Non-Linear Dimensionality Reduction and Gaussian Process Based Classification Method for Smoke Detection

Aimed at the nonlinear properties of analog circuits features , kernel Principal Component Analysis(KPCA) combined with Support Vector Machine(SVM) based diagnostic method for faults of analog circuit was proposed in this paper. Here, uses KPCA to reduce the irrelevant features. The KPCA method is used to extract the initial features. The SVM is then applied to the circuit after features extraction. It can be used as a new idea for Fault Diagnosis of Analog Circuits. To better verify the superiority of the proposed method, KPCA+SVM classification results were compared with the results of PCA+SVM. It is concluded that KPCA+SVM classifier achieved a better performance than PCA+SVM in the terms of the accuracy.

In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. An integrated method is applied for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. The experimental results show that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.

Aimed at the non-stationary characteristics of rolling bearing vibration signal,a fault diagnosis method was proposed based on local-wave method and KPCA(kernel principal component analysis)-LSSVM(least squares support vector machine).Firstly,local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function(IMF),whose feature energy,singular values and AR model parameters were computed as initial feature vectors.Secondly,ini tial feature vectors were mapped into a higher-dimensional space with KPCA,and the kemel principal components were extracted as new feature vectors,which used as the input of LSSVM for fault classification.The experimental results show the KPCA-LSSVM method improves LSSVM's classification performance by KPCA obtaining additional discriminative information,and has better generalization than direct LSSVM method,and can identify rolling bearing fault patterns more accurately.

Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM–KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and to ameliorate the generalization of the SVM–KPCA algorithm. Three databases are used to validate the suggested SVM–KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier’s performance compared with similar algorithms working on the proposed database. Second, the SVM–KPCA algorithm performs well in differentiating between two classes of medical images. Third, the approach is robust and might be utilized for other MRIs. This proposes a significant role for computer aided diagnosis analysis systems used for clinical practice.

This study investigates stock market portfolio construction that is an interesting and important research in the areas of investment and applications, as it allows more profits and returns at lower risk rate with effective portfolios. To construct accurate prediction and thus good portfolio construction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. This paper introduces a machine learning method of Support Vector Machine (SVM) to construct a stocks portfolio by selecting well-performing firms in Casablanca Stock Exchange (Casablanca S.E). This model can perform a nonlinear classification. However, the accuracy of SVM classification is particularly sensitive to the quality of data. To insure a better performance of the model, we bring the Kernel Principal Component Analysis (KPCA) into SVM to extract the low-dimensional and efficient feature information. As empirical results show, based on SVM, within KPCA, the stock selection model outperforms the MASI index.

Machine learning and pattern recognition recently become a hot topic in computing world. This is due to the fast-growing of resources as well as techniques that make it easier to solve machine learning and pattern recognition problems. Problems that require machine learning solutions may be very simple for humans but actually can be very complex for machines to solve them. Face recognition is amongst those problems. Almost all human can easily recognize others without require specific knowledge to do it, different from machines which require its. This paper discussed face recognition task using machine learning strategies which involved Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to identify person. KPCA extracted features from 2D image input and produced the important features of an image input. The extracted face features are recognized by SVM by classifying human face according to their stored identity in a database. SVM, which was basically a binary classifier worked by using one-against-one strategy to compare the face feature vector of a single test image to the stored face image in a face image database. Experiment results on grayscale images with size 92x112 pixels gave 96.25% of accuracy rate. Hence, KPCA and SVM for face recognition is a robust machine learning method.

Underground metal target detection technology has been widely applied in industrial production, resource exploration, and engineering construction, etc. However, due to the influence of non-negligible noise and high dimensionality in collected data, achieving efficient and accurate underground target classification remains a grand challenge for further applications of underground metal target detection on portable devices with limited computing capability and energy supply. This study aimed to seek out robust and efficient data-based strategies to classify the underground metal targets of different shapes and materials based on electromagnetic induction detection. We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB). Several parameters, including classification accuracy, the number of features after dimensionality, the feature type importance, and the time consumption were considered to evaluate the data-based classification strategies. Among the classification strategies investigated and considering the above evaluation parameters, the artificial neural network (ANN) classifier assisted with the kernel principal component analysis (KPCA) feature extraction method yielded the best performance in the material-based classification (accuracy:0.99) and the shape-based classification (accuracy:0.99). The locally linear embedding (LLE) improved the robustness of machine learning classifiers and efficiency of the artificial neural network in the material-based classification (improvement of average accuracy:0.17, reduction of classification time cost:14%) and shape-based classification (improvement of average accuracy:0.16, reduction of classification time cost:22%). Our comparative investigation provides a robust and efficient data-based strategy for underground metal target classification, which is significant for applications of underground metal target detection on portable devices with limited computing capability and energy supply. The cross-combination strategy of dimensionality reduction methods and machine learning models provides a way to find the optimal machine learning model for underground target detection.

Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection.

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of inverse modeling. First, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Second, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model via interpolation, reducing the number of necessary nodes. Empirical evidence of the performance of these models will be presented through illustrative examples of vegetation monitoring and atmospheric modeling.

Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants

Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions

This paper describes a real-time data collection framework and an adaptive machining learning method for constructing a real-time energy prediction model for a machine tool. To effectively establish the energy consumption pattern of a machine tool over time, the energy prediction model is continuously updated with new measurement data to account for time-varying effects of the machine tool, such as tool wear and machine tool deterioration. In this work, a real-time data collection and processing framework is developed to retrieve raw data from a milling machine tool and its sensors and convert them into relevant input features. The extracted input features are then used to construct the energy prediction model using Gaussian Process (GP) regression. To update the GP regression model with real-time streaming data, we investigate the use of sparse representation of the covariance matrix to reduce the computational and storage demands of the GP regression. We compare computational efficiency of sparse GP to that of full GP regression model and show the effectiveness of the sparse GP regression model for tracking the variation in the energy consumption pattern of the target machine.

Kernel principal component analysis-based Gaussian process regression modelling for high-dimensional reliability analysis

Support Vector Machines and Kernel methods have become very popular as methods for learning from examples. In this paper, in order to solve the non-linear and the time of recognition problem, recent advanced techniques are presented. For example, Kernel Principal Components and Proximal Support Vector Machines, Kernel Principal Components Analysis can describe multiple correlations between pixels in the image recognition, and can handle the high order statistics of the original data, at the same time the nonlinear features of the image can be preferably extracted. Proximal Support Vector Machines is an improvement for these characters of Support Vector Machines, combines the proximal algorithm with Support Vector Machines, has a better ability of nonlinear mapping and a stronger generalization capability, and the pace of Proximal Support Vector Machines is very enhanced. In this paper, the computer simulation was progressed based on ORL face database, the effectiveness of the Proximal Support Vector Machines algorithm and Kernel Principal Components Analysis algorithm were shown by experimental results.