A New Algorithm of Support Vector Machine Ensemble and Its Application

Fault diagnosis plays an important role during the process of blast furnace ironmaking for producing safety. In this paper the important parameters of puddling process are selected as judgments criterions of fault diagnosis by analyzing the changes of these parameters. Support vector machine (SVM) is used to establish the fault diagnosis model for its suitable characters for fault classification. But the stability and accuracy of model based on single SVM could not meet the needs of practical ironmaking. Therefore, a SVM ensemble based on bagging is presented to establish a novel fault diagnosis system. The real-time producing data are collected in 5# blast furnace of a steel enterprise for training and testing the fault diagnosis models with single SVM and SVM ensemble. The experiments about the comparison between single SVM and SVM ensemble and about the SVM ensembles with different number of individual SVM are made. The experimental results demonstrate that the performance of novel fault diagnosis system based on SVM ensemble is better than the one based on single SVM, and the best fault diagnosis system that can meet the practical needs of ironmaking is found.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Traction machine is the power source of traction elevator, through fault diagnosis of traction machine can effectively reduce the occurrence of elevator safety accidents. In view of the problems of weak fault characteristics of traction machine and the influence of the vibration signal by the transmitting path, which lead to the poor diagnosis effect, a vibration signal acquisition scheme and fault diagnosis method of elevator traction machine based on improved Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Support Vector Machines (SVM) Algorithm are proposed. Firstly, the vibration signal acquisition scheme of traction machine is designed by finite element simulation of vibration response of traction machine by Abaqus. A fault diagnosis method based on improved CEEMD and SVM Algorithm is proposed to solve the problem of weak fault feature and poor fault diagnosis effect of traction machine. The improved CEEMD method introduces the integrated fault factors to calculate the fault feature correlation degree of each IMF component, and sorts them according to the integrated fault factors from large to small, the Intrinsic Mode Functions (IMF) with larger integrated fault factors is selected and its time-frequency characteristic is calculated to construct the feature vector, and then the tractor fault is identified and classified by SVM. Finally, the effect and recognition rate of this method and the unimproved CEEMD method are compared by fault injection test. Experimental results show that the CEEMD method based on integrated fault factors can effectively extract the fault features of the tractor and improve the recognition rate of the tractor fault diagnosis.

This document discusses the support vector machine (SVM) algorithm, then discusses least squares support vector machine (LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of temperature signal of turbine blade being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with LVQ neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well as no-linear, high dimension and local minimization problems in the fault diagnosis of temperature signal of turbine blade.

In order to improve the accuracy of fault diagnosis, it is proposed that an RFID fault diagnosis method based on support vector machine and particle swarm optimization. Particle Swarm Optimization (PSO) algorithm is used to optimize the penalty parameters and radial basis kernel functions of Support Vector Machines (SVM). Combined with the example of airport baggage sorting system based on radio frequency identification (RFID), the analysis results show that the RFID fault diagnosis method using SVM and PSO algorithm can diagnose RFID devices more effectively and accurately than SVM algorithm. This method works better than traditional fault diagnosis methods.

Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVMs and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region-computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVMs without losing much generalization performance.

Credit is the main product of savings and loan cooperatives to increase profitability. The greater the credit issued, the greater the benefits obtained by cooperatives. Each cooperative will package credit products in such a way as to attract the attention of every customer. However, cooperatives can find problems in the process of lending, such as the “Daruzzakah Rensing” Cooperative located in “Desa Rensing, Kecamatan Sakra Barat, Lombok Timur-NTB-Indonesia”. The main products of the Cooperative “Daruzzakah Rensing” are savings and loans. In distributing credit, the cooperative always decides based on statistical data. This data is sometimes not useful if the supporting methods used to predict and classify the data are not appropriate. Therefore, this research requires a method that can classify and predict problematic and non-problematic customers. To answer this question, using the SVM (Support Vector Machine) algorithm to find out the level of accuracy in analyzing creditworthiness proposed by prospective debtors. The SVM algorithm is used to predict, classify, evaluate, and analyze credit. From the results of data processing carried out using the SVM algorithm (Support Vector Machine), it can be categorized as an excellent method, with an accuracy of 90.42% and AUC at 0.957. Accuracy of 90.42% means the SVM algorithm can provide decisions about feasible or not feasible in granting credit to customers who apply for loans.

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm.

MS-SVM: Minimally Spanned Support Vector Machine

Dilihat dari sisi konsumen, ada berbagai aspek yang mempengaruhi konsumen untuk membeli suatu kendaraan bermotor. Hal ini karena masing-masing orang memiliki minat dan kebutuhan yang berbeda. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui kelompok umur dan profesi apa saja yang menjadi pelanggan potensial di SPS Motor Honda Lombok Timur. Metode penelitian yang digunakan adalah metode analitik deskriptif, dimana peneliti mengambil data-data penjualan sepeda motor dari SPS Motor Honda Lombok Timur, yang selanjutnya diolah dan dianalisis menggunakan software Rapidminer 5. Hasil analisis data tersebut selanjutnya dideskripsikan dengan detail mengenai kelompok pelanggan potensial. Berdasarkan analisis yang telah dilakukan, dapat ditarik kesimpulan bahwa kelompok yang termasuk dalam pelanggan potensial dari SPS Motor Honda Lombok Timur adalah kelompok dengan kriteria umur berkisar 33-46 tahun yang berprofesi sebagai wiraswasta dan petani, Hal ini disebabkan karena, pelanggan tersebut memiliki penghasilan dengan usia produktif. Sedangkan kelompok pelanggan yang tidak potensial ada 2 irisan yaitu yang pertama apabila kategori usia produktif tapi tidak memiliki penghasilan dengan rentang umur 17-32 tahun dengan profesi pelajar dan wiraswasta. Kedua yaitu, apabila kategori usia tidak produktif dengan rentang usia 47-73 tahun dan tidak memiliki penghasilan dengan profesi petani dan wiraswata.DOI : 10.29408/jit.v2i2.1447

