Parameters Of Support Vector Machine Research Articles

A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

International Electrotechnical Commission (IEC) proposed the IEC three-ratio method based on Dissolved Gas Analysis (DGA), which is one of the most effective tools for Power Transformer Fault Diagnosis (PTFD). However, the PTFD accuracy is generally limited because the classification boundary could be too stiff to classify samples located near the boundary. The Support Vector Machine (SVM) was applied to PTFD to improve diagnosis accuracy, while traditional SVM multi-classification methods and parameter optimization algorithms are subject to poor training efficiency. As a result, the SVM-based PTFD model is difficult to update frequently with the accumulation of fault data. A new SVM-based PTFD decision framework is proposed in this paper which can significantly boost the training efficiency and ensure the accuracy. In the proposed framework, a multi-step feature extraction process consisting of characteristic gas concentration and its ratios is applied. Based on the feature distribution of various samples, a proper SVM multi-classification method is presented using a hierarchical decision tree structure. In addition, according to the principles of SVM and radial basis kernel function, a Support Vector feature-based parameter optimization algorithm (SVFB) is proposed. IEC TC 10 data and the historical data of online transformer monitoring provided by the State Grid Corporation of China are adopted as sample sets. The simulation results demonstrate that the proposed decision framework can reach high diagnosis accuracy while shortening the training time.

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

AbstractTo identify the varieties of red jujube rapidly and nondestructively, hyperspectral imaging (HSI) technology was applied in this article. Hyperspectral data of 480 samples with four different varieties were acquired in the range of 400.68–1001.61 nm. First, Savitzky–Golay and standard normal variable were utilized to process raw spectra. Afterward, a novel method combining competitive adaptive reweighted sampling and iterative retained information variable (CARS‐IRIV) was proposed to select feature wavelengths. The support vector machine (SVM) modeling results indicated that CARS‐IRIV had better information extraction performance and simplified the model. Finally, to further improve the accuracy, sparrow search algorithm (SSA) was adopted to optimize the parameters (c, g) of SVM. The results showed that SSA‐SVM exhibited greater accuracy than other compared models, and the accuracy of training and test sets were 100% and 96.68%, respectively. It confirmed that HSI technology coupled with CARS‐IRIV‐SSA‐SVM can effectively identify varieties of red jujube.Practical ApplicationsThe traditional ways of classifying red jujube varieties are destructive and laborious. Therefore, HSI technology was adopted to overcome the above shortcomings. In this article, the best identification performance was based on the CARS‐IRIV‐SSA‐SVM model with an identification accuracy of 96.68%. This study is helpful for the identification of other agricultural product varieties by HSI technology.

A two-stage fault diagnosis methodology for rotating machinery combining optimized support vector data description and optimized support vector machine

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal samples and fault samples as equally important for pattern recognition training. It ignores that the rotating machinery is in a normal state most of the time, and the collected normal samples are far more than the fault samples. Considering this situation, this paper proposes a two-stage intelligent fault diagnosis methodology for rotating machinery combing optimized support vector data description (SVDD) and optimized support vector machine (SVM). Specifically, SVDD is applied for fault detection, and SVM is applied for fault identification. The parameters of SVDD and SVM are optimized by the grasshopper optimization algorithm (GOA). Multiscale entropy (ME) is used for feature extraction, which is the input feature vector of SVDD and SVM. Under the framework of the proposed methodology, the strengths and weaknesses of 8 different entropy-based indicators in feature extraction are explored. The analysis results of three cases prove the availability and universality of the proposed methodology. According to the analysis results of three case analyses, the permutation entropy (PE)-based indicators are not promising for fault detection. Refined composite multiscale fuzzy entropy (RCMFE) is recommended for fault detection and fault identification to generalize the universality. Based on RCMFE, the fault detection accuracy in the three cases is more than 99%, and the fault identification accuracy is more than 94%. This methodology, therefore, provides a powerful tool for rotating machinery fault diagnosis, which is beneficial to practical application.

Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM.

