Kernel Principal Component Analysis Algorithm Research Articles

Early micro-short circuit fault diagnosis of lithium battery pack based on Pearson correlation coefficient and KPCA

Though lithium batteries have been extensively applied in electric vehicles as the power sources due to the excellent advantages in recent years, the safety risk of them is always existed inarguably. For the effect of early micro-short circuit faults on the electrical characteristics of the batteries is minor, it is hard to be detected and diagnosed timely, as a result it may evolve into direct short circuit and cause severe safety accidents, such as thermal runaway, explosion and so on. This paper proposes a method for diagnosing micro-short circuit fault in battery pack based on Pearson correlation coefficients (PCCs) and kernel principal component analysis (KPCA). Firstly, the voltage values of each battery in various operation states are measured and denoised using a moving filter method, then PCCs between each battery's voltage of the same series branch are calculated to form a dataset. Secondly, the dataset of PCCs is taken to train KPCA algorithm to obtain the control threshold by calculating the squared prediction error (SPE) statistic. After the training process finished, the PCCs of real-time voltage values of the batteries are calculated, then the related SPE statistic is calculated to determine whether the threshold is exceeded or not, meanwhile the contribution graph will be drawn if the threshold is exceeded. Finally, the faulty cell can be cross localized according to the feature of contribution rate in the contribution graph. Experiments are carried out under the condition of Dynamic Stress Test (DST), and comparisons are performed with principal component analysis (PCA) algorithm by using the different input features, such as raw voltage data, Kendall and Spearman correlation coefficient, respectively. The experimental results show that, the proposed method can diagnose the fault as soon as it occurred, and the feature of contribution rate is more obvious than other methods, which is beneficial to the fault localization. Moreover, for the fault judgment only depends on the correlation of the battery's voltages rather than the absolute values of them, which always vary obviously in different operation states, the proposed method can be applied in various engineering scenarios expediently.

Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance

INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.

Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection

<span lang="EN-US">The user experience can be greatly improved by using learning models that have been trained using data from mobile devices and other internet of things (IoT) devices. Numerous efforts have been made to implement federated learning (FL) algorithms in order to facilitate the success of machine learning models. Researchers have been working on various privacy-preserving methodologies, such as deep neural networks (DNN), support vector machines (SVM), logistic regression, and gradient boosted decision trees, to support a wider range of machine learning models. The capacity for computing and storage has increased over time, emphasizing the growing significance of data mining in engineering. Artificial intelligence and machine learning have recently achieved remarkable progress. We carried out research on data poisoning attacks in the FL system and proposed defence technique using uniform manifold approximation and projection (UMAP). We compare the efficiency by using UMAP, principal component analysis (PCA), Kernel principal component analysis (KPCA) and k-mean clustering algorithm. We make clear in the paper that UMAP performs better than PCA, KPCA and k-mean, and gives excellent performance in detection and mitigating against data-poisoning attacks.</span>

Construction and optimization of representative actual driving cycles based on the improved autoencoder

In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.

Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension

Principal Component Analysis (PCA) is a widely used technique for fault detection and diagnosis. PCA works well when the data set has linear characteristics. However, most industrial processes have nonlinear characteristics in their data. Kernel PCA (KPCA) is an alternative solution for such types of data sets. This solution doesn’t come without a cost since one of KPCA’s disadvantages is a large number of observations which results in more occupied storage space and more execution time than the PCA technique. Furthermore, if the data is too large it may minimize the monitoring performance of the KPCA model. Reduced KPCA (RKPCA) is a solution for the conventional KPCA limitations. Firstly, RKPCA can deal with nonlinear characteristics without crucial problems because it is based on the KPCA algorithm with a data reduction part where it keeps most of the data’s information. Thus, by reducing the number of observations RKPCA reduces the occupied storage space and execution time while preserving tolerable monitoring performance. The proposed RKPCA algorithm consists of two parts. First, the large-sized training data set is reduced using the fractal dimension technique (correlation dimension). Afterward, the KPCA model is developed through the obtained reduced training data set. The proposed scheme is applied to the Tennessee Eastman Process and the Cement Plant Rotary Kiln data sets to evaluate its performance in comparison with other algorithms.

