Real-Time Differential Global Poisoning System Stability and Accuracy Improvement by Utilizing Support Vector Machine

Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization–Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization–Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

Addressing the challenges of complex signal characteristics and difficult fault type differentiation in ultrasonic detection, a fault classification method based on Particle Swarm Optimization Support Vector Machine (PSO-SVM) is proposed. This method utilizes the particle swarm optimization algorithm to globally optimize key parameters in the support vector machine model, overcoming the limitations of traditional support vector machines, such as relying on experience for parameter selection and unstable classification performance. Taking experimental data collected from ultrasonic detection as the research object, the original signals are first subjected to feature extraction and preprocessing. Subsequently, PSO-SVM and traditional SVM models are constructed for fault pattern recognition and classification experiments, respectively. By comparing and analyzing the performance of the two methods in terms of classification accuracy and other performance indicators, the experimental results demonstrate that the PSO-optimized SVM model outperforms the standalone SVM method in both fault classification accuracy and overall recognition effectiveness. This verifies the effectiveness and feasibility of the proposed method in ultrasonic detection fault classification.

A method based on compound multiscale permutation entropy, support vector machine, and particle swarm optimization for roller bearings fault diagnosis was presented in this study. Firstly, the roller bearings vibration signals under different conditions were decomposed into permutation entropy values by the multiscale permutation entropy and compound multiscale permutation entropy methods. The compound multiscale permutation entropy model combined the different graining sequence information under each scale factor. The average value of each scale factor was regarded as the final entropy value in the compound multiscale permutation entropy model. The compound multiscale permutation entropy model suppressed the shortcomings of poor stability caused by the length of the original signals in the multiscale permutation entropy model. Validity and accuracy are considered in the numerical experiments, and then compared with the computational efficiency of the multiscale permutation entropy method. Secondly, the entropy values of the multiscale permutation entropy/compound multiscale permutation entropy under different scales are regarded as the input of the particle swarm optimization–support vector machine models for fulfilling the fault identification, the classification accuracy is used to verify the effectiveness of the multiscale permutation entropy/compound multiscale permutation entropy with particle swarm optimization–support vector machine. Finally, the experimental results show that the classification accuracy of the compound multiscale permutation entropy model is higher than that of the multiscale permutation entropy.

This article focuses on classifying a variety of faults in power transformers with high precision, using an Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) system designed for fault diagnosis. The process begins with the identification of five distinct gases dissolved in oil, serving as diagnostic features. Minimal output encoding is then used to construct multiple binary support vector machine (SVM), facilitating a multi-class classification of transformer faults. While other studies often combine traditional Particle Swarm Optimization (PSO) algorithms with SVMs, our approach employs an enhanced PSO algorithm. This improved algorithm allows for the optimization of inertia and learning factors, values of which adapt based on iteration counts. The PSO is then leveraged on the optimization of the penalty factor and the radial basis function of SVM, thereby improving its classification performance. Simulation results indicate that our IPSO-SVM methodology achieves 90% and 92% accuracy in training and testing sets, respectively. This method significantly enhances the accurateness oftransformer malfunction diagnosis, exhibiting superior diagnostic precision compared to traditional power transformer malfunction diagnosis methods.

Time-frequency methods for vibration-based gearbox faults detection have been considered the most efficient method. Among these methods, continuous wavelet transform (CWT) as one of the best time-frequency method has been used for both stationary and transitory signals. Some deficiencies of CWT are problem of overlapping and distortion ofsignals. In this condition, a large amount of redundant information exists so that it may cause false alarm or misinterpretation of the operator. In this paper a modified method called Exact Wavelet Analysis is used to minimize the effects of overlapping and distortion in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm Optimization (PSO) algorithm has been used for this purpose. This method have been implemented for the acceleration signals from 2D acceleration sensor acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir University of Technology. Gearbox has been considered in both healthy and chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with radial basis functions has used the extracted features from exact wavelet analysis for classification. The efficiency of this classifier is then evaluated with the other signals acquired from the setup test. The results show that in comparison of CWT, PSO Exact Wavelet Transform has better ability in feature extraction in price of more computational effort. In addition, PSO exact wavelet has better speed comparing to Genetic Algorithm (GA) exact wavelet in condition of equal population because of factoring mutation and crossover in PSO algorithm. SVM classifier with the extracted features in gearbox shows very good results and its ability has been proved.

