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Photovoltaic (PV) installations have become integral for harnessing solar energy, yet ensuring uninterrupted power generation remains crucial. This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and identify simultaneous faults, using kernel principal component analysis (KPCA) and statistical metrics. The proposed method employs KPCA, a machine learning technique adept at identifying patterns in complex data. By utilizing statistical metrics in a feature space generated by KPCA, potential faults in PV system performance data are flagged. Unlike prior research that focused on single faults, this work extends the application of KPCA to detect and identify multiple faults occurring simultaneously, such as partial shading combined with open or short circuit faults. Through extensive simulations, including 100 samples of different faults under varying irradiance conditions, the method demonstrates high accuracy rates: 93.33% for partial shading, 100% for open circuit, 100% for short circuit, and 81.81% for combinations of partial shading with either open or short circuit faults. Results from a Matlab‐Simulink model validate the effectiveness of KPCA in detecting both single and simultaneous faults in PV systems' DC side. [ABSTRACT FROM AUTHOR]

Gearboxes, as critical components, often operate in demanding conditions, enduring constant exposure to variable loads and speeds. In the realm of condition monitoring, the dataset primarily comprises data from normal operating conditions, with significantly fewer instances of faulty conditions, resulting in imbalanced datasets. To address the challenges posed by this data disparity, researchers have proposed various solutions aimed at enhancing the performance of classification models. One such solution involves balancing the dataset before the training phase through oversampling techniques. In this study, we utilized the Sparse Autoencoder technique for data augmentation and employed Support Vector Machine (SVM) and Random Forest (RF) for classification. We conducted four experiments to evaluate the impact of data imbalance on classifier performance: (1) using the original dataset without data augmentation, (2) employing partial data augmentation, (3) applying full data augmentation, and (4) balancing the dataset while using Kernel Principal Component Analysis (KPCA) for dimensionality reduction. Our findings revealed that both algorithms achieved accuracies exceeding 90%, even when employing the original non-augmented data. When partial data augmentation was employed both algorithms were able to achieve accuracies beyond 98%. Full data augmentation yielded slightly better results compared to partial augmentation. After reducing dimensions from 18 to 11 using KPCA, both classifiers maintained robust performance. SVM achieved an overall accuracy of 98.72%, while RF achieved 96.06% accuracy. [ABSTRACT FROM AUTHOR]

In this paper we describe a method for denoising data using kernel principal component analysis (KPCA) that is able to recover preimages of the intrinsic variables in the feature space using a single line search along the gradient descent direction of its squared projection error. This method combines a projection-free preimage estimation algorithm with an ℓ 1 -norm KPCA. These two stages provide distinct advantages over other KPCA preimage methods in the sense that they are insensitive to outliers and computationally efficient. The method can improve the results of a range of unsupervised learning tasks, such as denoising, and clustering. Numerical experiments in the Amsterdam Library of Object Images demonstrate that the proposed method performs better in terms of mean squared error than the ℓ 2 -norm analogue, as well as in synthetic data. The proposed method is applied to different datasets and the results are reported. [ABSTRACT FROM AUTHOR]

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.

This paper proposes a data-driven prediction scheme for the remaining life of centrifugal pump bearings based on the KPCA–LSTM network. A centrifugal pump bearing fault experiment bench is built to collect data, and the performance of time domain, frequency domain, and time-frequency domain characteristics under different working conditions is analyzed. Time domain characteristics, frequency domain characteristics, wavelet packet decomposition energy characteristics, and CEEMDAN energy features are found to be able to capture fault information under different working conditions. Therefore, 43 sensitive features are determined from the time domain, frequency domain, and time-frequency domain. Through the analysis of XJTU-SY bearing life cycle data and based on the weighted scores of monotonicity, robustness, and trend indicators, twelve outstanding characteristics of the bearing in the whole life cycle are selected, and a one-dimensional feature quantity that can characterize the life-degradation process of the centrifugal pump bearing is constructed after KPCA dimension reduction processing. The LSTM network, sensitive to time series, is selected to predict and analyze the constructed one-dimensional feature trend, and the prediction effects of the BP network and the CNN network are compared. The results show that this method has advantages in prediction accuracy and model training time. [ABSTRACT FROM AUTHOR]

