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With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set.

Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis. In this article, (I) the cycle temporal algorithm (CTA) combined with the dynamic kernel principal component analysis (DKPCA) and the multiway dynamic kernel principal component analysis (MDKPCA) fault detection algorithms are proposed, which are used for continuous and batch process fault detections, respectively. In addition, (II) a fault variable identification model based on reconstructed-based contribution (RBC) model that paves the way for determining the cause of the fault are proposed. The proposed fault diagnosis model was applied to Tennessee Eastman (TE) process and penicillin fermentation process for fault diagnosis. And compare with other fault diagnosis methods. The results show that the proposed method has better detection effects than other methods. Finally, the reconstruction-based contribution (RBC) model method is used to accurately locate the root cause of the fault and determine the fault path.

Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling stage or/and the online monitoring stage of statistical process monitoring. We provide a fast incremental nonlinear matrix completion (FINLMC) method for missing data imputation, which enables us to use kernel methods such as kernel principal component analysis (KPCA) to monitor nonlinear multivariate processes even when there are missing data. We also provide theoretical analysis for the effectiveness of the proposed method. Experiments show that the proposed method can reduce the false alarm rate and improve the fault detection rate in nonlinear processing monitoring with missing data. The proposed method FINLMC can also be used to solve missing data in other problems such as classification and process control.

In order to effectively recognize and identify liquor quality by nuclear magnetic resonance (NMR) spectrum data, we propose a hybrid data processing and recognition algorithm. In this algorithm Kernel principal component analysis (KPCA) was used to remove the correlation and reduce dimension of the NMR spectrum, and then convolutional neural network (CNN) was used for classification and identification of the processed spectrum. The compared experiment result show that the proposed KPCA+CNN method can obtain higher accuracy than CNN and PCA+CNN methods based on the NMR spectrum. The KPCA+CNN algorithm has good application prospect and reference value.

The kernel principal component analysis (KPCA) is widely used as a fault monitoring tool for complex nonlinear chemical processes in recent years. The cumulative contribution rate that extracts the kernel principal is usually obtained relied on a fixed model, which cannot be employed for time-varying chemical processes. Hence, a novel adaptive kernel principal component analysis (AKPCA) integrating grey relational analysis (GRA) (AKPCA-GRA) is proposed to dynamically monitor the fault occurrence. A moving window integrating the threshold method is used to adaptively extract the kernel principal for chemical processes. Then the corresponding T2 and Q statistics calculated by the selected kernel principal based on the AKPCA decides whether the fault has occurred. Moreover, the GRA method is used to analyze and calculate the correlation coefficient of abnormal features obtained based on the APKCA method, which provides the operational guidance for the nonlinear chemical process to find out the variable causing the fault. Finally, the proposed method is verified using the Tennessee Eastman (TE) process. The case results demonstrate that the proposed method outperforms the KPCA, the KPCA based on the threshold and the moving window principal component analysis, the support vector machine (SVM) and the Logistic Regression (LR) in terms of the missed alarm rate (MAR) and the false alarm rate (FAR), which can effectively analyze the variables causing the fault.

International audience; This chapter introduces a powerful class of machine learning approaches called kernel methods, which present an alternative to arguably more widely known neural network approaches. Kernel methods can learn even highly nonlinear problems by making an implicit transformation from a low-dimensional input space into a higher-dimensional feature space. This is in contrast to neural networks, which make explicit use of nonlinear functions to learn nonlinear problems. Alternatively, kernel methods can be seen as approaches exploiting similarities between data points. In this chapter, you will learn the fundamentals of kernel methods, their capabilities, and limitations and see how they can be used for both supervised and unsupervised learning. As usual, you can try your hand in solving chemically motivated machine learning tasks using kernel methods for several case studies.

This paper highlights kernel principal component analysis (KPCA) in distinguishing damage-sensitive features from the effects of liquid loading on frequency response. A vibration test is performed on an aircraft wing box incorporated with a liquid tank that undergoes various tank loading. Such experiment is established as a preliminary study of an aircraft wing that undergoes operational load change in a fuel tank. The operational loading effects in a mechanical system can lead to a false alarm as loading and damage effects produce a similar reduction in the vibration response. This study proposes a non-nonlinear transformation to separate loading effects from damage-sensitive features. Based on a baseline data set built from a healthy structure that undergoes systematic tank loading, the Gaussian parameter is measured based on the distance of the baseline data set to various damage states. As a result, both loading and damage features expand and are distinguished better. For novelty damage detection, Mahalanobis square distance (MSD) and Monte Carlo-based threshold are applied. The main contribution of this project is the nonlinear PCA projection to understand the dynamic behavior of the wing box under damage and loading influences and to differentiate both effects that arise from the tank loading and damage severities.

