KPCA Method Research Articles

Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

Constructing a Hybrid Algorithm to Model the Physical and Chemical Inspection Station Data of the Shatt Al-Arab Waters*

The aim of this paper is to find a hybrid method between of statistical methods that deal with non-linear high-dimensions. The data often suffer from complexity and overlap problems in their mathematical functions. It is difficult to delineate or accurately determine the effect of each variable on the other, accordingly it was constructing a hybrid model between the KPCA and FCM methods. The KPCA method aims to address the problem of high-dimensional nonlinear data and reduce it by finding a kernel matrix that depends primarily on the smoothing parameter matrix that was estimated using the ROT method. Then, the FCM was adopted to obtain the clusters. This proposal was applied to the water sector in Basrah Governorate through a study of (8) stations for physical and chemical examination through (15) variables for the years (2019, 2020, 2021) and data were collected on a monthly basis. Through the application of this methodology, the paper was able to determine (7) Basic variables, which are (TH, Na, Cl, TDS, No3, EC, O_G). As for the stations, the overlapping stations between the clusters were identified, which are (SH1, SH2, SH3, SH4, E20, T34), as for the best degree of fuzziness it was (3.6) and the best number of clusters is (k = 3).

SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

Multi-objective optimisation research of ship form in waves based on Kernel principal component analysis

ABSTRACT In the process of multi-objective optimisation design of ship forms, subjective weighting methods rely on experts’ experience and have a certain impact on the scientificity of results. The objective weighting method can effectively solve this drawback and reduce the interference of human factors. Therefore, this paper proposes a multi-objective optimisation design programme for ship forms based on KPCA method, and constructs a multi-objective optimisation design framework. In this paper, the KPCA method is used to calculate the contribution rate of each indicator for objective weighting, give the comprehensive evaluation score of the optimal solution set and make decisions, solve the technical drawbacks of subjective weighting, and obtain a satisfactory optimal hull form. The results show that the automatic multi-objective optimisation design method proposed in this paper has certain engineering application value, and can provide theoretical basis and technical support for green design and manufacturing of ships.

Distributed incipient fault detection with causality-based multi-perspective subblock partitioning for large-scale nonlinear processes

Detecting incipient faults is a crucial task in complex large-scale nonlinear industrial processes. However, conventional distributed frameworks encounter challenges in identifying appropriate subblock partitioning methods. To address this problem, we propose a novel distributed incipient fault detection method, called CMPSP-MKPCA, based on causal relationship multi-perspective subblock partitioning (CMPSP) and modified kernel principal component analysis (MKPCA), facilitating incipient fault detection in large-scale nonlinear processes. Firstly, considering the property of incipient faults to undergo limited propagation among process flow connections, we obtain fine-grainehe direction of potential incipient fault information propagation. Secondly, the design of multi-perspective detection blocks provides real-time selection of sub-block delineation results to adapt to the different needs of incipient faults during latency and transition periods. Thirdly, local detection is realized by combining the Modified KPCA method and Kullback-Leibler divergence (KLD), while the global detection statistics is obtained by Bayesian inference fusion. Simulation experiments based on the continuous stirred tank reactor (CSTR) system and the Tennessee Eastman (TE) system verify the effectiveness and superiority of the method.

Research on Digital Construction and Design of Minority Clothing Based on Multivariate Statistical Analysis

Abstract Digitizing minority costumes is a modern way to protect non-heritage. In this paper, after analyzing the Hu invariant moment features and HOG features, we propose a feature extraction method for ethnic minority costumes that integrates the fusion features of HOG features and Hu invariant moment features. On this basis, the structure of the extracted multivariate feature data is analyzed, and the KPCA method of multivariate statistical analysis is adopted to process the multivariate data of ethnic minority costumes and digitally construct and design the ethnic minority costumes. In addition, experiments were conducted to explore the effect of minority costumes on feature extraction and the effect of digitization construction and design. The results show that the extraction accuracy of six ethnic costumes is stable at 0.75, 0.45, 0.50, 0.75, 0.72, and 0.68, respectively, and its Top-10 accuracy reaches 0.61 at the highest level, while the accuracy of retrieving only some of them is less than 0.40, and the effects of constructed ethnic costume outlines are all around 0.9. The output results of ethnic dresses constructed based on this research are roughly similar to the original ethnic dresses, which is conducive to the inheritance of ethnic dress culture.