Medical image registration is vital for precise healthcare diagnosis, treatment planning, and disease progression tracking, but traditional methods fail to capture complex spatial transformations and anatomical variations. A Kernel Principal Component Analysis (KPCA) driven Teaching Learning based optimization (TLBO) approach is proposed to overcome these limitations. The proposed approach is categorized into three phases, i.e., pre-processed phase, contour extraction phase with feature extraction using KPCA, and evaluating robust affine transformation parameters leveraging TLBO for accurate alignment. In the pre-processing phase, gaussian filter is applied to remove noise from source and target images, followed by normalization process. Afterwards, contour extraction is carried out to create a feature image that represents the boundaries of an image. Centroid localization is then utilized to compute translation parameters, which determine the spatial alignment between the images. By utilizing KPCA, this method captures non-linear relationships in the data to enhance the representation of image features. TLBO is employed to optimize the rigid transformation parameters to improve the accurate alignment of source and target images. Extensive experiments are carried out on monomodal and multimodal medical images such as CT and MRI taken from the Harvard Brain ATLAS, Kaggle as well as in-house clinical dataset to demonstrate the effectiveness of the proposed approach. The proposed approach significantly outperforms state-of-the-art methods, with improvements of 44.37% in Root Mean Square Error (RMSE), 19.93% in Structural Similarity Index Measure (SSIM), 20.01% in Peak Signal-to-Noise Ratio (PSNR), and 16.21% in Cross Correlation (CC) quality assessment metrics. [ABSTRACT FROM AUTHOR]

The influence of damage on structural characteristic parameters is often obscured by the complex and changeable marine environment, which may lead to damage misjudgment. Aiming at this problem, a time domain structural damage identification method based on exponential autoregressive conditional heteroskedasticity (EARCH) and kernel principal component analysis (KPCA) is proposed. First, the vibration modal component of the structure is obtained by the reduced-order variational mode decomposition method. Then, the EARCH model is established and the damaged feature matrix of the structure is extracted. After that, KPCA after parameter optimization is used to eliminate the influence of environmental changes on damage identification results. Finally, the SPE damage index is constructed to complete the damage identification of the structure. The structural damage identification was carried out on 16 test conditions under different environmental disturbances, based on indoor offshore platform vibration tests. The results show that the accuracy recognized by the proposed method reaches 98%, which is 4% higher than the traditional KPCA method based on EARCH model features, demonstrating the effectiveness of this approach. Furthermore, the method was applied to real offshore platform data, and the results show that the structural damage state is accurately identified even in the presence of typhoon interference, which is 16% higher than the traditional KPCA method, proving its feasibility and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications. [ABSTRACT FROM AUTHOR]

The amount of video data is growing exponentially on a daily basis. Easily available software or mobile applications offer simple tools to perform the forgery in the video. So, before sending these videos from one place to another, it is important to verify them. In this paper, a forgery detection system is proposed to detect the multiple forgeries in the video using the VGG-16 deep neural model and KPCA (Kernel Principal Component Analysis). The proposed system works in four stages. The preprocessing approach is initially employed to extract and resize video frames. Then, a pre-trained VGG-16 model is tuned to extract the visual features from each input frame. A feature selection methodology, such as KPCA, is applied to minimize the dimensions of extracted features. Finally, correlations distribution among the selected features is analyzed to expose the forgeries. The performance of the proposed system is tested on a forged video dataset. The simulation result reveals that it gives better performance in identifying forgeries in the video, with accuracy and precision of 97.24% and 96.86%, respectively. In addition, the significance of the proposed system is that it yields superior results in post-processing operations like noise addition and adjustments to brightness, contrast, and hue. [ABSTRACT FROM AUTHOR]