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

The purpose of this study is to determine the prediction of rice farming yields in Padangsidimpuan City through SVM (Support Vector Machine) Algorithms. This type of research used quantitative methods of SVM (Support Vector Machine) with a Data-Driven development (DDD) method. This approach utilized patterns and trends in data to build accurate prediction models where the DDD method can be used when researchers have access to relevant and meaningful data to guide the development of software or prediction models.The SVM algorithm has proven to be effective in predicting rice yield trends, both in determining the direction of change (up or down) and in estimating the value of the next harvest. The implemented SVM model is able to identify patterns of change in historical data and provide relevant predictions for agricultural yields. Historical data covering a fairly long period of time provides sufficient information for models to identify trends and patterns. This model can provide better predictions with more complete and high-quality data.

Prediction of viscosity of imidazolium-based ionic liquids using MLR and SVM algorithms

Introduction Process control and product quality control in petroleum refineries are often performed by means of on-line analysers which provide real-time information to a central system. This allows the adjustment of process variables if process disturbances or set point tendencies arise that differ from desired values. Analytical methods based on near infrared (NIR) spectroscopy combined with chemometric methods have been used as a tool for process analytical chemistry (PAC). This approach can provide useful information about sample composition at petroleum refineries because the various petroleum fractions and their physico-chemical characteristics are associated with different proportions of n-alkanes, iso-alkanes, cycloalkanes and aromatic compounds present in the fractions. Near infrared spectroscopy is able to distinguish between different contents of C–H bonds of methyl and methylene groups present in these compounds together with N–H, S–H and O–H bonds present in minor quantities. The partial least squares (PLS) algorithm is the one most commonly used in multivariate calibration model development. PLS is a linear method that can model some degree of non-linearity using an appropriate number of latent variables. However, certain applications of the support vector machines (SVM) algorithm have been shown to produce results better than those obtained by PLS. Feedstocks for petroleum refineries are often from different geographic regions and possess quite different characteristics. Likewise, petroleum fractions used in the production of derivatives may vary their characteristic properties over a wide range. It is difficult to include in a calibration model all the variability which may occur in practice in the refining process by means of experimental design and, moreover, complex mixtures such as petroleum fractions may not have a linear relationship with the parameter under study. For these reasons, it becomes necessary to use analytical methods that can provide non-linear calibration models with low prediction errors even for samples which have not been included in the calibration analytical range. One such method uses the SVM algorithm; it is based on statistical learning theory and can provide non-linear calibration models with high generalisation performance. These features make SVM an attractive option for the development of calibration models more effective for on-line analysis of petroleum, petroleum fractions and petroleum derivatives at petroleum refineries. The SVM algorithm was originally developed to solve pattern recognition problems and was subsequently extended to handle regression problems. Support vector machines for classification or support vector classification (SVC) simultaneously minimises the empirical error of classification and maximises the separation margin between classes using an optimal separation hyperplane, leading to a unique solution. One of the major features of SVC models is that they can operate in a kernelinduced feature space allowing non-linear modeling while good generalisation performance can be obtained even with relatively small datasets. These characteristics mean that SVC can provide a better classification performance than linear classification algorithms such as soft independent modeling of class analogy (SIMCA). To deal with regression problems, a modification of the classification algorithm was performed; in support vector regression (SVR), the use of the e-insensitive loss function that limits regression errors and penalises deviations beyond the adjusted limit during model development, helps ensure good performance. Key steps in SVC and SVR model development are the parametric optimisation and appropriate choice of the kernel function. This work reports the performance of SVM applied to NIR spectroscopy data for (1) development of regression and classification models for on-line analysis of petroleum fraction streams and diesel oil at a refinery and (2) compares the SVM predicted results with corresponding data produced by SIMCA and PLS methods.

In this paper, an automatic dialect identification (ADI) system is proposed by extracting spectral and prosodic features for Kannada language. A new dialect dataset is collected from native speakers of Kannada language (A Dravidian language). This dataset includes five distinct dialects of Kannada language representing five geographical regions of Karnataka state. Investigation of the significance of spectral and prosodic variations on five Kannada dialects is carried out. Mel-frequency cepstral coefficients (MFCCs), spectral flux, and entropy are used as representatives of spectral features. Besides, pitch and energy features are extracted as representatives of prosodic parameters for identification of dialects. These raw feature vectors are further processed to get a new derived feature vectors by using statistical processing. In this paper, a single classifier based multi-class support vector machine (SVM) and multiple classifier based ensemble SVM (ESVM) techniques are employed for classification of dialects. The effectiveness and performance evaluation of the explored features are carried out on newly collected Kannada speech corpus, with five Kannada dialects and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Experimental results have demonstrated that the derived feature vectors performs better when compared to raw feature vectors. However, ESVM technique has demonstrated better performance over a single SVM. Spectral and prosodic features have resulted individually with the dialect recognition performance of 83.12% and 44.52% respectively. Further, the complementary nature of both spectral and prosodic features is evaluated by combining both feature vectors for dialect recognition. However, an increase in dialect recognition performance of about 86.25% is observed. This indicates the existence of complementary dialect specific evidence with spectral and prosodic features. The experiments conducted on standard IViE corpus have shown a higher recognition rate of 91.38% using ESVM. Proposed ADI systems with derived features have shown better performance over the state-of-the-art i-vector feature based systems on both datasets.