Optimized Relevance Vector Machine Research Articles

Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models

Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models

Pattern and predictors of death from aluminum and zinc phosphide poisoning using multi-kernel optimized relevance vector machine

The use of metal phosphides, particularly aluminum phosphide, poses a significant threat to human safety and results in high mortality rates. This study aimed to determine mortality patterns and predictive factors for acute zinc and aluminum phosphide poisoning cases that were admitted to Menoufia University Poison and Dependence Control Center from 2017 to 2021. Statistical analysis revealed that poisoning was more common among females (59.7%), aged between 10 and 20 years, and from rural regions. Most cases were students, and most poisonings were the result of suicidal intentions (78.6%). A new hybrid model named Bayesian Optimization-Relevance Vector Machine (BO-RVM) was proposed to forecast fatal poisoning. The model achieved an overall accuracy of 97%, with high positive predictive value (PPV) and negative predictive value (NPV) values of 100% and 96%, respectively. The sensitivity was 89.3%, while the specificity was 100%. The F1 score was 94.3%, indicating a good balance between precision and recall. These results suggest that the model performs well in identifying both positive and negative cases. Additionally, the BO-RVM model has a fast and accurate processing time of 379.9595 s, making it a promising tool for various applications. The study underscores the need for public health policies to restrict the availability and use of phosphides in Egypt and adopt effective treatment methods for phosphide-poisoned patients. Clinical suspicion, positive silver nitrate test for phosphine, and analysis of cholinesterase levels are useful in diagnosing metal phosphide poisoning, which can cause various symptoms.

A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices

Accurate forecasting of precious metal prices will greatly assist investors, account managers, and mineral enterprises in making sound decisions and market assessments, while further improvement in the accuracy of precious metal price forecasting is challenging due to its non-linear oscillating characteristics. This paper proposes a novel three-stage hybrid learning paradigm for accurately predicting the prices of three precious metals, i.e., silver, palladium, and platinum. Firstly, the original price series will be decomposed by the complementary ensemble empirical mode decomposition (CEEMD) into several subsequences. Then the subsequences are reprocessed by secondary decomposition (SD) and sequence reconstruction using WPD and permutation entropy to reduce data noise and repetitive modeling. Secondly, all subsequences are fed into the relevance vector machine improved by the African vulture optimization algorithm for prediction to obtain the initial prediction results and the error series. Finally, the error series is further decomposed and predicted by WPD and an optimized relevance vector machine to correct the previously predicted precious metal prices and obtain the final prediction results. The actual precious metal price data are provided as model inputs for multi-step ahead forecasting, and the experimental results show that: the proposed novel learning paradigm achieves MAPE values of 0.2003%, 0.4552%, and 0.2151% for one-step-ahead forecasting of the prices of silver, palladium, and platinum, respectively; the proposed hybrid model performs the best among all the compared models, where hybridization of all the components is essential to improve the prediction accuracy and enables the proposed model to have a higher generalization capability; the results of this paper may provide more scientific insight and reference for precious metals investment and production.

A new feature extraction method of ship radiated noise based on variational mode decomposition, weighted fluctuation-based dispersion entropy and relevance vector machine

Due to the changeable circumstance and the continuous improvement of ship manufacturing technique, ship radiated noise (SRN) has become more and more complex, which makes it more essential to extract its features. Moreover, the target ships can be quickly classified by feature extraction, thus occupying the advantage of maritime confrontation. For the sake of improved feature extraction technology, a new feature extraction method of SRN based on optimized variational mode decomposition by tuna swarm optimization (TSO-VMD), weighted fluctuation-based dispersion entropy (WFDE) and optimized relevance vector machine by sparrow search algorithm (SSA-RVM) is proposed, moreover, the features of seven types of SRN are extracted respectively. To solve the weakness that decomposition level K and penalty factor α must to be preset artificially in VMD, TSO-VMD is proposed. To select optimal width factor and super parameter of RVM, SSA-RVM is proposed. In the first place, decompose SRN into a range of intrinsic modal functions (IMFs) by TSO-VMD, calculate the K-nearest neighbor mutual information (KNN-MI) value between each IMF and the original signal, and normalize it to obtain normalized KNN-MI (nor-KNNMI). Next, select the IMF with the largest nor-KNNMI value as the feature vector, and 60 samples are randomly selected from the feature vector and their fluctuation-based dispersion entropy (FDE) are calculated. Afterwards, select the nor-KNNMI value corresponding to the feature vector to weight the FDE value to obtain the WFDE value, and use the WFDE value to classify SRN. In the end, input the WFDE value of each sample into SSA-RVM for identification. The experimental results show that the identification rate of the proposed method is over 90%. Therefore, the proposed method can accurately and efficiently extract the features of SRN.

Online state-of-health estimation of lithium-ion battery based on relevance vector machine with dynamic integration

The lithium-ion battery’s state of health (SOH) is one of the essential parameters of the battery management system. An accurate state of health estimation of the battery pack helps to improve the service life of the overall battery pack. Given the poor generalization ability of a single data-driven model during SOH online estimation and the lack of uncertainty expression ability in the estimation results, this paper proposes an online SOH estimation method of lithium-ion battery based on bat algorithm optimized relevance vector machine (BA-RVM) with dynamic integration. Firstly, we perform feature extraction and select equal voltage drop discharge time as an indirect health factor. Secondly, we establish the integration model, take the wavelet kernel relevance vector machine (RVM) as the sub-model, and use the bat algorithm (BA) to optimize its kernel parameters to improve the estimation accuracy of the sub-model. Then we use the online monitoring data to update the weights of the sub-models continuously and dynamically integrate the output of the sub-models to improve the accuracy of SOH online estimation further. Finally, the correctness and effectiveness of the method are verified based on battery data from NASA and compared with other data-driven methods. The experimental results show that compared with the method based on a single data-driven model, this method has higher accuracy and more vital generalization ability, and the estimation results have specific uncertainty expression ability.

Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework

This study proposes a hybrid kernel function relevance vector machine (HKRVM) optimized model for battery prognostics and health management. To monitor battery state of health (SOH), two ageing features (AFs) are extracted from the incremental capacity curve to quantify capacity degradation. To further predict remaining useful life (RUL), the AFs are treated with the BOXCOX transformation to enhance the linearity between AFs and cycles. Then, a metabolic extreme learning machine is developed to successionally predict the degradation trends of AFs quickly and accurately. The HKRVM is proposed to capture the underlying relationship between AFs and capacity. To determine the optimal weights and kernel parameters in HKRVM, the biological evolution in the genetic algorithm (GA) is integrated into the grey wolf optimizer (GWO) to further improve the population diversity and optimization performance of the basic GWO.Furthermore, 23 benchmark functions are employed to illustrate the effectiveness of the genetic grey wolf optimizer (GGWO). Finally, the extracted and predicted AFs are fed into the optimized HKRVM for validation. For four battery packs, the mean error of estimated SOH is 2.7773% and the predicted RUL is 21.3333 cycles, which is better than the Gauss process regression, support vector regression, and some particle-filter-based methods.

Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography

The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode. Surface electromyography (sEMG) combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes. sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification. High-order zero-crossing were computed as the sEMG features regarding locomotion modes. Relevance vector machine (RVM) classifier was investigated. Bat algorithm (BA) was used to compute the RVM classifier kernel function parameters. The classification performance of the particle swarm optimization-relevance vector machine (PSO-RVM) and RVM classifiers was compared. The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes: upslope, downgrade, level-ground walking and stair ascent/descent.

Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

The observation data of dam displacement can reflect the dam’s actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel Jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.

Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Condition monitoring and early fault detection of wind turbine faults can reduce maintenance costs and prevent cascaded failures. This article proposes a new Normal Behavior Modeling (NBM) method to predict wind turbine electric pitch system failures using supervisory control and data acquisition (SCADA) information. The proposed method is particularly effective for online monitoring applications at a reasonable computational complexity. Briefly, in the data preprocessing stage of the proposed method, in order to remove interferential information and improve data quality, the operational state codes from turbine programmable logic controller are applied to filter SCADA data. In the modeling process, we designed a NBM method using optimized relevance vector machine (RVM) regression, which is relatively fast and computationally efficient. An adaptive threshold by the probabilistic output of RVM is proposed and used as the rule of anomaly detection. One normal case and three typical fault cases have been studied to demonstrate the feasibility of the proposed method. The performance of the method is assessed using 38 actual pitch system faults compared with two existing methods.

A multi-scale prediction model based on empirical mode decomposition and chaos theory for industrial melt index prediction

Melt index (MI) is one of the most important variables determining the product quality in the industrial propylene polymerization process. In this paper, a multi-scale prediction model is proposed for MI prediction by combining the empirical mode decomposition (EMD), chaos theory and optimized relevance vector machine (RVM) model. First, the EMD method is used to decompose the MI time series into intrinsic mode functions (IMFs) and the residual. Then the chaotic characteristics of each component are identified with chaos theory. For the components with chaotic characteristics, relevance vector machine (RVM) chaotic prediction model is developed as the predictive model. For the components without chaotic characteristics, least squares support vector machine (LSSVM) is used as the predictive model. At the same time, an improved ant colony optimization (IACO) algorithm is used to optimize the parameters of RVM and LSSVM. In the end, the final prediction results of MI are obtained by summing the predicted results of all components. Research on the proposed multi-scale model is carried out on a real propylene polymerization plant and the results are compared among the RVM-chaos, IACO-RVM-chaos and multi-scale models. The research results show that the model developed achieves a good performance in the industrial MI prediction process.

Rotor Position Self-Sensing of SRM Using PSO-RVM

The motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in an intelligent battery management system. The capacity and internal resistance are often used as the batteries health indicator (HI) for quantifying degradation and predicting RUL. However, on-line measurement of capacity and internal resistance are hardly realizable due to the not fully charged and discharged condition and the extremely expensive cost, respectively. Therefore, there is a great need to find an optional way to deal with this plight. In this work, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation modeling and RUL prediction. Moreover, Box-Cox transformation is employed to improve HI performance. Then Pearson and Spearman correlation analyses are utilized to evaluate the similarity between real capacity and the estimated capacity derived from the HI. Next, both simple statistical regression technique and optimized relevance vector machine are employed to predict the RUL based on the presented HI. The correlation analyses and prediction results show the efficiency and effectiveness of the proposed HI for battery degradation modeling and RUL prediction.

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Lithium-ion battery plays a key role in most industrial systems, which is critical to the system availability. It is important to evaluate the performance degradation and estimate the remaining useful life (RUL) for those batteries. With the capability of uncertainty representation and management, Relevance Vector Machine (RVM) becomes an effective approach for lithium-ion battery RUL estimation. However, small sample size and low precision of multi-step prediction limits its application in battery RUL estimation for sparse RVM algorithm. Due to the continuous on-line update of monitoring data, to improve the prediction performance of battery RUL model, dynamic training and on-line learning capability is desirable. Another challenge in on-line and real-time processing is the operating efficiency and computational complexity. To address these issues, this paper implements a flexible and effective on-line training strategy in RVM algorithm to enhance the prediction ability, and presents an incremental optimized RVM algorithm to the model via efficient on-line training. The proposed on-line training strategy achieves a better prediction precision as well as improves the operating efficiency for battery RUL estimation. Experiments based on NASA battery data set show that the proposed method yields a satisfied performance in RUL estimation of lithium-ion battery.