presents an efficient solution through a combination of empirical mode decomposition (EMD) and kernel independent component analysis (KICA) methods. The nonlinear mixture signals are firstly decomposed into a set of intrinsic mode function (IMF) components using EMD, which can be combined with the original observed signals to construct a set of new signals. Thus, the original problem can be effectively transformed into a problem of over-determined BSS, which can be solved by the use of KICA. The adoption of particle swarm optimization (PSO) algorithm can further enhance the performance of the EMD–KICA solution. The proposed solution is assessed through a set of simulation experiments and the numerical results demonstrate its effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

31

Lv, Ning, Guang Yuan Bai, Lu Qi Yan, and Yuan Jian Fu. "The Fault Diagnosis Model of Beer Fermentation Process Based on Kernel Principal Component Analysis for Constant Value Detection." Advanced Materials Research 1030-1032 (September 2014): 1822–27. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1822.

Full text

Abstract:

In order to overcome the application limitations of principal component analysis fault diagnose model in non-linear time-varying and reduce computational complexity for process monitoring based on non-linear principal component, we introduced kernel transformation theory of nonlinear space to extract data feature extraction and a fault monitoring model based on kernel principal component analysis (KPCA) for constant value detection was proposed. Through the proper selection of kernel function parameter values, the KPCA model can achieve constant value of process fault detection and has lower computational complexity than other non-linear algorithms. The fault detection experiment for beer fermentation process shows that this method is able to detect process faults in a timely manner and has good real-time performance and accuracy in the batch process of slowly time-varying.

APA, Harvard, Vancouver, ISO, and other styles

32

Zhao, Chunhui, Furong Gao, and Fuli Wang. "Nonlinear Batch Process Monitoring Using Phase-Based Kernel-Independent Component Analysis−Principal Component Analysis (KICA−PCA)." Industrial & Engineering Chemistry Research 48, no. 20 (October 21, 2009): 9163–74. http://dx.doi.org/10.1021/ie8012874.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Jiang, Ling Li, Ping Li, and Bo Zeng. "Signal Denoising Method Based on KICA by Noise Components." Applied Mechanics and Materials 329 (June 2013): 269–73. http://dx.doi.org/10.4028/www.scientific.net/amm.329.269.

Full text

Abstract:

Denoising is an essential part of fault signal analysis. This paper proposes a kernel independent component analysis (KICA)-based denoising method for removing the noise from vibration signal. By introducing noise components of the observed signal, one-dimensional observed signal is extended to multi-dimensional signal. Then performing KICA on multidimensional signal, the noise in the observed signal consistent with the introduced noise will be removed that achieve the purpose of denosing. The effectiveness of the proposed method is demonstrated by the case study.

APA, Harvard, Vancouver, ISO, and other styles

34

Nawaz, Muhammad, Abdulhalim Shah Maulud, and Haslinda Zabiri. "Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system." E3S Web of Conferences 287 (2021): 03011. http://dx.doi.org/10.1051/e3sconf/202128703011.

Full text

Abstract:

Process monitoring techniques in chemical process systems help to improve product quality and plant safety. Multiscale classification plays a crucial role in the monitoring of chemical processes. However, there is a problem in coping with high-dimensional correlated data produced by complex, nonlinear processes. Therefore, an improved multiscale fault classification framework has been proposed to enhance the fault classification ability in nonlinear chemical process systems. This framework combines wavelet transform (WT), kernel principal component analysis (KPCA), and K nearest neighbors (KNN) classifier. Initially, a moving window-based WT is used to extract multiscale information from process data in time and frequency simultaneously at different scales. The resulting wavelet coefficients are reconstructed and fed into the KPCA to produce feature vectors. In the final step, these vectors have been used as inputs for the KNN classifier. The performance of the proposed multi-scale KPCA-KNN framework is analyzed and compared using a continuous stirred tank reactor (CSTR) system as a case study. The results show that the proposed multiscale KPCA-KNN framework has a high success rate over PCA-KNN and KPCA-KNN methods.

APA, Harvard, Vancouver, ISO, and other styles

35

Xu, Ping, You Cai Wang, Kai Wang, and Qiu Yan Wang. "Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA." Applied Mechanics and Materials 623 (August 2014): 202–10. http://dx.doi.org/10.4028/www.scientific.net/amm.623.202.