The marine vertical centrifugal pump is an important piece of auxiliary equipment for ships. Due to the complex operating conditions of marine equipment and the frequent swaying of the hull, typical pump failures such as rotor misalignment, rotor unbalance and mechanical loosening occur frequently, which seriously affect the service life of the marine vertical centrifugal pump. Based on multi-domain characteristic parameters, a fault identification method combining weighted kernel principal component analysis (WKPCA) and particle swarm optimization support vector machine (PSO-SVM) is proposed in this paper. It can effectively solve the problem of multi-fault classification of the centrifugal pump and provide reference for efficient maintenance of equipment. Firstly, a vertical centrifugal pump test bench is set up to simulate typical faults. The collected original fault data are denoised by Kalman filtering. Then, a multi-domain feature set composed of 20 feature parameters was constructed. However, due to high dimension, data redundancy and calculation time were increased. After dimensionality reduction, a fault feature set with 9 feature indexes was established by combining with the WKPCA method. Finally, the PSO-SVM model is used to realize multi-fault identification, and the recognition results of the traditional support vector machine and the genetic algorithm support vector machine (GA-SVM) are compared to verify the diagnosis results and classification performance of PSO-SVM. The results show that the accuracy of WKPCA and PSO-SVM fault recognition methods based on multi-domain characteristic parameters is 1, and it has good convergence.

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

Sleep quality is one of the most important factors for human physical and mental health. Sleep disorder may increase the risk of developing chronic physical and mental illnesses such as heart failure, coronary heart disease, depression, and bipolar disorder. In addition, sleep disorder also decreases work productivity and increases the risk of traffic accidents. The problem of sleep disorder is usually associated with the irregularity in sleep cycles. People need to get the right proportion of every stages and sufficient number of cycles to obtain a quality sleep. The aim of this study is to examine distinctive features related to sleep stages (wake, light sleep, deep sleep) from heart rate variability (HRV), and evaluate their usefulness to classify sleep stages. We utilize support vector machine (SVM) to classify the sleep stages classification and compare the result with conventional methods. We also utilize particle swarm optimization (PSO) for feature selection. The simulation results show that our proposed sleep classification with SVM and PSO can improve the accuracy of sleep stage classification.

Steel corrosion prediction based on support vector machines

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data . Method : Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors. Modeling was done in Python software with K -Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms . Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion : With less classification error compared to similar studies, the PSO -SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers.

Existing studies have attempted to determine the tool chipping condition using the indirect method of data capture and intelligent analysis techniques considering machine parameters, and tool conditions using signal processing techniques. Due to the obstructive nature of the machining operation, however, it is daunting to use signal capturing to intelligently capture the condition of the tool as well as that of the workpiece. This study aimed to apply some advanced signal processing techniques to the vibration signals captured experimentally during machining operation for the decision making and analysis of tool and workpiece conditions. Vibration signals were captured during turning operations while using four (4) classes of tools, based on their flank wear. The signals were first pre-processed and decomposed using the Empirical Mode Decomposition (EMD) method. The Hilbert–Huang transform (HHT) was applied to the resulting IMFs obtained to compute the feature vectors used to classify the condition of the tool and workpiece. A total of 12 features, consisting of instantaneous properties such as instantaneous energy, instantaneous frequencies, and amplitudes, were obtained for data training and classification of tool conditions. To optimize the classification process, feature selection was performed using a genetic algorithm (GA) to reduce the number of features from 12 to 4 for data training and classification. The feature vectors were first trained for tool classification with a neural network scaled conjugate gradient (SCG) algorithm. The result showed that the model classification error was 0.102. Two other machine learning models, support vector machine (SVM) and K-Nearest Neighbors (KNN), were also implemented for classifying the tool conditions, from the feature vector, to determine the model that most accurately predicted the condition of the tool. To avoid bias and reduce misclassification errors, the k-fold cross-validation technique was applied with ‘k’ taken as 5 and 10. The computed feature vectors were used as inputs to train the machine learning model using both SVM and KNN models to classify the tool and workpiece condition during machining. The error loss of each model was evaluated and plotted to review the performance. The average overall error loss of 0.5031 was observed for the SVM model with 5-fold cross-validation, whereas the error loss of 0.0318 was observed for the KNN model with 5-fold cross-validation. The average overall error loss of 0.5009 was observed for the SVM model with 10-fold cross-validation when trained using the features selected by a genetic algorithm (GA), while the average overall error loss of 0.0343 was observed for the KNN model. The optimal performance of the SVM model was obtained when all features were used for the training, whereas the KNN model performed better when feature selection was implemented. The error losses of the models were evaluated to be less in KNN models, compared to SVM and SCG. The obtained results also showed that the developed KNN models performed 10 times better than the SVM model in predicting the tool condition from the captured vibration signal during the machining process.