Improving the Pulmonary Nodule Classification Based on KPCA-CNN Model

Lung cancer mortality, the main cause of cancer-associated death all over the world, can be reduced by screening risky patients with low-dose computed tomography (CT) scans for lung cancer. In CT screening, radiologists will have to examine millions of CT pictures, putting a great load on them. Convolutional neural networks (CNNs) with deep convolutions have the potential to improve screening efficiency. In the examination of lung cancer screening CT images, estimating the chance of a malignant nodule in a specific location on a CT scan is a critical step. Low-dimensional convolutional neural networks and other methods are unable to provide sufficient estimation for this task, even though the most advanced 3-dimensional CNN (3D-CNN) has extremely high computing requirements. This article presents a novel strategy for reducing false positives in automatic pulmonary nodule diagnosis from 3-dimensional CT imaging by merging a kernel Principal Component Analysis (kPCA) approach with a 2-dimensional CNN (2D-CNN). To recreate 3-dimensional CT images, the kPCA method is utilized, with the goal of reducing the dimension of data, minimizing noise from raw sensory data while maintaining neoplastic information. The CNN can diagnose new CT scans with an accuracy of up to 90% when trained with the regenerated data, which is better than existing 2D-CNNs and on par with the best 3D-CNNs. The short duration of training, and certain accuracy shows the potential of the kPCA-CNN to adapt to CT scans with different parameters in practice. The study shows that the kPCA-CNN modeling technique can improve the efficiency of lung cancer diagnosis.

Data-driven identification model for associated fault propagation path

In this paper, a data-driven identification model of the associated fault propagation path is proposed. Different from traditional fault diagnosis methods, the alternative approach is focused more on identifying the fault propagation path. Firstly, the KPCA method is used for fault detection. Then, a new transfer entropy method is proposed to construct the causality diagram. Finally, a kernel extreme learning machine-based search approach is proposed for fault propagation path identification. To demonstrate the effectiveness and applicability, the proposed model is applied to the Tennessee-Eastman Process and the real telemetry data of the satellite in orbit. Experimental results show that the number of causal edges obtained by the proposed method is only 12.62% of the traditional transfer entropy method, and 24.45% of the multivariate transfer entropy method.

Predicting the Trend of Dissolved Oxygen Based on the kPCA-RNN Model

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on the kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN’s recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R 2 scores up to 0.908, 0.823, and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like Feed-forward neural network (FFNN), support vector regression (SVR) and general regression neural network (GRNN), the predictive accuracy of the kPCA-RNN model was at least 8%, 17% and 12% better than the comparative models in these three cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

Fault detection of uncertain nonlinear process using reduced interval kernel principal component analysis (RIKPCA)

The interval kernel principal component analysis (IKPCA) is an extension of the KPCA method to deal with data with uncertainties. However, for a large data set the IKPCA method suffers high computation complexity. To avoid these disadvantages, a new fault detection method for uncertain nonlinear process entitled reduced interval kernel principal component analysis is proposed in this paper. The concept of the developed method consists of determine a reduced data set by choosing variables with the variance of highest projection in the direction of the selected principal components. Two RIKPCA models are developed: the first model is based on the midpoints-radii KPCA (RIKPCACR) and the second one is based on the lower and upper bounds of intervals (RIKPCAUL). The purpose of the developed RIKPCA technique is to improve the efficiency of the IKPCA technique and to minimize the computation time. The efficiency of the developed method is illustrated by an application to the Tennessee Eastman process (TEP), and the desired performance is satisfied.

Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved Black Hole Algorithm

Aiming at predicting the key economic and technical indicators (Granularity and Ore content)in the grinding production process, the extreme learning machine (ELM) soft-sensor model with different activation functions on grinding process optimized by improved black hole (BH) algorithm was proposed. Based on the selected auxiliary variables for the soft-sensor model of the grinding, the KPCA method is used to reduce the dimensionality of the high-dimensional data. In order to investigate the influence of different activation functions on the prediction accuracy of the ELM model, seven continuous function (Arctan, Hardim, Morlet, ReLu, Sigma, Sin and Tanh) are used as the activation function of the ELM neural network to establish soft-sensor models respectively. For the shortcomings that ELM model weights and offset values are arbitrarily given so as to result in the low prediction accuracy and low reliability, an improved BH algorithm based on the golden sine operator and the Levy flight operator (GSLBH) was used to optimize the parameters of the ELM neural network. Simulation results show that the model has better generalization and prediction accuracy, and can meet the real-time control requirements of the grinding process.