Full text

Abstract:

The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four common sensor faults. At first its fault is detected by Q statistic; secondly its fault is identified by T2 contribution percent change. The simulation and the practical result show the KPCA method has good performance on complex control system in sensor fault detection and diagnosis.

APA, Harvard, Vancouver, ISO, and other styles

36

Sabrol, Hiteshwari, and Satish Kumar. "Recognition of Tomato Late Blight by using DWT and Component Analysis." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 194. http://dx.doi.org/10.11591/ijece.v7i1.pp194-199.

Full text

Abstract:

Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e. late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.

APA, Harvard, Vancouver, ISO, and other styles

37

Cai, Fei, Honghui Chen, and Zhen Shu. "Web document ranking via active learning and kernel principal component analysis." International Journal of Modern Physics C 26, no. 04 (February 25, 2015): 1550041. http://dx.doi.org/10.1142/s0129183115500412.

Full text

Abstract:

Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

APA, Harvard, Vancouver, ISO, and other styles

38

Zhao, Xiao Qiang, and Zhan Ming Li. "An Improved KPCA Method of Fault Detection Based on Wavelet Denoising." Key Engineering Materials 467-469 (February 2011): 1427–32. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1427.

Full text

Abstract:

For complicated nonlinear systems, the data inevitably have noise, random disturbance, Traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix K for fault detection with large sample sets. So an improved KPCA method based on wavelet denoising is proposed. First, wavelet denoising method is used for data processing, then the improved KPCA method can reduce calculational complexity of fault detection. The proposed method is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.

APA, Harvard, Vancouver, ISO, and other styles

39

Fan, Yunpeng, Wei Zhang, and Yingwei Zhang. "Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/546138.

Full text

Abstract:

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

APA, Harvard, Vancouver, ISO, and other styles

40

Sun, Xiang. "Classification of Area Flowing Based on KRNN Method." Advanced Materials Research 255-260 (May 2011): 2855–59. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2855.

Full text

Abstract:

It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.

APA, Harvard, Vancouver, ISO, and other styles

41

Vani, H. Y., M. A. Anusuya, and M. L. Chayadevi. "Morlet-Kernel Principal Component Analysis Features for Speech Recognition." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4482–86. http://dx.doi.org/10.1166/jctn.2020.9102.

Full text

Abstract:

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.

APA, Harvard, Vancouver, ISO, and other styles

42

Sun, Yubing, Jun Wang, and Shaoming Cheng. "Early Diagnosis of Botrytis Cinerea Infestation of Tomato Plant by Electronic Nose." Applied Engineering in Agriculture 34, no. 4 (2018): 667–74. http://dx.doi.org/10.13031/aea.12748.

Full text

Abstract:

Abstract. Early diagnosis of disease is important for loss control. It is much easier to manage and prevent disease from spreading in this period. This study employed electronic nose (E-nose) for early diagnosis of infestation of tomato plant. Gas Chromatography-Mass Spectrometer (GC-MS) was applied for proving the potential of E-nose detection and taken as the evidence for determining the range of parameters of Kernel Principal Component Analysis (KPCA). Then, the way to seek the best parameter (the type of kernel, kernel parameter, and the number of principal component) of KPCA for the prediction of time of tomato plant under disease attack was introduced. The results showed that when the type of kernel was Polynomial, the kernel parameter was 2, and the number of principal component was 17, the highest correct discrimination rate of Linear Discriminant Analysis (LDA) was obtained, which was as high as 100%. Furthermore, multiple linear regressions (MLR) was employed and the results showed that MLR combined with KPCA obtained excellent performance. This study demonstrated that it was feasible for early diagnosis of infestation of tomato plant by E-nose and the model for predicting the infestation time was presented. Keywords: Disease infestation, Early diagnosis, Sensors, Tomato plant.

APA, Harvard, Vancouver, ISO, and other styles

43

Hu, De Kun, An Sheng Ye, Li Li, and Li Zhang. "Recognition of Facial Expression via Kernel PCA Network." Applied Mechanics and Materials 631-632 (September 2014): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.498.