Wind speed is the main component of wind power. Therefore, wind speed forecasting is of big importance due to its uses. It permits to plan the dispatch, determine the hours of storage needed, the amount of energy stored that should be used and avoid the big fluctuations in the electrical grid caused by the nature of the renewable energy resources. In this paper, we propose four hybrid models based on Support Vector Machine(SVM) and Artificial Neural Networks(ANNs) or just Neural Networks (NN) for wind speed forecasting. Using the Ordinary Least Squares(OLS) analysis for selecting the parameters more influencing wind speed. Then, a Support Vector Machine and Artificial Neural Networks models are tuned by Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). The performance of these models is evaluated using three statistical indicators: the Mean Square Error(MSE), Mean Error(ME) and Mean Absolute Error(MAE). The results show a better performance of the neural model compared to the support vector machine.

In this paper, the problem of fast time-varying channel prediction is investigated in high-speed railway communication systems. A channel prediction algorithm is proposed based on a support vector machine (SVM) model. In order to further improve the prediction accuracy, the penalty coefficient and Gaussian kernel width of the SVM model are optimized by a genetic algorithm (GA). Simulation results show that the proposed prediction model based on both the SVM and the GA (SVM-GA) has lower prediction error than traditional auto-regressive (AR) and single SVM prediction models. In addition, when the effect of the noise on prediction performance is considered, the SVM-GA prediction model is superior to the AR and the SVM models in terms of normalized mean squared error.

Hypertension is a chronic disease that often goes undetected in its early stages, increasing the risk of complications such as stroke and heart failure. Accurate classification of hypertensive patients is essential to support early intervention and reduce morbidity and mortality. This study aims to evaluate the performance of the Support Vector Machine (SVM) algorithm and to develop a hybrid classification model by integrating Particle Swarm Optimization (PSO) to improve the predictive performance of SVM. The research was conducted using 400 patient records from Medika Palopo General Hospital, equally divided into hypertensive and non-hypertensive groups, with 12 clinical features as input variables. The SVM model was built using a sigmoid kernel with default parameters (C = 1.0, gamma = auto), while the hybrid model utilized PSO to optimize the values of C and gamma. Evaluation results show that the conventional SVM model achieved an accuracy of 61.25%, precision of 63.22%, recall of 56.50%, F1-score of 59.31%, and AUC of 0.6400. After optimization using PSO, the hybrid model significantly improved with an accuracy of 96.75%, precision of 97.14%, recall of 96.31%, F1-score of 96.73%, and AUC of 0.9725. The findings indicate that the hybrid SVM-PSO approach effectively enhances the classification performance of hypertension prediction models and offers promising potential to be developed into an AI-based medical decision support system.

Panax notoginseng slices (PNS) are prepared from the taproot of a rare Chinese herbal plant, Panax notoginseng. The price and efficacy of PNS change depending on its grade, but substandard PNS are more prevalent in the Asian market. In this study, a portable near‐infrared spectrometer was used to collect the spectra of 240 PNS samples divided into four grades. The spectral data were preprocessed by the Savitzky–Golay (SG) filter to eliminate noise interference. Principal component analysis (PCA), competitive adaptive reweighted sampling, and variable combination population analysis were used to extract the feature variables of the spectral data. The selected feature variables were used to establish least squares support vector machine (LSSVM), support vector machine (SVM), and extreme learning machine (ELM) classification models. To further improve the classification accuracy of the most effective of these models, a grey wolf optimizer (GWO) was introduced, and particle swarm optimization (PSO) as well as genetic algorithm (GA) were used to conduct comparative analyses. The results showed that PCA provided accurate identification information of different PNS grades and that the classification effect of the LSSVM model was better than that of the ELM and SVM models. During the optimization process, the optimization accuracy of the GWO was better than that of the PSO and GA systems. Therefore, the optimal classification model was established as GWO–PCA–LSSVM, and the classification accuracy of the test set was 91.67%. Therefore, portable near‐infrared spectroscopy technology can be used to identify the grade of PNS effectively.