Principal component analysis (PCA) and Kernel principal component analysis (KPCA) are fundamental methods in machine learning for dimensionality reduction. The former is a technique for finding this approximation in finite dimensions and the latter is often in an infinite dimensional reproducing Kernel Hilbert-space (RKHS). In this paper, we present a geometric framework for computing the principal linear subspaces in both (finite and infinite) situations as well as for the robust PCA case, that amounts to computing the intrinsic average on the space of all subspaces: the Grassmann manifold. Points on this manifold are defined as the subspaces spanned by $K$ K -tuples of observations. The intrinsic Grassmann average of these subspaces are shown to coincide with the principal components of the observations when they are drawn from a Gaussian distribution. We show similar results in the RKHS case and provide an efficient algorithm for computing the projection onto the this average subspace. The result is a method akin to KPCA which is substantially faster. Further, we present a novel online version of the KPCA using our geometric framework. Competitive performance of all our algorithms are demonstrated on a variety of real and synthetic data sets.

This is the peer reviewed version of the following article: Diez, P. [et al.]. Nonlinear dimensionality reduction for parametric problems: a kernel proper orthogonal decomposition. "International journal for numerical methods in engineering", 30 Desembre 2021, vol. 122, núm. 24, p. 7306-7327, which has been published in final form at DOI: 10.1002/nme.6831. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of parametric solutions lies in a low-dimensional manifold (with dimension equal to the number of independent parameters) embedded in a large-dimensional space (dimension equal to the number of degrees of freedom of the full-order discrete model). A posteriori model reduction is based on constructing a basis from a family of snapshots (solutions of the full-order model computed offline), and then use this new basis to solve the subsequent instances online. Proper orthogonal decomposition (POD) reduces the problem into a linear subspace of lower dimension, eliminating redundancies in the family of snapshots. The strategy proposed here is to use a nonlinear dimensionality reduction technique, namely, the kernel principal component analysis (kPCA), in order to find a nonlinear manifold, with an expected much lower dimension, and to solve the problem in this low-dimensional manifold. Guided by this paradigm, the methodology devised here introduces different novel ideas, namely, 1) characterizing the nonlinear manifold using local tangent spaces, where the reduced-order problem is linear and based on the neighboring snapshots, 2) the approximation space is enriched with the cross-products of the snapshots, introducing a quadratic description, 3) the kernel for kPCA is defined ad hoc, based on physical considerations, and 4) the iterations in the reduced-dimensional space are performed using an algorithm based on a Delaunay tessellation of the cloud of snapshots in the reduced space. The resulting computational strategy is performing outstandingly in the numerical tests, alleviating many of the problems associated with POD and improving the numerical accuracy. Generalitat de Catalunya, 2017-SGR-1278; Ministerio de Ciencia e Innovación, CEX2018-000797-S; PID2020-113463RB-C32; PID2020-113463RB-C33

Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and preimaging are introduced to capture the nonlinear relationship between ECGs and respiration. The parameter selection of the radial basis function kernel in KPCA is also improved, ensuring accuracy and a reduction in computational cost. The correlation coefficient and amplitude square coherence coefficient are used as metrics to carry out quantitative and qualitative comparisons with three traditional EDR algorithms. The results show that the proposed method performs better than the traditional EDR algorithms in obtaining single-lead-EDR signals.

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

Detection of out-of round (OOR) faults of metro vehicle wheels is very important to improve stationarity and stability in metro vehicles and avoid accidents caused by OOR faults. Diagnosis of OOR faults demands extracting useful information accurately from mass of vibration signals with poor signal-to-noise ratio (SNR) of metro vehicle wheels for complex running condition. In this paper, we proposed a diagnosis method on OOR faults of metro vehicle wheels combined with variational mode decomposition (VMD), kernel principal component analysis (KPCA), and deep belief network (DBN) to diagnose the OOR faults of metro wheels. Vibration signals of China metro vehicle wheels collected while the metro vehicle is running are used to train the diagnosis model and adjust parameters of DBN and KPCA based on testing accuracy. The different dimensions of KPCA, epoch number, and node number of DBN are compared, and the better parameters of diagnosis model based on vibration signals are concluded in this paper. The generalization of the diagnosis model is checked nine times by testing the calculation of each group of parameters and using an error declining process. The mean accuracy of diagnosis model proposed in this paper is 0.9136, and the diagnosis model presented in this paper is very significant to detect OOR faults online.

In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF Lp-norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.

In our body the skin is the largest organ, it protects from injury, infection and also helps to maintain the temperature of the body. Melanoma Skin cancer is one of the most dangerous skin diseases and it is caused by an uncontrolled growth of abnormal skin cells, by ultraviolet radiation from sunshine. Melanoma is more common among white skins such as Americans than in darker skins. The digital lesion images have been analyzed based on image acquisition, pre-processing, and image segmentation technique. The image segmentation technique is applied to easily identify the affected portion in the skin input image. The images are enhanced using morphological filters and sharpen region of interest in an image, enhancement method enhanced the non-uniform background illumination and converts the input image into a binary image too easy to identify foreground objects. The mole of melanoma is segmented from the background using Active Contour algorithm. After that, the feature extraction methods such as Kernel PCA, SIFT are used to extract melanoma affected area in an image based on their intensity and texture features.

