Clusterization of Pure And Formalin Fresh Noodles with Electronic Nose Based on Kernel Principal Component Analysis

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

In the recognition of digital modulation signals, extracting the characteristics that more easily distinguish the source signals, and have better performance of the signal, the source signals can be identified by using these characteristics. This paper optimized the original 20 features using orthogonal experiment method, and then identified the signals by the neural network, finally compared its results with that of PCA(principal component analysis) and KPCA(kernel principal component analysis) methods. Experimental results show that orthogonal experiment method in Gaussian and multipath environment can optimize the selection of the features to achieve higher recognition rate than the original features. Orthogonal experiment method has a better ability than PCA and KPCA methods in the optimal choice of these 20 characteristics.

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.

Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes

In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error.

This study proposes a diagnosis method for IGBT open circuit fault and sensor drift fault in CRH2 through ensemble empirical mode decomposition (EEMD) and an improved kernel principal component analysis (KPCA) way. EEMD is modified to reduce noise in the current and voltage signals. Subsequently, an improved KPCA method using weight matrix to highlighting fault information is developed to detect and diagnose the composite fault. Then the three-phase current averaging method is applied to perform fault location. Numerical simulation results verify the validity and availability of the composite fault detection method.

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

Application of KPCA combined with SVM in Raman spectral discrimination

The purpose of this research are to analyze the procurement process of raw materials on six precision (on time, right place, right quality, right quantity, right type, and right price), processing activities to earn income and create added value of products, distribution channels in product marketing activities, the roles of supporting services, and consumer behavior of wheat flour and wet noodle. This research uses a case study method at Multi Sari 99 Agroindustry in Panjang District, Bandar Lampung City. Data were collected in January – April 2018 and analyzed descriptively using quantitative and qualitative analyses. The results of this research showed that the six components of raw materials procurement process of Multi Sari 99 Agroindustry had been done precisely. The income earned by Multi Sari 99 Agroindustry was Rp2,009,349.54/100 kg wheat flour. Mi Basah Multi Sari 99 Agroindustry was considered viable since it had possitive added value and profitable as R/C ratio value was more than one. There were two channels in marketing activities of wet noodle product. Supporting services utilized by the agroindustry had given positive impacts toward agroindustry activities. Wheat flour consumers and wet noodles consumers did not understand about food diversification program. Consumers would still consume wet noodles even though they know that the raw materials are imported. Key words : agroindustry, consumer, performance, wet noodle

Data-driven techniques have been receiving considerable attention in the industrial process monitoring field due to their major advantages of easy implementation and less requirement for the prior knowledge and process mechanism. Principal component analysis (PCA) method is known as a popular method for monitoring and fault detection in industrial systems but as it is basically a linear method. However, most practical systems are nonlinear. To make the extension to nonlinear systems, kernel PCA (KPCA) method has been proposed for process modeling and monitoring. We present in this paper an online reduced rank optimized KPCA (RR-KPCA) technique for fault detection in order to extend the advantages of the KPCA models to online processes. Following the fault detection, the identification of the variables correlated to the fault occurred is of great importance. For this purpose, it is proposed to extend the approaches of localization by partial PCA and by elimination in the linear case to the nonlinear case, by exploiting the solution of reduction of the dimension of the kernel matrix in the feature space. The partial RR-KPCA and the elimination sensor identification (ESI-RRKPCA) are generated based on the static RR-KPCA and the online RR-KPCA methods. The idea of these approaches is to generate partial RR-KPCA models with reduced sets of variables. In other words, their goal is to generate indices of fault detection sensitive to certain faults and insensitive to others. The proposed fault isolation methods are applied for monitoring an air quality monitoring network (AIRLOR) data.

We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most significant poses on a data set. We found the better result from the sparsified Kernel PCA method.

With the spread of internet, the applications based on Internet are developing fast. However, it is generally accepted that internet security plays an increasing important role in our information society. As a significant measure, intrusion detection technique seems necessary. The traditional Intrusion Detection System has a high leaking and false alarm rate. We propose a new model based on Kernel Principal Component Analysis and Weight Particle Swarm Optimization-Support Vector Machine in this paper. The Kernel Principal Component Analysis method can be used in extracting the feature of nonlinear datasets, in order to reduce dimension. The Weight Particle Swarm Optimization helps to optimize the parameters in the Support Vector Machine. In the result, the model in this paper has a good performance.

We present a new diagnostic diagram for local ultraluminous infrared galaxies (ULIRGs) and quasars, analysing particularly the Spitzer Space Telescope’s infrared spectrograph spectra of 102 local ULIRGs and 37 Palomar Green quasars. Our diagram is based on a special non-linear mapping of these data, employing the kernel principal component analysis method. The novelty of this map lies in the fact that it distributes the galaxies under study on the surface of a well-defined ellipsoid, which, in turn, links basic concepts from geometry to physical properties of the galaxies. Particularly, we have found that the equatorial direction of the ellipsoid corresponds to the evolution of the power source of ULIRGs, starting from the pre-merger phase, moving through the starburst-dominated coalescing stage towards the active galactic nucleus-dominated phase, and finally terminating with the post-merger quasar phase. On the other hand, the meridian directions distinguish deeply obscured power sources of the galaxies from unobscured ones. These observations have also been verified by comparison with simulated ULIRGs and quasars using radiative transfer models. The diagram correctly identifies unique galaxies with extreme features that lie distinctly away from the main distribution of the galaxies. Furthermore, special two-dimensional projections of the ellipsoid recover almost monotonic variations of the two main physical properties of the galaxies, the silicate and polycyclic aromatic hydrocarbon features. This suggests that our diagram naturally extends the well-known Spoon diagram and it can serve as a diagnostic tool for existing and future infrared spectroscopic data, such as those provided by the James Webb Space Telescope.

The existing recommendation algorithms have lower robustness in facing of shilling attacks. Considering this problem, we present a robust recommendation algorithm based on kernel principal component analysis and fuzzy c-means clustering. Firstly, we use kernel principal component analysis method to reduce the dimensionality of the original rating matrix, which can extract the effective features of users and items. Then, according to the dimension-reduced rating matrix and the high correlation characteristic between attack profiles, we use fuzzy c-means clustering method to cluster user profiles, which can realize the effective separation of genuine profiles and attack profiles. Finally, we construct an indicator function based on the attack detection results to decrease the influence of attack profiles on the recommendation, and incorporate it into the matrix factorization technology to design the corresponding robust recommendation algorithm. Experiment results indicate that the proposed algorithm is superior to the existing methods in both recommendation accuracy and robustness.

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.