Because transformer plays an important role in power system, it rarely runs in fault or abnormal state. Therefore, it is difficult to obtain sufficient transformer fault samples. In this paper, aiming at the problem of less fault sample data and data imbalance, a novel data augmentation method based on kernel principal component analysis is proposed to non-linearly map the original data to a high-dimensional feature space. In this way, the new sample data retaining the feature information of the original data can be obtained. Second, the deep residual network is introduced with the identity path to construct the fault diagnosis model, which enables the weight parameters to be effectively transferred and updated. The simulation results show that the proposed method can effectively expand the data samples with high similarity with the original data, and the residual network model has strong feature extraction ability, which improves the accuracy of fault diagnosis.

This paper proposes a data-driven prediction scheme for the remaining life of centrifugal pump bearings based on the KPCA–LSTM network. A centrifugal pump bearing fault experiment bench is built to collect data, and the performance of time domain, frequency domain, and time-frequency domain characteristics under different working conditions is analyzed. Time domain characteristics, frequency domain characteristics, wavelet packet decomposition energy characteristics, and CEEMDAN energy features are found to be able to capture fault information under different working conditions. Therefore, 43 sensitive features are determined from the time domain, frequency domain, and time-frequency domain. Through the analysis of XJTU-SY bearing life cycle data and based on the weighted scores of monotonicity, robustness, and trend indicators, twelve outstanding characteristics of the bearing in the whole life cycle are selected, and a one-dimensional feature quantity that can characterize the life-degradation process of the centrifugal pump bearing is constructed after KPCA dimension reduction processing. The LSTM network, sensitive to time series, is selected to predict and analyze the constructed one-dimensional feature trend, and the prediction effects of the BP network and the CNN network are compared. The results show that this method has advantages in prediction accuracy and model training time.

This paper first demonstrates an enterprise-intelligent financial management platform built on big data technology. Secondly, KPCA is used to preprocess financial data to improve the accuracy of financial data warnings and combined with the XGBoost algorithm to construct a financial warning model to analyze the enterprise’s economic crisis. Finally, the enterprise’s intelligent financial management platform underwent performance testing and prediction analysis. The results show that when the number of concurrent users is 100, the average response time of the platform is 1.94s, and the error rate of the system is about 0.5%. The platform can also predict and analyze financial data of the enterprise, which can help the enterprise better understand its financial situation and formulate a development plan. In summary, accounting automation can promote the development of enterprise financial management innovation. Intelligent financial management platforms can further realize enterprise accounting informatization to help enterprises achieve good economic development.

Chinese mitten crab is one of China's unique aquaculture species, which has significant economic value to the aquatic product market. In order to recognize different individual Chinese mitten crab, this paper proposes a method of first detection and recognition of carapace combined with YOLOv5 (You Only Look Once v5) and principal component analysis (PCA) and its improved method. First, the image of the Chinese mitten crab is obtained through the camera. Secondly, YOLOv5 and transfer learning method are used to detect the target of the Chinese mitten crab, and then the target is automatic cropped according to the detected target frame of Chinese mitten crab carapace. Finally, four methods of KPCA, one-dimensional PCA (1D-PCA), two-dimensional PCA (2D-PCA), and two-way two-dimensional PCA ((2D)2-PCA) were used for matching. The results show that the (2D)2-PCA recognition rate can reach 84.42%, which is 18.27%, 9.128% and 8.689% higher than the other three methods respectively. In addition, the matching speed only takes 1.859 s, compared with the other three methods. The method improves by 86.051 s, 2.562 s and 0.784 s, respectively. Therefore, this method has a better experimental effect in the experiment, and the recognition speed is faster. The results of the study can avoid the economic loss of aquatic products and provide a new research method for the recognition of the Chinese mitten crab carapace. [ABSTRACT FROM AUTHOR]

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients' information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient's information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks. The proposed anomaly-based IDS is divided into three steps: pre-processing, feature selection, and decision. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection step, the proposed approach uses two data-driven kernel techniques: kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results. Therefore, in decision step, in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used. To check the efficiency of the developed detection scheme, a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results. The proposed method achieved 99.9% accuracy, 99.8% specificity, 100% Sensitivity, 99.9 F-score. [ABSTRACT FROM AUTHOR]