In this paper, we propose an efficient algorithm for denoising the degraded face image sequence in the principal component analysis (PCA) domain for recognizing this face. We first apply a temporal filter that performs motion compensation combined with a weighted average filter, then an adaptative spatial filter realized by PCA transformation that decomposes the image temporally filtered into two sub-images using a threshold calculated according to the variance of noise and intensity retained by the first eigenvectors. The first sub-image containing a great intensity variance expresses the small features, and the other with high noise level and low intensity variance describes the large feature. Therefore, the spatial filter can be adapted according to the amount of information in each area, so that small features are reprojected in the Kernel PCA domain where image details are reconstructed efficiently, and large features are denoised by an anisotropic diffusion filter to recover the homogenous regions. Finally, the restored image is used in recognition process. The experimental results obtained from the Cohen-Kanade facial expression (CKFE) database tested against different noise levels and three types of blur (Gaussian, motion and pillbox) show better restoration and recognition performance of this algorithm compared to other methods.

In building automation system, timely detecting the operational faults in water chillers is crucial to system operation management. As a linear data transformation technique, the performance of principal component analysis (PCA) based chiller fault detection method is limited, especially for the incipient ones, due to the nonlinearities in chillers. In addition, the control limit for the monitoring statistic, such as squared prediction error (SPE), is determined under the Gaussian assumption of the score variables, which can hardly be satisfied in water chillers. Therefore, an enhanced fault detection method with the application of kernel density estimation (KDE) and kernel entropy component analysis (KECA) algorithm is reported in this paper. Cauchy-Schwarz (CS) divergence was evaluated as monitoring statistic to measure the cosine of the angle between two data sets after being projected onto a dominated subspace, and then adopted as an index for dissimilarity. KDE with its bandwidth being optimized was also applied to estimate the distribution of the CS divergence, so that the control limit for fault monitoring could be determined. The proposed KDE based KECA-CS method was validated using the experimental data from ASHRAE RP-1043, and further compared to the PCA -SPE, kernel principal component analysis (KPCA)-SPE, and KECA-SPE methods. Results showed that the best performance could be realized when using the KDE based KECA-CS method. The reported fault detection ratio was over 68% for the seven typical chiller faults even at their corresponding least severity level. The average fault detection accuracy was over 90%.

To effectively improve the fault detection rate of the support vector machine (SVM) based method, we propose a fault detection method with SVM based on principal component augmented matrix (PCAM-SVM). Firstly, the principal component analysis (PCA) algorithm is used to obtain the scores of training data in principal component space. Secondly, the input characteristics of time delay and time difference for the scores are added to construct the augmented matrix. Then, the combined augmented matrix on normal data and fault data is used to obtain the discriminant function of SVM model for classification. Finally, the SVM model is used to perform the classification operation on test data. The proposed method increases the complexity of input characteristics of model, reduces data autocorrelation, and enhances the fault detection performance of SVM by constructing principal component augmented matrix. The method has been applied to a multivariate dynamic simulation case and the Tennessee-Eastman process. The simulation results validate the feasibility and effectiveness of PCAM-SVM by comparison with PCA, independent component analysis (ICA), kernel principal component analysis (KPCA), SVM and PCA-SVM.

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson’s disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher’s linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

Forecasting of stock market is considered as one of the most decisive and critical tasks for the data scientists in financial domain. Stock market is one of exciting and demanding monetary activities for individual investors, and financial analysts. The stock market is an inter-connected important economic international business. Prediction of stock price has become a crucial issue for stock investors and brokers. The stock market is able to influence the day to day life of the common people. The stock price is based on the state of market stability. As the dormant high noises in the data impair the performance, reducing the noise would be competent while constructing the forecasting model. To achieve this task, integration of kernel principal component analysis, support vector machine with teaching learning based optimization algorithm is proposed in this research work. Kernel principal component analysis is able to remove the unnecessary and unrelated factors, and reduces the dimension of input variables and time complexity. The feasibility and efficiency of this proposed hybrid model has been applied to forecast the daily open prices of stock index of a leading Company. The performance of the proposed approach is evaluated with 3543 daily transactional (13th December 2001 to 4th December 2020) stocks price data from Bombay Stock Exchange (BSE). Empirical results show that the proposed model enhances the performance of the prediction model and can be used for taking better decision and more accurate predictions for financial investors.

The exponential growth of the photovoltaic system installations also requires an adequate maintenance and supervision system to ensure the continuity of service of the system. Conventional protection systems for electrical systems have shown their shortcomings for protecting photovoltaic systems. In this article, a statistical approach based on principal component analysis and its variants is used to detect and identify faults in a photovoltaic array. This involves analysing the variations of the data of the current–voltage and voltage–power characteristics. Subsequently, the calculation of the contributions is applied to the SPE index for the identification of faults. By employing the intermediate value theorem, six different operating states have been identified. The various results obtained first from the simulation model from the Simulink environment and then from a real system of 18 PV show that the kernel principal component analysis allows defect detection with a better precision.