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm−1) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data

The main focus of this study is to develop a system that can accurately detect the presence of fog in real time at a trajectory level. This study leveraged video data from the SHRP 2 Naturalistic Driving Study (NDS). Extensive data reduction steps were taken to classify various levels of foggy weather conditions from the video data to form two unique image data sets. Afterward, features based on the gray level co-occurrence matrix (GLCM) were extracted from the images and used as classification parameters for training support vector machine (SVM) and K-nearest neighbor (K-NN) algorithms. In addition, a convolutional neural network (CNN) was also examined to improve the detection performance. Although the analysis was done initially on a data set consisting of two weather conditions, clear and fog, it has been extended to include different levels of fog, that is, near fog and distant fog. While the accuracy of the first analysis with two categories was approximately 92% and 91% for SVM and K-NN classifiers, respectively, the CNN produced much greater accuracy of 99%. As expected, the accuracy of the second analysis, with more refined weather categories, was relatively lower than the first analysis where CNN, SVM, and K-NN models produced an accuracy of about 98%, 89%, and 88%, respectively. With the rapid advances in connectivity and affordable cameras, the proposed detection models could be integrated into the smartphones of regular road users, creating an effective way to collect real-time road weather information that could be used to improve weather-based variable speed limit (VSL) systems.

Boosted Sine Cosine Algorithm with Application to Medical Diagnosis.

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.

Dam deformation forecasting using SVM-DEGWO algorithm based on phase space reconstruction.

A hybrid model integrating chaos theory, support vector machine (SVM) and the difference evolution grey wolf optimization (DEGWO) algorithm is developed to analyze and predict dam deformation. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method and the kolmogorov entropy method. Secondly, the hybrid model is established for dam deformation forecasting. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM, and the GWO algorithm is improved to realize the optimization of SVM parameters. Prior to this, the effectiveness of DEGWO algorithm based on the fusion of the difference evolution (DE) and GWO algorithm has been verified by 15 sets of test functions in CEC 2005. Finally, take the actual monitoring displacement of Jinping I super-high arch dam as examples. The engineering application examples show that the PSR-SVM-DEGWO model established performs better in terms of fitting and prediction accuracy compared with existing models.

Condition Monitoring of Substation Equipment Based on Machine Vision

This article aims to reach a global level by selecting a Harr-like function with the participation of bubbles and light and shadow on a digital display, improving the fitness function of the genetic algorithm and the crossover change function. The classifier support vector machine parameters were optimized by an improved genetic algorithm. Finally, compared to other identification algorithms, basic component analysis reduces the number of characters through support vector machines and recognizes the target character. The results of the experiment showed that eight sets of experiments were performed on each character. Experiments have shown that character recognition is best achieved using Harr-like software and then SVM classification based on improved gene algorithms. For small samples, the sorting speed is also fast, which can meet the time requirement. In addition, the historical state data of the transformer are analyzed, which is consistent with the manual monitoring results, but the time is shorter; machine vision has been shown to be effective in monitoring the condition of substation equipment.

Improvement and Application of Fractional Particle Swarm Optimization Algorithm

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes.

Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method

The support vector classifier (SVC) is one of the most powerful machine learning algorithms. This algorithm has been accepted as an effective method in three-dimensional geological modeling. Although the model selection has a great impact on the performance of SVC algorithm, most of mining studies have neglected it and used the grid search method. Therefore, in this study, a new approach is proposed for improving the selection of SVC models. This approach uses particle swarm optimization (PSO) to determine the important parameters of SCV such as penalty and kernel parameters. The proposed approach was applied in the modeling process of the Iju porphyry copper deposit to delineate alteration and mineralization zones. The optimal penalty and kernel parameters were found to be 27.2 and 2−4.75 for alteration zone, and 22.72 and 2−6.23 for mineralization zone, respectively. With 97.4% and 97.01% rates of accuracy for mineralization and alteration zones, the PSO results showed reasonable performance in classification. The proposed approach had better accuracy than grid search method. Therefore, because of its better performance, the geological models were developed using the PSO method to be used as a basis for future resource evaluation.

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning.

Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of the brain. Neurologists interpret magnetic resonance images that are often complicated, time-consuming, and contradictory. Using EEG signals, this disease can be analyzed and diagnosed more accurately. However, it is imperative that MS be diagnosed by specialists using assistive technology. Until now, a few methods have been proposed in this field that are sometimes associated with different challenges in analysis. This paper presents a hybrid approach to MS diagnosis in order to decrease classification error rates. Using the proposed method, EEG descriptors in both the time and frequency domains are analyzed. After the feature extraction stage, a modified version of the ant colony optimization method (m-ACO) was used to select the appropriate subset of features. Then, the support vector machine is used to determine whether the disease exists. A metaheuristic algorithm adjusts the support vector machine's parameters to withstand overfitting challenges. Despite a limited number of input channels, significant classification accuracy has been achieved using wavelet analysis techniques, dividing all five subbands of EEG signals, signal windowing, and extracting efficient features from the data. Additionally, alpha, beta, and gamma bands of the signal are important, and the accuracy, sensitivity, and specificity levels are higher than 98.5%. Compared to similar MS diagnostic methods, the proposed method achieved significantly higher diagnostic accuracy. Application and implementation of this method can be effective in treating neurological diseases, including multiple sclerosis.

Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization

To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction.

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology.

The application of Internet of Things technology provides conditions for the systematization of enterprise financial data and the intellectualization of financial management. In this study, support vector machine (SVM) algorithm and genetic algorithm (GA) are combined to obtain the innovation model of enterprise financial information intelligent processing based on GA-SVM optimization algorithm. Nine factors affecting enterprise financial information processing from 2010 to 2018 are selected as influencing factors, and according to the idea and method of data modeling, the simulation experiment of enterprise financial information intelligent processing model is carried out with MATLAB. The results show that the average fitness of GA-SVM is close to the best fitness, indicating that each individual in the population is near the optimal solution. The RMSE of GA-SVM is 0.35%, and the root mean square error obtained is greater than this value, which shows that the application effect of GA-SVM is significantly better than that of SVM using cross validation method, and it is more suitable for precision prediction. At the same time, it also reflects that the optimization of SVM parameters by GA is reliable. The algorithm can be better applied in the intelligent processing of enterprise financial information and can provide a certain reference for enterprises in the financial management based on the Internet of Things.

Motor fault diagnosis based on teaching and learning gray wolf algorithm optimized support vector machine

To quickly and accurately identify motor faults, this paper proposes the gray wolf algorithm based on teaching and learning improvement to adaptively optimize the parameters of the multi-class support vector machine. Finally, based on the improved algorithm, the seven types of fault data such as the air gap eccentricity of the motor, the broken bar of the rotor, the bearing seat damage, and the bearing wear were carried out fault diagnosis experiments, and a comprehensive comparison and analysis were carried out with the widely used algorithm. The results show that the motor fault diagnosis accuracy rate of this method is 97.88%, which is better than other methods in classification accuracy.

Evaluation Model of College English Teaching Effect Based on Particle Swarm Algorithm and Support Vector Machine

Based on the principle of particle swarm algorithm and support vector machine, this article aims to improve the classification performance of college English teaching effect and explores the best support vector machine parameter optimization algorithm to promote college English teaching for the theory and application research of data analysis. First, the advantages and disadvantages of common support vector machine parameter selection methods such as grid search algorithm, gradient descent method, and swarm intelligence algorithm are studied. Secondly, this article has a detailed analysis and comparison of various other algorithms. Finally, the study analyzed the advantages and disadvantages of the quantum particle swarm algorithm, introduced the dual-center idea into the quantum particle swarm algorithm, and proposed an improved quantum particle swarm algorithm. Through simulation experiments, it is proved that the improved quantum particle swarm algorithm is more superior in optimizing the parameters of support vector machine. In general, this paper uses the PSO algorithm to simultaneously solve the SVM feature selection and parameter optimization problems and has achieved good results. Within the scope of the literature that the author has, there is still a lack of work in this area. Compared with the existing algorithms, the algorithm proposed in this paper has stronger feature selection ability and higher efficiency.