In order to strengthen the improvement and optimization of eco-environment management in the Yangtze River Economic Belt, the paper studies the use of KPCA to build a judgment model of eco-environment management capacity, and analyzes the factors affecting the governance capacity in combination with the panel data regression model, so as to propose strategies for improving the governance capacity. The research outcomes indicate that from 2012 to 2021, the environmental and ecological governance capacity of the Yangtze River Economic Belt maintained an upward trend, and the governance capacity of the lower reaches was the best, with Shanghai, Jiangsu and Zhejiang ranking the top three, and Yunnan and Guizhou ranking the worst. The regression coefficients of industrial development, urbanization, economic development and opening to the outside world are − 3.4896, − 3.7891, 2.3489 and 0.1148 respectively. It is required to strengthen environmental and ecological governance from four aspects of industrial transformation, urban construction, green economy and open communication, and improve the eco-environment management capacity and level of the Yangtze River Economic Belt. [ABSTRACT FROM AUTHOR]

The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models. [ABSTRACT FROM AUTHOR]

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vascular vertigo/dizziness is a complex clinical syndrome involving multiple disciplines and specialties, such as neurology and psychiatry. Due to the intricate etiology and the similarity between causes and symptoms, traditional diagnostic methods based on clinical symptoms and signs are often inaccurate. This study aims to establish an effective and accurate intelligent diagnostic method for vascular dizziness to address this issue. Initially, we collected patients’ medical history and biochemical indicators as research indices. To tackle the sample imbalance issue in clinical data, we employed an improved SMOTE(ISMOTE) algorithm to generate minority class data. The enhancement of the ISMOTE algorithm lies in its ability to more effectively identify and generate minority class samples in sparse regions, resolving the issue of traditional SMOTE algorithms potentially neglecting sparse areas when generating synthetic samples. Subsequently, we utilized the Pearson correlation coefficient for feature correlation analysis, screening and analyzing the original features, and identified 13 feature indices. To further improve model performance and simplify the computational process, we applied the KPCA algorithm to these indices for dimensionality reduction, ultimately obtaining three comprehensive feature indices. Finally, we constructed a Stacking ensemble algorithm model comprising base models (including KNN, RF, Naive Bayes, SVM, GBDT, and XGBoost). To optimize the overall model performance, we introduced a fully connected cascade neural network as a meta-layer model and employed grid search and the Levenberg-Marquardt (LM) algorithm to optimize the base models and meta-layer model, respectively. This enabled the Stacking ensemble algorithm better to learn the correlations among predictions from each base model, enhancing the model’s generalization ability. Experimental results demonstrate that the proposed ISMOTE-KPCA-STACKING model exhibits significant advantages in diagnosing vascular vertigo/dizziness, outperforming single base models in multiple evaluation metrics. Furthermore, the model excels in handling imbalanced data and feature selection, providing an effective method for accurately diagnosing vascular vertigo/dizziness.

Various methods are used to obtain a superior palmprint recognition system. After selecting a palmprint image filter, using Gabor orientation scale pairs is an option to support the refinement of the verification process. Many researchers use the [8×5] pair for the value of the Gabor orientation scale in the field of palmprint recognition. However, from the experiments conducted, other Gabor pairs have more impact on system improvement. The problem is to get the most suitable value pairs for palmprint applications, so in this study, a comparison of seven kinds of Gabor pairs is carried out. This Gabor pair being compared applies using original images, PCA dimension reduction, and the Euclidean method. From the research that has been done, the pair of Gabor orientation scale [8 × 7] or image expansion of 56 will have the most significant impact compared to other pairs. Suppose the result of this Gabor pair is [8×7] by using other improvement systems, namely the 3W filter instead of the original image, KPCA to replace the PCA, and the cosine method in the matching method. In that case, it will increase the verification value by 99.611%. The trial value obtained can be an alternative method of choice for improving palmprint recognition.