Author(s): Magyar, Zsuzsanna Blanka | Advisor(s): Handcock, Mark S | Abstract: Parties are the main vehicles of representation in modern, democratic societies. Party systems, that is the number and the size of all the parties within a country, can vary greatly across countries. There is an ongoing debate in the political science literature about the appropriate way to reduce the dimensionality of the cross-country party system data for comparative purposes. This thesis reviews that literature and offers a new solution: Principal Component Analysis to find the most important information in the data matrix. I use data from 17 advanced democracies from 1970-2013. I conduct analyses using various related methods (Principal Component Analysis, Principal Component Analysis on the Residuals, kernel Principal Component Analysis, Non-Linear Principal Component Analysis, Principal Component Analysis on log-ratio transformed variables and Principal component Analysis on non-centered variables). I find that the most important differences across countries are: “the size of the biggest two parties”, “competition between the two biggest parties”, “existence of a third party” and “balanced multipartism.” I argue that most of the current political science literature uses summary measures that are only correlated with the first of those four dimensions. I suggest a strategy for incorporating a measure of the second dimension that relies on indices of opposition structure.

Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep. High-dimensional datasets, especially in EEG-based classification, present challenges as there are often a large number of potentially useful features for detecting the phenomenon of interest. Thus, it is often important to reduce the dimension of the original data prior to training the classifier. In this study, linear dimensionality reduction methods-principal component analysis (PCA) and probabilistic PCA (PPCA)-were compared with eight non-linear dimensionality reduction methods (kernel PCA, classical multi-dimensional scaling, isometric mapping, nearest neighbour estimation, stochastic neighbourhood embedding, autoencoder, stochastic proximity embedding, and Laplacian eigenmaps) on previously collected behavioural and EEG data from eight healthy non-sleep-deprived volunteers performing a 1D-visuomotor tracking task for 1 h. The effectiveness of the feature reduction algorithms was evaluated by visual inspection of class separation on 3D scatterplots, by trustworthiness scores, and by microsleep detection performance on a stacked-generalisation-based linear discriminant analysis (LDA) system estimating the microsleep/responsive state at 1 Hz based on the reduced features. On trustworthiness, PPCA outperformed PCA, but PCA outperformed all of the non-linear techniques. The trustworthiness score for each feature reduction method also correlated strongly with microsleep-state detection performance, providing strong validation of the ability of trustworthiness to estimate the relative effectiveness of feature reduction approaches, in terms of predicting performance, and ability to do so independently of the gold standard. Graphical abstract Proposed microsleep detection system.

Timely detection and diagnosis of process abnormality in industries is crucial for minimizing downtime and maximizing profit. Among various process monitoring and fault detection techniques, principal component analysis (PCA) and its different variants are probably the ones with maximum applications. Because of the linearity constraint of the conventional PCA, some non-linear variants of PCA have been proposed. Among different non-linear variants of PCA, the kernel PCA (KPCA) has gained maximum attention in the field of industrial fault detection. This article revisits the basic KPCA algorithm along with different limitations of KPCA and the crucial open issues in design of KPCA based monitoring system. Different modifications proposed by different researchers are reviewed. Strategies adopted by various researchers for optimal selection of kernel parameter and number of principal components are also presented.

Subspace identification methods, such as canonical variate analysis (CVA), are non-iterative tools suitable for the state-space modelling of multi-input, multi-output (MIMO) processes, e.g. industrial processes, using input-output data. To learn nonlinear system behavior, kernel subspace techniques are commonly used. However, the issue of kernel design must be given more attention because the type of kernel can influence the kind of nonlinearities that the model can capture. In this paper, a new kernel design is proposed for CVA based identification, which is a mixture of a global and local kernel to enhance generalization ability and includes a mechanism to vary the influence of each process variable into the model response. During validation, model hyper-parameters were tuned using random search. The overall method is called Feature-Relevant Mixed Kernel Canonical Variate Analysis (FR-MKCVA). Using an evaporator case study, the trained FR-MKCVA models show a better fit to observed data than those of single-kernel CVA, linear CVA, and neural net models under both interpolation and extrapolation scenarios. This work provides a basis for future exploration of deep and diverse kernel designs for system identification.

The accurate and reliable prediction of future runoff is important to guarantee for strengthening water resource optimization and management. The novel contribution of this article is the development of a hybrid model (FWA-ANFIS), which is based on the improvement of the adaptive neuro-fuzzy inference system (ANFIS) with the fireworks algorithm (FWA). The dominant driving factors of runoff are selected from several hydro-meteorological indices (precipitation, soil moisture content, and evaporation) as predictors by correlation coefficient (CC) analysis, mutual information (MI) analysis, correlation analysis and principal component analysis (CC-PCA), mutual information and kernel principal component analysis (MI-KPCA), MI-PCA, and CC-KPCA. The FWA-ANFIS model is applied to the Beiru River, China, with data from 1985–2016 (1985–2012 for model training and 2013–2016 for model prediction). The standard ANFIS, the GA-ANFIS, the PSO-ANFIS, the FWA-ELM, the GA-ELM, and the PSO-ELM are utilized as compared prediction models on the identical dataset. The results indicate that CC-PCA outperforms the other methods regarding the selection of predictors, and FWA-ANFIS has the best performance in terms of the root mean square error, correlation coefficient, and coefficient of determination, followed by the GA-ANFIS, PSO-ANFIS, ANFIS, FWA-ELM, GA-ELM, and PSO-ELM models. Furthermore, the degrees of uncertainty of the models increase in the following order: FWA-ANFIS, GA-ANFIS, PSO-ANFIS, ANFIS, PSO-ELM, GA-ELM, and FWA-ELM.