Analysis of Factors Affecting the CSI300 Index Based on KPCA and Various Machine Learning Algorithms

This paper utilises Kernel Principal Component Analysis (KPCA) and various machine learning algorithms to analyze the importance of factors affecting the Chinese Securities Index 300 (CSI300). Based on previous research, this paper constructs an indicator system consisting of 4 secondary and 21 tertiary indicators affecting the CSI300. The data is then reduced through KPCA and processed by various machine learning algorithms, including LightGBM, XGBoost, SVM, and Random Forest, to compare their predictive ability and feature importance. The results indicate that: (1) Under appropriate model parameter settings, the LightGBM model performs the best, while the other algorithms, such as the XGBoost, SVM, and Random Forest models, perform worse and with greater variability than the former. (2) This paper identifies the most significant indicator factors that affect the CSI300 index, such as closing price, price-to-book ratio, and turnover rate. Conversely, some factors, such as the buy-to-sell ratio, exhibit lower importance. These research findings have certain reference and guiding significance for improving the accuracy and reliability of stock market forecasting and practical and theoretical research in financial markets.

A Hybrid Machine Learning Algorithm for Detection of Simulated Expiratory Markers of Diabetic Patients Based on Gas Sensor Array

The method for breath detection using gas sensor array is gaining popularity. It was found that acetone can be used as breath marker in diabetic patients. In this article, seven metal oxide gas sensors were used to collect acetone and ethanol gas, which are used to simulate the exhaled breath of diabetic patients to obtain multidimensional response data. The kernel principal component analysis (KPCA) algorithm is used to extract the characteristics from the data collected by the sensor array. The kind of gases is qualitatively identified using the adaptive boosting (AdaBoost) algorithm, and the grid search (GS) method is used to automatically optimize the parameters of AdaBoost algorithm. The quantitative analysis of gas concentration is performed using multivariate relevance vector machine (MVRVM) and it is also trained using the gas sensor array drift dataset at different concentrations from the University of California (UCI) database. The experimental results show that the accuracy of the algorithm in the qualitative identification of acetone and ethanol gas reaches 99.722%, and the root-mean-square errors (RMSE) for quantification of acetone and ethanol gases are 0.027 and 0.030, respectively. The algorithm is used for qualitative identification on the gas sensor array drift dataset at different concentrations with an accuracy of 94.55%, and the RMSE for quantification of acetone and ethanol gases are 11.59 and 8.72, respectively.

EFFECTIVE APPROACH FOR FACE RECOGNITION AND ACTIVE SHAPE 3D MODELS USING KERNEL PRINCIPAL COMPONENT ANALYSIS

Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and surveillance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can be obtained. After that nonlinear principal components, we use GAs to select the optimal feature set for classification.

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Yarn quality is a key indicator in spinning production process, which directly determines the processing quality of the finished fabric. Considering the problems that the yarn quality is difficult to predict due to many influencing factors and widely monitoring dimensions existed. This paper proposes a multi-correlation parameters-based feature subspace model for yarn quality fluctuation prediction method. Firstly, Kernel Principal Component Analysis (KPCA) algorithm is used to analyze the relationship between the relevant parameters affecting yarn quality, and the characteristic subspace of yarn quality fluctuation is reconstructed. Then, a feature subspace-oriented deep learning prediction structure based on Deep Belief Network (DBN) is constructed, and the intelligent prediction model of yarn quality is established by using Particle Swarm Optimization (PSO) algorithm to optimize the modeling parameters. Finally, a case for spinning spun yarn process data set is used to validate the effectiveness of the proposed method, result shows that the proposed method can effectively analyze the multi-correlation parameters fluctuation law, which shows good effect for predicting yarn quality accurately.

Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures.

Condition monitoring of wind turbine based on deep learning networks and kernel principal component analysis

Condition monitoring is frequently hampered by the abominable working conditions of wind turbines (WTs). The present study proposes a new approach for condition monitoring of WT, which involves the convolutional neural network (CNN) that cascades to the long- and short-term memory network (LSTM) with kernel principal component analysis (KPCA). First, the density-based spatial clustering of applications with noise (DBSCAN) method was used to filter data from supervisory control and data acquisition (SCADA) to improve the effectiveness of the data. Then, the KPCA algorithm was applied for performance monitoring and fault prediction of WT by selecting input variables, and the KPCA–CNN–LSTM model was established. Finally, taking a wind farm as an example, the established prediction model was used to analyze multiple components of the WT. The experimental outcomes demonstrate that the proposed model can help not only in monitoring the state of the WT but also in predicting the abnormal operation state of the WT at an early stage, thereby verifying the effectiveness of the proposed method.