The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of F 2 and G-mean metrics. [ABSTRACT FROM AUTHOR]

High-speed shaft bearing (HSSB) failures are exorbitant since they lead electrical energy generation to halt suddenly. In order to identify the health condition of the wind turbine and preserve the sustainability of energy production, a nonlinear vibration-based monitoring technique based on kernel principal component analysis (KPCA) has been developed. After extracting degradation characteristics from the time, frequency, and time-frequency domains. The most sensitive features are then fused using KPCA to capture the monitored bearing's operating conditions; this method demonstrated its efficiency in dealing with the nonlinearity of the system. To detect flaws in HSSB and assess whether it is healthy, degraded, or broken, T 2 , and SPE charts have been used. Real run-to-failure data from a wind turbine HSSB is used to validate the proposed technique. The suggested strategy caught the nonlinear relationship in the process variables more successfully than existing techniques, including linear PCA, and demonstrated enhanced process monitoring performance. [ABSTRACT FROM AUTHOR]

Skin cancer detection through dermoscopy images has remained challenging due to higher inter‐class uniformity and intra‐class diversity. Deep convolutional neural networks (CNNs) have recently obtained remarkable attention for automated skin cancer classification; however, most of these methods extract features from the global image of high resolution. One of the major drawbacks of these methods is the downscaling of input images to a low resolution, which leads to information loss. Moreover, the lack of a vast number of dermoscopy images is a concern. To overcome these issues and improve the classification accuracy, we propose a novel method using patch‐based local deep feature extraction. In the proposed method, the features are extracted from different patches of a dermoscopy image using a pre‐trained CNN model and are fused to preserve fine details. The kernel principal component analysis is then employed to select the prominent features, and a feed‐forward neural network is finally used to detect skin cancer. The approach is validated using two benchmark datasets: ISIC 2016 and ISIC 2017. The experimental results show that the suggested method achieves promising results in comparison with state‐of‐the‐art approaches. Moreover, a comparative analysis is made among contemporary pre‐trained CNN models. [ABSTRACT FROM AUTHOR]

This study proposes a data-driven model for monitoring and diagnosis in energy system of papermaking process based on kernel component analysis (KPCA) and the kernel slow feature analysis (KSFA). Four different abnormal patterns are designed, while the false alarm rate (FAR) and the missed detection rate (MDR) are used to evaluate the validity of the proposed model. The performance of KPCA is better than that of conventional PCA and KSFA. The online monitoring and diagnosis analysis of energy utilization are achieved based on the historical data and the proposed model. The production status and the energy consumption level across the whole process can be acquired. The results demonstrate that the proposed method is effective. It can provide the valuable reference foundation for further energy analysis and optimization in papermaking process. [ABSTRACT FROM AUTHOR]

This paper investigates the emergency group decisionmaking problem based on interval-valued Pythagorean fuzzy language sets (IVPFLSs). The frequent occurrence of emergency events can bring huge economic damage to human beings. To reduce the loss, it is very important to make reasonable emergency decisions effectively and timely. In the emergency decision making (EDM), these problems are few studied, such as high dimension problem, data nonlinearity, and correlation. For EDM problems, the advantage of IVPFLSs is that it can reasonably express the evaluation information given by decision makers (DMs) through both qualitative and quantitative aspects. However, if the dimension and nonlinear relationship of the decision data keep growing, and the traditional decision-making methods will fail. The distance measure between decision data is necessary to calculate in the process of dimensionality reduction, and the current research does not propose the definition of IVPFLSs distance measure. On the basis of this, this paper first uses the attributes and DMs as variables to define the standard Euclidean distance measure between IVPFLSs. For nonlinear features, we construct the interval-valued Pythagorean fuzzy language kernel principal component analysis (IVPFLKPCA) model to reduce the dimensionality. What is more, we also obtain the reasonable weight vectors of the attribute and DMs from cumulative contribution rate. For low-dimensional decision data, according to the technique for order performance by similarity to ideal solution method, the best emergency plan is selected for the information variables after dimensionality reduction. In sum, the IVPFL-KPCA model not only avoids multicollinearity and nonlinear separability between decision data, but also obtains reasonable weights. It further improves the efficiency of decisionmaking operations and reduces the difficulty of the algorithm. Finally, an example of earthquake emergency plan is shown to demonstrate the feasibility and practicability of the proposed method. Besides, we also compare it with the existing methods, which proves the effectiveness of the method. [ABSTRACT FROM AUTHOR]

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.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.