As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through $Q$ learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).

In this work, we propose a progressive principal component analysis (PPCA) method for compressing deep convolutional neural networks. The proposed method starts with a prespecified layer and progressively moves on to the final output layer. For each target layer, PPCA conducts kernel principal component analysis for the estimated kernel weights. This leads to a significant reduction in the number of kernels in the current layer. As a consequence, the channels used for the next layer are also reduced substantially. This is because the number of kernels used in the current layer determines the number of channels for the next layer. For convenience, we refer to this as a progressive effect. As a consequence, the entire model structure can be substantially compressed, and both the number of parameters and the inference costs can be substantially reduced. Meanwhile, the prediction accuracy remains very competitive with respect to that of the baseline model. The effectiveness of the proposed method is evaluated on a number of classical CNNs (AlexNet, VGGNet, ResNet and MobileNet) and benchmark datasets. The empirical findings are very encouraging. The code is available at https://github.com/zhoujing89/ppca .

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient $ {K_{\mathrm {d}}(\lambda)}$ from remote sensing reflectance $ {R_{\mathrm {rs}}(\lambda)}$ in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful $ {K_{\mathrm {d}}(\lambda)}$ retrieval algorithms in UVR, the results showed that KpcaUV (with $ {R^{2}}$ : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with $\boldsymbol {R^{2}}$ : 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain ${K_{\mathrm {d}}(380)}$ for the continuous observation of the large area.

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by integrating the principal component analysis (PCA) with the Bayesian network (BN). Though the integration of PCA-BN for FDD purposes has been studied in the past, the present work makes two contributions for process systems. First, the application of correlation dimension (CD) to select principal components (PCs) automatically. Second, the use of Kullback-Leibler divergence (KLD) and copula theory to develop a data-based BN learning technique. The proposed method uses a combination of vine copula and Bayes’ theorem (BT) to capture nonlinear dependence of high-dimensional process data which eliminates the need for discretization of continuous data. The data-driven integrated PCA-BN framework has been applied to two processing systems. Performance of the proposed methodology is compared with the independent component analysis (ICA), kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and their integrated frameworks with the BN. The comparative study suggests that the proposed framework provides superior performance.

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gear has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used for nonlinear feature extraction, but the blind setting of kernel function parameters greatly affects the performance of KPCA algorithm. For the optimization of kernel parameters, it is necessary to study theoretical modeling to improve KPCA performance. In this paper, employing a Fisher criterion (FC) discriminant function in pattern recognition, the optimization mathematical model of the kernel parameter was presented and the improved particle swarm optimization algorithm (PSO) was applied to search for the optimum value, and the performance of the Kernel principal component analysis for nonlinear problems was improved. The optimized KPCA was applied for feature extraction of different wear fault modes of a planetary gear, and the feature dimensions were reduced from 27 to 10. The feature parameters with 92.9 % contribution rates were retained and sample sets were formed to feed the support vector machine (SVM) for final classification and identification. The intelligently optimized KPCA based on the PSO-FC has improved the structural distribution of data in the feature space and showed a good scale clustering effect in planetary gear wear state recognition. The accuracy of the SVM classification was improved by 17.5 %.

Fire detection is one of the most important needs of surveillance and security systems in industrial applications. In this paper, a novel fire detection algorithm based on motion analysis using fractal and spatio-temporal features is presented. Initially, in each frame, dynamic textures are detected through three different fractal analysis methods and thresholding techniques. In the first method, Kernel Principal Component Analysis technique is used with fractal analysis and in the next a temporal blanket method is proposed. Finally, the third method is introduced based on temporal local fractal analysis and Laplace method. An RGB probability model is provided to separate the moving regions that have similar colors to the fire regions in each frame. Then, several spatio-temporal features such as correlation coefficient and mutual information are extracted from the candidate regions. Lastly, a two-class SVM classifier is used to classify these candidate regions. Various experimental results show that our proposed algorithm outperforms the relevant state-of-the-art algorithms.

Improving the efficiency of photovoltaic (PV) systems has gained priority in current research due to the large volumes of PV panels installed. Moreover, the remarkable efforts made to investigate d...

Mining subsidence, as one of the most common geological hazards, bringing a massive threat to the new infrastructure in such a post-mining area. To evaluate the stability of a post-mining area tran...