Research on Fault Diagnosis Method of Rolling Bearing Based on Feature Optimization and Self-Adaptive SVM

Aiming at the problems of the difficulty of extracting the fault sensitive features in the mixed domain of bearings under complex working conditions and the lack of self-adaptability of support vector machine (SVM) parameters, to solve these problems, we investigate a diagnosis method combining optimal feature selection and self-adaptive SVM. Firstly, the optimal feature space through multicluster feature selection (MCFS) is constructed. Then, the differential evolution (DE) strategy is used to improve the search performance of gray wolf optimizer (GWO) to improve the diagnosis accuracy of SVM and make it strong self-adaptability. The experimental results show that, with a small number of sensitive features containing significant category differences, the proposed method not only guarantees the construction of the optimal feature space under the minimum feature dimension, but also greatly improves the accuracy of fault recognition. Simultaneously, compared with the traditional feature reduction method (PCA), the proposed MCFS combined diagnosis model improves the accuracy from 98.5% to 100% with 1.5%. Concurrently, compared with MPA-SVM, GWO-SVM, and PSO-SVM, the convergence performance is improved by 61.54%, 78.26%, and 92.64%, respectively. It can seek the best classification performance in the shortest time. The effectiveness and superiority of the proposed method are fully verified.

Optical fringe analysis using support vector machine

An optical fringe analysis is very important in many scientific fields, such as optical metrology, rheology, and Fourier optics. It is proposed to analyze optical fringe patterns, obtained by a diffraction grating and Michelson’s interferometer, by treating them as training datasets for a multiclass classification problem, which is solved by using a support vector machine (SVM). The relation between a decision boundary function obtained by SVM and various optical parameters in the lab coordinate system is derived. The information that is extracted by the SVM algorithm is (1) the orientation angle of straight-line fringes; (2) the location of a smeared diffraction spot; (3) upper bounds on the spot size of each diffracted order; (4) the ratio between laser beam propagation distances; (5) the ratio between the radii of circular fringes of a Michelson’s interferogram; and (6) the orientation of elliptic fringes.

An instruction detection system with Support Vector Machine, Cuckoo-Genetic algorithm and principal component analysis

Nowadays, with the abundant growth of internet users, attacks on computer systems have dramatically increased. This condition might increase the risk of security for Internet users or networks systems. Thus, Intrusion Detection Systems (IDS) is used for detection, identification and diagnosis of security issues on computer networks .As a data mining technique, Support Vector Machine (SVM) is considered in the design and implementation of IDS.SVM’s performance is influenced by its parameters and its input feature space respectively. So, in order to reach and achieve a reasonable efficiency of SVM, two values should be optimized: values of parameters for better accuracy, and set of input feature for shorter training time. In this paper, we used Principal Component Analysis (PCA) for feature extraction and dimension reduction of input data and the Cuckoo-Genetic algorithm, as a meta-heuristic optimization technique, to determine the optimum parameters of SVM for classification of normal and malicious data. The practical results, by using proposed method on NSL-KDD dataset, indicates that the proposed method reached a better detection rate Compared to genetic and cuckoo.

Research and Application of Power Grid Maintenance Scheduling Strategy under the Interactive Mode of New Energy and Electrolytic Aluminum Load

Formulating a reasonable and feasible unit maintenance scheme is a promising way to eliminate potential risks and improve the reliability of power systems. However, the uncertainty and volatility of new energy outputs, such as wind power, increase the difficulty of scheme formulation. To overcome the complexity of uncertainty, a robust unit maintenance scheme considering the uncertainty of new energy output and electrolytic aluminum load is established in this paper. Considering the significant time-series characteristics of new energy, this paper first introduces the definition and mathematical model of information granulation (IG), through which the initial new energy output data can be transformed into fuzzy particles used for prediction and analysis. Moreover, a support-vector machine (SVM) regression prediction model is adopted, and a corresponding progressive search algorithm is designed to determine SVM parameters efficiently. Then, a robust unit maintenance model is established considering the upper and lower predicted error. In addition, electrolytic aluminum loads are allowed to participate in power system dispatch. Finally, the modified reliability test system–Grid Modernization Lab Consortium (RTS–GMLC test system) and an actual power grid in Southwest China are used to verify the accuracy and feasibility of the proposed method.