The design and optimization of a sinter mixture moisture controlling system usually require complex process mechanisms and time-consuming field experimental simulations. Based on BP neural networks, a new KPCA-GA optimization method is proposed to predict the mixture moisture content sequential values with time more accurately so as to derive the optimal water addition to meet industrial requirements. Firstly, the normalized input variables affecting the output were dimensionalized using kernel principal component analysis (KPCA), and the contribution rates of the factors affecting the water content were analyzed. Then, a BP neural network model was established. In order to get rid of the randomness of the initial threshold and weights on the prediction accuracy of the model, a genetic algorithm is proposed to preferentially find the optimal initial threshold and weights for the model. Then, statistical indicators, such as the root mean square error, were used to evaluate the fit and prediction accuracy of the training and test data sets, respectively. The available experimental data show that the KPCA-GA model has high fitting and prediction accuracy, and the method has significant advantages over traditional neural network modeling methods when dealing with data sets with complex nonlinear characteristics, such as those from the sintering process. [ABSTRACT FROM AUTHOR]

Various methods are used to obtain a superior palmprint recognition system. After selecting a palmprint image filter, using Gabor orientation scale pairs is an option to support the refinement of the verification process. Many researchers use the [8×5] pair for the value of the Gabor orientation scale in the field of palmprint recognition. However, from the experiments conducted, other Gabor pairs have more impact on system improvement. The problem is to get the most suitable value pairs for palmprint applications, so in this study, a comparison of seven kinds of Gabor pairs is carried out. This Gabor pair being compared applies using original images, PCA dimension reduction, and the Euclidean method. From the research that has been done, the pair of Gabor orientation scale [8 × 7] or image expansion of 56 will have the most significant impact compared to other pairs. Suppose the result of this Gabor pair is [8×7] by using other improvement systems, namely the 3W filter instead of the original image, KPCA to replace the PCA, and the cosine method in the matching method. In that case, it will increase the verification value by 99.611%. The trial value obtained can be an alternative method of choice for improving palmprint recognition. [ABSTRACT FROM AUTHOR]

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.

Summary: The mobile welding robot based on a hybrid power drive system is a system with complex control characteristics such as nonlinearity, strong coupling, and disturbance of working environment. These characteristics have different effects on the control performance. In order to improve control characteristics of the mobile welding robot, it is necessary to study an effective evaluation method to explore the influence of these parameters on the control performance of the mobile welding robot. In the process of seam tracking, the operation indexes of welding robot system are determined by comprehensive evaluation. On the premise of ensuring tracking accuracy and stability, the energy distribution of hybrid power system is optimized to improve results of continuous power supply time. Therefore, a comprehensive evaluation of the welding robot is established, which is combined information entropy and Kernel Principal Component Analysis (KPCA). The evaluation parameters are divided into the first‐level evaluation index and the second‐level evaluation index. The evaluation model is established by using information entropy, and the data are linearized and dimensionally reduced by using the KPCA. The results show that the method can well deal with and analyze the nonlinear relationship between various indexes. At the same time, the numerical relationship between performance indexes can be established. On this basis, a comprehensive performance evaluation theory based on information entropy, and nuclear principal components is established. Finally, the comprehensive evaluation results are brought into the energy management optimization strategy. The experimental results show that this method not only improves the accuracy of weld tracking, but also ensures the stability of the robot and reduces energy consumption. [ABSTRACT FROM AUTHOR]