This paper proposed a coupled model of effective feature extraction and optimized classifier, which can overcome the existing problems of coal and gas outbursts classification in the literatures. Firstly, we support the use of kernel principal component analysis and linear discriminant analysis methods to extract linear and nonlinear feature information from coal and gas outbursts influencing factors. Secondly, in order to realize the complementarity and correlation between coal and gas outbursts influencing factors, we perform parallel feature fusion to combine the extracted linear and nonlinear feature information. Thirdly, an improved classifier called BO–MKRVM has been proposed that combines mixed kernel relevance vector machine (MKRVM) and Bayesian optimization (BO) algorithm to predict coal and gas outbursts according to the extracted features. In BO–MKRVM model, an effective mixed kernel function which combines Gaussian kernel function and Sigmoid kernel function is proposed to improve the learning and generalization ability of the MKRVM classifier, then the BO and tenfold cross-validation are utilized to optimize kernel parameters and weight coefficients of MKRVM with strong global and local search capability, the proposed BO–MKRVM classifier is performed on coal and gas outbursts dataset. Compared with the single feature extraction method, the combination of linear and non-linear feature extraction methods can obtain complete feature information and contribute to the classification performance of outbursts. The mixed kernel function considers the characteristics of coal and gas outbursts sample data, which can also effectively improve the outbursts accuracy. After the MKRVM classifier is optimized by BO algorithm, the BO–MKRVM classifier has better fitting effect and generalization ability, and obtains higher accuracy with a lower time. The experimental results are obviously better than those of other classification models, which further verifies the applicability of the proposed coupled model.

In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

Speaker adaptation is implemented in order to shift the speaker-independent model closer to the new speaker speech characteristics to improve the speech recognition performance. The kernel eigenspace-based speaker adaptation methods provide satisfactory performance using only a small amount of adaptation data. In such adaptation methods, kernel principal component analysis (KPCA) is applied to the training speaker space in order to create kernel eigenspace. Then, the adapted acoustic model to the new user is calculated in that space. One limitation of KPCA is its inability to define a precise pre-image of the model adapted in the kernel eigenspace, back to the speaker space. Therefore, a huge amount of computations is required to perform adaptation. The previously developed solutions for calculation of an approximate pre-image of the adapted model do not necessarily lead to the optimal conditions. Therefore, in this paper, we propose an efficient solution for this problem to construct more reliable pre-image of the adapted model in the speaker space. For this purpose, we benefit from the latent variable model to define a probabilistic model for description of the applied mapping between the kernel eigenspace and the speaker space. The experiments were conducted on two speech databases: FARSDAT, a Persian, and TIMIT, an English speech database. Implementing a typical HMM-based automatic speech recognition system, it was verified that the proposed method, utilizing about three seconds of adaptation data, achieves up to 4.4% and 7.6% relative phoneme recognition accuracy rate over the speaker-independent model on FARSDAT and TIMIT, respectively. Moreover, the proposed approach demonstrated superior performance compared to the other kernel eigenspace-based adaptation methods.

Quality-related fault detection is the key subject under process monitoring and fault diagnosis (PM-FD) framework, which is an effective mean to guarantee safety production and obtain reliable product quality, and thus, has recently become active areas of process control fields. In this paper, a novel decentralized detection method for quality-related faults is proposed for manufacturing industrial processes, which will help field engineers to purposefully and real-time observe the operation state of the production process. Specifically, the main innovations are: 1) the linear and nonlinear dynamic interdependencies between process and quality variables are revealed and quantified by the minimal redundancy maximal relevance (mRMR) algorithm, and the most representative variables are selected in each subprocess; 2) a new mixed kernel function based dynamic mixed kernel principal component analysis (DMKPCA) model is designed for enhancing the generalization ability and maintaining the data characteristics of original sample space; 3) unified monitoring statistics are constructed in the subprocess level, and Bayesian fusion is implemented for forming monitoring decisions from the plant-wide level. Finally, a case study on an actual hot rolling mill process (HSMP) is finally given to validate the effectiveness of the proposed scheme, and several competitive methods are applied to carry out the quality-related fault detection process.

Random Forest (RF) is one of the mostly used machine learning techniques in fault detection and diagnosis of industrial systems. However, its implementation suffers from certain drawbacks when considering the correlations between variables. In addition, to perform a fault detection and diagnosis, the classical RF only uses the raw data by the direct use of measured variables. The direct raw data could yield to poor performance due to the data redundancies and noises. Thus, this paper proposes four improved RF methods to overcome the above-mentioned limitations. The developed methods aim to reduce at first the amount of the training data and select the first kernel principal components (KPCs) using different kernel principal component analysis (PCA) based dimensionality reduction schemes. Then, the retained KPCs are fed to the RF classifier for fault diagnosis purposes. Finally, the proposed techniques are applied to a wind energy conversion (WEC) system. Different case studies were investigated in order to illustrate the effectiveness and robustness of the developed techniques compared to the state-of-the-art methods. The obtained results show the low computation time and high diagnosis accuracy of the proposed approaches (an average accuracy of 91%).

The rapid increase in the number of remote sensing sensors makes it possible to develop multisource feature extraction and fusion techniques to improve the classification accuracy of surface materials. It has been reported that light detection and ranging (LiDAR) data can contribute complementary information to hyperspectral images (HSIs). In this article, a multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is first designed and applied to HSIs to both reduce the dimensions and compress the noise impact. Next, 2-D and 3-D Gabor filters are, respectively, employed on the KPCA-reduced HSIs and LiDAR data to obtain discriminative Gabor features, and the magnitude and phase information are both taken into account. Three different modules, including the raw data-based feature cube (concatenated KPCA-reduced HSIs and LiDAR data), the Gabor magnitude feature cube, and the Gabor phase feature cube (concatenation of the corresponding Gabor features extracted from the KPCA-reduced HSIs and LiDAR data), can be, thus, achieved. After that, random forest (RF) classifier and quadrant bit coding (QBC) are introduced to separately accomplish the classification task on the aforementioned three extracted feature cubes. Alternatively, two superpixel maps are generated by utilizing the multichannel simple noniterative clustering (SNIC) and entropy rate superpixel segmentation (ERS) algorithms on the combined HSIs and LiDAR data, which are then used to regularize the three classification maps. Finally, a weighted majority voting-based decision fusion strategy is incorporated to effectively enhance the joint use of the multisource data. The proposed approach is, thus, named MFSuDF. A series of experiments are conducted on three real-world data sets to demonstrate the effectiveness of the proposed MFSuDF approach. The experimental results show that our MFSuDF can achieve the overall accuracy of 73.64%, 93.88%, and 74.11% for Houston, Trento, and Missouri University and University of Florida (MUUFL) Gulport data sets, respectively, when there are only three samples per class for training.

Sludge bulking is very common in wastewater treatment process (WWTP), which will degrade the operation performance or even destroy the process. In order to diagnose sludge bulking accurately, a data-knowledge-driven diagnosis (DKD) method is proposed to identify the occurrence and cause variable in this paper. This proposed DKD method contains the following advantages. First, a data-driven detection model, using a recursive kernel principal component analysis (RKPCA) algorithm, is designed to capture the intrinsic nonlinear and time-varying characteristic of sludge bulking. Then, the occurrence of sludge bulking can be detected with high accuracy. Second, a DKD model, based on the Bayesian network (BN), is developed to extract the causality among process variables to identify the root cause variables of sludge bulking. Then, the root cause variables of sludge bulking can be diagnosed to improve the operation performance of WWTP. Finally, the proposed DKD method was tested on the measured data from a real WWTP. Experimental results confirmed the effectiveness of the proposed DKD method.

Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful sensor channels for FDC model development in semiconductor manufacturing. The proposed method can be used as a pre-processing step prior to feature extraction, and the selected sensor channels can be leveraged by process engineers for finer feature design. In this research, firstly, time series alignment kernels (TSAKs) are proposed to handle multivariate trace data. Then, the proposed method combines 5 different time series alignment kernels (TSAKs) with a feature selection algorithm, minimum Redundancy Maximum Relevance (mRMR), to identify the important sensor channels. Furthermore, a TSAK+Kernel Principal Component Analysis (KPCA) algorithm is proposed for a visualization tool. Lastly, the TSAK+Support Vector Machine (SVM) is employed for results validation. In this study, validation of the proposed method is based on both open-source datasets and the proprietary datasets from a real production line.

In this article, a new indoor localization technique with extreme learning machine (ELM) is proposed where only a small number of received signal strength indicator (RSSI) measurements are labeled. In the off-line learning phase, the iterative self organizing data analysis techniques algorithm (ISODATA) is used to divide the RSSI measurements into some measurement data subsets. Then the multi-kernel ELM (MK-ELM) method is utilized to perform classification learning and obtain the RSSI measurement classification function. For each RSSI measurement subset, a two-stage feature extraction algorithm using the kernel principal component analysis, the deep learning network and ELM method is proposed for RSSI measurement feature extraction. At last, the position regression function of each subset is obtained by the semi-supervised regression learning. In the on-line position estimation phase, using the measurement classification and feature extraction of the received RSSI measurement, the position is estimated based on the corresponding position regression function. The field tests show that the proposed algorithm can obtain more accurate position estimation than other existing ELM based localization approaches do.

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt properly, the affected person’s survival rate usually does no longer exceed half a year. This can rapidly cause dying. For this reason, an automatic system is desirable for locating brain tumors at the early stage. In general, when compared to computed tomography (CT), magnetic resonance image (MRI) scans are used for detecting the diagnosis of cancerous and noncancerous tumors. However, while MRI scans acquisition, there is a chance of appearing noise such as speckle noise, salt & pepper noise and Gaussian noise. It may degrade classification performance. Hence, a new noise removal algorithm is proposed, namely the modified iterative grouping median filter. Further, Maximum likelihood estimation-based kernel principal component analysis is proposed for feature extraction. A deep learning-based VGG16 architecture has been utilized for segmentation purposes. Experimental results have shown that the proposed algorithm outperforms the well-known techniques in terms of both qualitative and quantitative evaluation.