Savitzky Golay and KPCA based Optimal Discrete Wavelet Transform Architecture for Image Compression

• In pre-processing, Savitzky Golay filtering technique (SGFT) is utilized to eliminate the noise signals thereby generating accurate results. • Utilizing DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm for post-processing thus obtaining an optimal reconstructed image with high quality. • Comparing our proposed approach with various other techniques to determine the effectiveness of the system. In recent years, the DWT acts as an effective tool in image compression and decomposition. There exist numerous transform techniques for compressing images but there occurs information loss and low picture quality. To overcome such drawbacks this paper aims in developing an optimal reconstructed image with high quality. The proposed approach comprises two significant phases namely the pre-processing and post-processing. During pre-processing, the multispectral images are enhanced by removing the noise signals from the input image. In this paper, a Savitzky Golay filter is employed to eliminate noise signals and smoothing the multi-spectral image for the generation of more accurate results. The pre-processed image is then subjected to post-processing. In post-processing, the DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm is employed to obtain an optimal reconstructed image with high quality and without losing the information. In addition to this, the proposed model is trained using aerial images obtained from the SIPI image database. The simulation metrics are evaluated for various approaches and the results reveal that the proposed approach outperforms other approaches.

Image classification with parallel KPCA‐PCA network

AbstractPrincipal component analysis (PCA) is widely used in computer vision for object detection. In this article, we take advantage of the algorithms of PCA and kernel principal component analysis (KPCA) to construct a deep learning model named parallel KPCA‐PCA network (PK‐PCANet). In the proposed model, both of the given PCA and KPCA algorithm are aiming to calculate the filters that will be used in the following convolution layers. The extracted features from PCANet and KPCANet are fused by the strategy of parallel feature fusion. With the aim of reducing the dimensionality of the learned features, the algorithm of compressed sensing is incorporated in the proposed network. According to the cooperative advantages of deep learning network and compressed sensing, the proposed PK‐PCANet model obtains some improvements in several visual recognition tasks. Extensively experiments are performed on face recognition, hand‐written digit recognition and object classification, and the experimental results on various image classification benchmarks such as Extended Yale B, AR, MNIST, CIFAR‐10, and VOC 2007 validated the efficiency of the proposed method of PK‐PCANet.

Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation

The detection of building wall surface defects is of great significance to eliminate potential safety hazards. In this paper, a research on building wall design defect image recognition based on partial differential equation is proposed. Collect the image data of building surface defects, sample and quantify the collected images, and preprocess the defect images such as digital threshold segmentation, filtering, and enhancement. Then, the improved partial differential equation is used to recognize the image as a whole. The second-order partial differential diffusion equation and the fourth-order partial differential equation are used to recognize the high-frequency and low-frequency bands of the image, respectively. The kernel principal component analysis algorithm is used to transfer the overall image input space to the high-dimensional feature space. The kernel function is used to calculate the inner product in different subband images of the high-dimensional feature space to reduce the dimension of the overall image. The processed coefficients are inversely transformed by nondownsampling contour wave to realize the overall image recognition and ensure that the edge information of the source image does not disappear. Experimental results show that compared with other algorithms, the proposed algorithm has better effect and better stability.

An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.

MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

Data-knowledge-driven diagnosis method for sludge bulking of wastewater treatment process

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.

Fault Diagnosis of Wind Turbine Pitch System Based on Multiblock KPCA Algorithm

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.

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result in higher time complexity and are not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (KPCR) to accomplish Quality-related process monitoring with a lower computational cost. Specifically, the kernel technique is devoted to map the original input into the higher dimensional space to boost the nonlinear ability of the principal component regression (PCR), and then the KPCR is employed to capture the correlation between the input kernel matrix and the output matrix. At the same time, the kernelized input space is decomposed into two orthogonal quality-related and quality-unrelated spaces by SVD, and the statistics of the two spaces are calculated to detect the faults respectively. Compared with other multivariate statistical methods, it has the following advantages: 1) A quality-related kernel principal component analysis (QR-KPCR) algorithm is proposed. 2) Compared with partial least squares method, the recursive process is omitted and the training time is shortened. 3) The model is more concise and the fault detection process is faster. 4) By contrast with other multivariate statistical process monitoring, it has a higher fault detection rate. Experimental results on a widespread example and an industry benchmark verify the effectiveness and reliability of the proposed method.