Feature Extraction of Conducted Electromagnetic Noise Based on KPCA and Its Application in Main Network

In this paper, a new method for conducting EMI noise source identification is proposed. Firstly, the basic principle of the classical KPCA method is analyzed. Then, the core principal component (KPCA) data is selected, and the input space is transformed into the feature space through nonlinear transformation. This method analyzes the relationship between the time and frequency of the electromagnetic interference signal from the frequency domain, so as to extract the time-domain characteristics of the noise signal, and finally diagnose the characteristics that cause the conduction of excessive signal, and according to the characteristics of the noise signal targeted suppression experiment.

Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction

This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image processing and analysis for useful information .And then compare the two methods and know which methods are appropriate to imaging dimensionality reduction The methods were applied to a group of satellite images of an area in the province of Basra, which represents the mouth of the Tigris and Euphrates in the Shatt al-Arab, as well as the water channels permeating Basra Governorate and the water bodies scattered around these channels.In this research, it is shown that the fourth image band is best when using the PCA method the value of it is eigen value was the biggest ,while the KPCA method showed that the third image band was the best, giving the highest latent value. Comparing the two methods using the mean error error (MSE) method, the results showed that the main KPCA method was the best.

A virtual sensing based augmented particle filter for tool condition prognosis

Timely evaluation and prediction of tool condition is critical to establish optimized maintenance plans in order to enhance production, minimize costly downtime. This paper presents an augmented particle filter based on virtual sensing technique with support vector regression (SVR) model to account for uncertainties in the tool condition degradation process. Tool condition is predicted by recursively updating a physics-based tool condition degradation model with virtual measurement approximately estimating tool degradation condition through virtual sensing technique, following a Bayesian inference scheme. Additionally, in order to improve estimation accuracy of virtual sensing model, different state-of-the-art dimension reduction techniques including principal component analysis (PCA) and its kernel version (KPCA), locality preserving projection (LPP) method have been investigated for feature fusion in a virtual sensing model, and the KPCA method performs best in terms of sensing accuracy. Afterwards, virtual measurement is then incorporated into particle filter. The effectiveness of the developed method is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control (CNC) milling machine.

Image de-hazing Method Based on Dark Channel Prior

In this paper, an image de-hazing method based on dark channel prior is discussed. Based on the atmospheric model and physical theory, an improved KPCA method will be used to optimize transmission function instead of filter method before. Meanwhile, a progressive traversal method will be used for estimation of atmospheric light. Also, color domain correction will be added after obtaining the restored image for a better consistency with human visual properties. For different types of fog, such as water-fog in Macau, the method can show a great result.

New fault detection method based on reduced kernel principal component analysis (RKPCA)

This paper proposes a new method for fault detection using a reduced kernel principal component analysis (RKPCA). The proposed RKPCA method consists on approximating the retained principal components given by the KPCA method by a set of observation vectors which point to the directions of the largest variances with the retained principal components. The proposed method has been tested on a chemical reactor and the results were satisfactory.

Incremental Eigenspace Model Applied to Monitoring System

This paper describes a real time feature extraction for real-time surveillance system. We use incremental KPCA method in order to represent images in a low-dimensional subspace for real-time surveillance in the traditional approach to calculate these eigen space models, known as batch PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigen space is only possible when all the images must be kept in order to update the eigen space, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that incremental KPCA has similar accuracy compare to KPCA and more efficient in memory requirement than KPCA. This makes pro-posed model is suitable for real time surveillance system. We will extend our research to real time face recognition based on this research.

Wavelet kernel entropy component analysis with application to industrial process monitoring

Aiming at the features that modem industrial processes always have some characteristics of complexity and nonlinearity and the process data usually contain both Gaussion and non-Gaussion information at the same time, a new process performance monitoring and fault detection method based on wavelet transform and kernel entropy component analysis (WT-KECA) is proposed in this paper. Unlike other kernel feature extraction methods, this method chooses the best principal component vectors according to the maximal Renyi entropy rather than judging by the top eigenvalues and eigenvectors of the kernel matrix simply. Besides, it can denoise and anti-disturb due to the application of wavelet transform. The proposed method is applied to process monitoring in the Tennessee Eastman (TE) process and the fault identification is realized. The simulation results indicate that the proposed method is more feasible and efficient in comparing to KPCA method.

Online prediction model based on the SVD–KPCA method

This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). The proposed SVD–KPCA method uses the Singular Value Decomposition (SVD) technique to update the principal components. Then we use the Reduced Kernel Principal Component Analysis (RKPCA) to approach the principal components which represent the observations selected by the KPCA method.

Sparse representation and blind source separation of ill-posed mixtures

Bofill et al. discussed blind source separation (BSS) of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method (combining K-clustering with PCA) is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A. Some simulations illustrate the availability and accuracy of the method we proposed.