Dynamic principal component analysis (DPCA) and its nonlinear extension, dynamic kernel principal component analysis (DKPCA), are widely used in the monitoring of dynamic multivariate processes. In traditional DPCA and DKPCA, extended vectors through concatenating current process data point and a certain number of previous process data points are utilized for feature extraction. The dynamic relations among different variables are fixed in the extended vectors, i.e. the adoption of the dynamic information is not adaptively learned from raw process data. Although DKPCA utilizes a kernel function to handle dynamic and (or) nonlinear information, the prefixed kernel function and the associated parameters cannot be most effective for characterizing the dynamic relations among different process variables. To address these problems, this paper proposes a novel nonlinear dynamic method, called dynamic neural orthogonal mapping (DNOM), which consists of data dynamic extension, a nonlinear feedforward neural network, and an orthogonal mapping matrix. Through backpropagation and Eigen decomposition (ED) technique, DNOM can be optimized to extract key low-dimensional features from original high-dimensional data. The advantages of DNOM are demonstrated by both theoretical analysis and extensive experimental results on the Tennessee Eastman (TE) benchmark process. The results on the TE benchmark process show the superiority of DNOM in terms of missed detection rate and false alarm rate. The source codes of DNOM can be found in https://github.com/htz-ecust/DNOM .

Autonomous structural health monitoring (SHM) of a large number of bridges became a topic of paramount importance for maintenance purposes and safety reasons. This article proposes a set of machine learning (ML) tools to perform automatic detection of anomalies in a bridge structure from vibrational data. As a case study, we considered the Z-24 bridge for which an extensive database of accelerometric data is available. The proposed framework starts from the stabilization diagram obtained through operational modal analysis (OMA) to perform the clustering of modal frequencies and their tracking by density-based time-domain filtering. The features extracted are then fed to a one-class classification (OCC) algorithm to perform anomaly detection. In particular, we propose two new anomaly detectors, namely, one-class classifier neural network (OCCNN) and OCCNN 2 , that find the normal class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimate. The detection algorithms are then compared with known methods based on the principal component analysis (PCA), the kernel PCA (KPCA), the Gaussian mixture model (GMM), and the autoassociative neural network (ANN). The proposed OCCNN solution presents increased accuracy and F 1 score over conventional algorithms, without the need to set critical parameters, while OCCNN 2 provides the best performance in terms of F 1 score, accuracy, and responsiveness.

Air pollution has become the fourth leading cause of premature death on Earth. Air pollution causes poor health and death; about one case out of every ten deaths worldwide is caused by air pollution, which is six times more than malaria. Human activities are the main cause of air pollution, such as chemical industries, road traffic, and fossil fuel power plants. Over the span of several years, monitoring air quality has become an exigent and essential task. In order to limit the health impact of air pollution and to ensure safe operation of chemical processes, it is necessary to quickly detect and locate instrumentation defects. There are several process monitoring techniques in the literature. Among these techniques is the one selected for this work for the detection and location of sensor faults: the kernel principal component analysis (KPCA) method, which was selected for its primary advantages of easy employment and less necessity for prior knowledge. Using the KPCA method for monitoring nonlinear systems, the calculation cost and the memory size are related to the number of initial data. This is currently a major limitation of the KPCA method, especially in industrial environments. In order to remedy this limitation, in this paper we propose a new method of detection and localization. The key idea of this approach is to extend the method of localization based on the principle of reconstruction-based contributions (RBC) by downsizing the kernel matrix in the characteristic space. The proposed technique is named reconstruction-based contribution reduced rank kernel principal component analysis(RBC-RRKPCA). The approach is demonstrated using real air quality monitoring network data and simulated data from the Tennessee Eastman process (TEP) as a challenging benchmark problem. We also present a comparative study of the performances of the conventional diagnostic technique RBC-KPCA and the proposed technique RBC-RRKPCA. The results in this paper reveal that the proposed technique achieves the highest detection and localization accuracy.

When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge. And when using the data-driven contribution graph method for fault location, due to the influence of the fault variable, the contribution value of the non-fault variable will become larger, which will cause a tailing effect and misdiagnosis. In this paper, a multiblock kernel principal component algorithm (MBKPCA) is proposed. In this algorithm, the variables of the pitch system operation process are divided into several variable blocks based on the historical fault data and perform faults based on the kernel principal component algorithm for each variable block diagnosis. Taking historical data of an area in North China as an actual calculation example, the proposed block kernel principal component analysis algorithm is compared with the traditional kernel principal component analysis algorithm. The results show that the MBKPCA can effectively reduce the “tailing effect” of the fault variable on the non-fault variable when the contribution graph method is used to identify the fault source, and the method can accurately locate the fault source and achieves higher detection accuracy.