Full text

Abstract:

In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: cascaded kernel principal component analysis (KPCA), binary hashing, and block-wise histograms. In the proposed model, KPCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and pooling. For comparison and better understanding, We have tested these basic networks extensively on many benchmark visual datasets ( such as the JAFFE [13] database, the CMU AMP face expression database, a part of the Extended Cohn-Kanade (CK+) database), The results demonstrate the potential of the KPCANet serving as a simple but highly competitive baseline for facial expression recognition.

APA, Harvard, Vancouver, ISO, and other styles

44

Li, Chun Ling, and Yu Feng Lu. "Head Pose Recognition Based on 2-D KPCA." Applied Mechanics and Materials 373-375 (August 2013): 468–72. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.468.

Full text

Abstract:

One’s head pose can be estimated using face images. The hidden manifold of head pose in the high dimensional space can be successfully embedded into a 2 dimensional space using Kernel Principal Component Analysis (KPCA). A pose curve is gotten using KPCA train samples and new pose image is projected onto this curve. The pose angle can be estimated using interpolation method. The disadvantage of traditional linear method is conquered by using 2-D KPCA and the experimental results that the method is effective to estimate head poses. The kernel functions effects on estimation accuracy are also discussed.

APA, Harvard, Vancouver, ISO, and other styles

45

Wang, Xiao Yan. "Fault Detection of Continuous Casting Process Using Kernel PCA." Advanced Materials Research 468-471 (February 2012): 1706–9. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1706.

Full text

Abstract:

Viewing to monitoring the continuous casting process, a nonlinear fault detection method based on kernel principal component analysis (KPCA) was introduced. KPCA can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions, which is to first map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. Based on T2 and SPE charts in feature space, principal component analysis(PCA)can be used to detect faults.. The simulation results show that the proposed approach effectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA.

APA, Harvard, Vancouver, ISO, and other styles

46

Zhang, Lan, Hongjun Su, and Jingwei Shen. "Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis." Remote Sensing 11, no. 10 (May 23, 2019): 1219. http://dx.doi.org/10.3390/rs11101219.

Full text

Abstract:

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image.

APA, Harvard, Vancouver, ISO, and other styles

47

Sawssen, Bacha, Taouali Okba, and Liouane Noureeddine. "KELM-KPCA Method for COVID-19-induced Pneumonia Detection." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 17 (February 24, 2021): 166–71. http://dx.doi.org/10.37394/23209.2020.17.20.

Full text

Abstract:

The new corona virus 2019 (COVID-19) has become the most pressing issue facing mankind. Like a wildfire burning through the world, the COVID-19 disease has changed the global landscape in only one year. In this mini-review, a novel image classifier based on Kernel Extreme Learning Machine (KELM) and Kernel Principal Component Analysis (KPCA) is presented. The proposed algorithm called KELM-KPCA, aims to detect COVID-19 disease in chest radiographs, using a constrained dataset.

APA, Harvard, Vancouver, ISO, and other styles

48

Ye, Beige, Taorong Qiu, Xiaoming Bai, and Ping Liu. "Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis." Entropy 20, no. 9 (September 13, 2018): 701. http://dx.doi.org/10.3390/e20090701.

Full text

Abstract:

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

APA, Harvard, Vancouver, ISO, and other styles

49

Wang, Qiang, Yong Bao Liu, Xing He, Shu Yong Liu, and Jian Hua Liu. "Fault Diagnosis of Bearing Based on KPCA and KNN Method." Advanced Materials Research 986-987 (July 2014): 1491–96. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1491.

Full text

Abstract:

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.

APA, Harvard, Vancouver, ISO, and other styles

50

Dong, Shaojiang, Dihua Sun, Baoping Tang, Zhengyuan Gao, Yingrui Wang, Wentao Yu, and Ming Xia. "Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 15 (December 11, 2014): 2827–34. http://dx.doi.org/10.1177/0954406214563235.

Full text

Abstract:

In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear feature extraction method KPCA was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model, and the bearing’s running state identification was thereby realized. Cases of test and actual were analyzed. The results validate the effectiveness of the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Kernel component analysis (KPCA/KICA)'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles