Squares-support Vector Machine Research Articles

Lithium-Ion Battery Health State Estimation Based on Feature Reconstruction and Optimized Least Squares Support Vector Machine

Abstract The state of health (SOH) of a battery is the main indicator of battery life. In order to improve the SOH estimation accuracy, a model framework for lithium-ion battery health state estimation with feature reconstruction and improved least squares support vector machine is proposed. First, the indirect health features (HF) are obtained by processing multiple health features extracted from the charging and discharging phases through principal component analysis to remove the information redundancy among multiple features. Subsequently, multiple smooth component subsequences of different frequencies are obtained by using variational modal decomposition to efficiently capture the overall downtrend and regeneration fluctuations of the data. Then, use the sparrow search algorithm to optimize the least squares support vector machine to build an estimation model, predict and superimpose the reconstructed fusion features of multiple feature subsequences. Finally, use the mapping relationship between the reconstructed HF and the SOH for the estimation. The NASA battery dataset and the University of Maryland battery dataset (CACLE) are used to perform validation tests on multiple batteries with different cycle intervals. The results show that the mean absolute error and root mean square error are less than 1% and the method has high-estimation accuracy and robustness.

A generation of synthetic samples and artificial outliers via principal component analysis and evaluation of predictive capability in binary classification models

Unbalanced sample groups tend to yield models with a higher prevalence of predominant classes. A sample group with balanced classes contributes to the development of more robust models with improved predictive capability to classify classes equally. In the literature, two methodologies for sample balancing can be found: elimination (undersampling) and synthetic sample generation (oversampling). Undersampling methodologies result in the loss of real samples, while oversampling methods may introduce issues related to adding non-real signals to the original spectra. To overcome these challenges, this paper aimed to utilize Principal Component Analysis (PCA) for the generation of virtual samples (synthetic samples and artificial outliers) to balance data in multivariate classification models. The proposed methodology was applied to data from mid-infrared spectroscopy (MIR) and high-resolution mass spectrometry (HRMS) with Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) models. The constructed models demonstrate that the addition of virtual samples enhances performance parameters (e.g., false negative rate, false positive rate, accuracy, sensitivity, specificity, among others) compared to unbalanced models, while also mitigating overfitting (a problem found in unbalanced models). Performance parameters exhibited a more significant improvement percentage using the non-linear model (SVM) compared to the linear model (PLS-DA). Furthermore, the created virtual spectra do not introduce new signals, i.e., original, and virtual spectra exhibit a similar spectral profile, differing only in the intensity levels. Finally, all models demonstrated good predictive capability according to permutation testing for the binary model developed in this work, limiting the rate of class permutation retention (between 40 % and 60 % of the y-vector remained in the original class). All created models exhibited accuracy values higher than the accuracy distribution of models with permuted classes for the test group.

Application of four intelligent models for the estimation of formation pressure of natural gas hydrate

Natural gas hydrate is a clean and efficient resource that contains lots of heat energy. The production, processing and transportation of natural gas can be influenced dramatically by natural gas hydrate. Natural gas hydrate will accumulate at the bottom of the pipeline, even block the pipeline and reduce the flow rate of the natural gas. This article presents four intelligent models named GWO-SVM (Grey Wolf Optimizer connected with Support Vector Machine), GWO-LSSVM (Grey Wolf Optimizer connected with Least Squares Support Vector Machine), GA-LSSVM (Genetic Algorithm connected with Least Squares Support Vector Machine), and GWO-RBF (Grey Wolf Optimizer connected with Radial Basis Function) to predict the formation pressure of natural gas hydrate in different systems. In order to determine the degree of influence of each input variable on the formation pressure of natural gas hydrate, this paper uses Spearman correlation analysis method to calculate the correlation coefficients between the formation pressure of natural gas hydrate and various influencing factors. The contradistinction among different intelligent models shows that the GWO-LSSVM model has the exceptional performance of the smallest average absolute relative deviation (AARD=2.89%), the minimum standard deviation (SD=0.05) with all 244 data of natural gas hydrate formation pressure in different systems. When compared to six commonly used empirical models for predicting formation pressure in common natural gas hydrate, the GWO-LSSVM model has the highest prediction accuracy, with an AARD of only 1.82%. Furthermore, when compared to three thermodynamic models employed for predicting formation pressure in sour natural gas hydrate, the GWO-LSSVM model has the highest prediction accuracy, with an AARD of 2.38%. The leverage method demonstrates that four intelligent models have excellent robustness in reflecting the inner relationships among natural gas hydrate formation pressure and influential factors. The four intelligent models for predicting hydrate formation pressure will be helpful to find hydrate formation pressure in different systems.

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Cutting-edge tool wear monitoring in AISI4140 steel hard turning using least square-support vector machine

ABSTRACT This research addresses the critical challenge of tool wear monitoring in AISI4140 steel hard turning through the innovative application of a Least Square-Support Vector Machine (LS-SVM) prediction model. While acknowledging the growing significance of hard turning over conventional grinding techniques, the background also recognizes the challenges posed by accelerated tool wear and decreased productivity. The main goal is to create a reliable tool wear prediction model that is tailored to harsh turning circumstances. Material selection, cutting force measurement, acceleration measurement, and accurate tool wear measurement are all included in the experimental procedures. Using time and frequency domain analyses, the research methodically examines how cutting force and acceleration affect tool wear. For feature selection, non-linear regression is used to determine which parameters have the greatest influence on wear. A comprehensive training and validation procedure, structural clarification, and a detailed mathematical foundation are used in the development of the LS-SVM-based tool wear prediction model. The presentation of the findings and related discussions explores the impact of acceleration and cutting force on wear. The LS-SVM model’s successful application for precise tool wear prediction in AISI4140 steel hard turning is highlighted in the conclusions, demonstrating the model’s potential to improve tool lifespan and manufacturing efficiency.

Estimating Soil Erodible Fraction Using Multivariate Regression and Proximal Sensing Data in Arid Lands, South Egypt

Estimating soil erodible fraction based on basic soil properties in arid lands is a valuable research topic in the field of soil science and land management. The Proximal Sensing (PS) technique offers a non-destructive and efficient method to assess wind erosion potential in arid regions. By using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) models and combining soil texture and chemical properties, determined through Visible-Near Infrared (vis-NIR) spectroscopy in 96 soil samples, this study aims to predict soil erodibility, soil organic matter (SOM), and calcium carbonate equivalent (CaCO3) in arid lands located in Elkobaneyya Valley, Aswan Governorate, Egypt. Results showed that the soil erodibility fraction (EF-Factor) had the highest values and possessed a strong relationship between slope and SOM of 0.01% in determining soil erodibility. The PLSR model performed better than SVM for estimating SOM, CaCO3, and EF-Factor. Furthermore, the results showed that the spectral responses of CaCO3 were observed in separate places in the wavelengths of 570, 649, 802, 1161, 1421, 1854, and 2362 nm, and the wavelengths with SOM parameter were 496, 658, 779, 1089, 1417, 1871, and 2423 nm. The EF-factor shows the highest significant correlation with spectral reflectance values at 526, 688, 744, 1418, 1442, 2292, and 2374 nm. The accuracy and performance of the PLSR model in estimating the EF-Factor using spectral reflectance data and the distribution of data points for both the calibration and validation data-sets indicate a good accuracy of the PLSR model, with RMSE values of 0.0921 and 0.0836 Mg h MJ−1 mm−1, coefficient of determination (R2) values of 0.931 and 0.76, and RPD values of 2.168 and 2.147, respectively.

Comparing visible and near infrared ‘point’ spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness

In this work, visible and near-infrared ‘point’ (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Biochar-enhanced concrete mixes: Pioneering multi-objective optimization

The incorporation of biochar (BC) as a partial substitute for cement in concrete formulations provides a promising pathway for mitigating the environmental impacts associated with carbon dioxide emissions during cement production. To achieve optimal composition of BC-enhanced concrete, multiple objectives including mechanical strength, economic factors, and embodied CO2 must be balanced while considering a multitude of variables constrained by complex non-linear relationships. Here, we introduced an intelligent hybrid optimization algorithm that combines Particle Swarm Optimization (PSO), Least Squares Support Vector Machine (LSSVM), and Non-dominated Sorting Genetic Algorithm II (NSGAII) to predict the performance of BC-enhanced concrete and optimize its mix proportions for multiple objectives. Our results demonstrate that the PSO optimization algorithm for searching LSSVM hyper parameters outperforms other optimization algorithms, exhibiting higher generalization performance and improved overall accuracy (R2 = 0.95). Furthermore, the proposed model framework effectively presents a complete Pareto front for the BC-enhanced concrete mix ratio. This triangular model system for concrete mix ratio can determine the optimal solution, tailored to the preferences of the owner's unit. Ultimately, PSO-LSSVM-NSGAII intelligent hybrid optimization algorithm enhances the efficiency of mix proportion design for BC-enhanced concrete.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BackgroundSurface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. ResultsSerum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SignificanceThe experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Crashworthiness Analysis and Optimization of Thin-Walled Structures Based on the TSSA-LSSVM Surrogate Model

Thin-Walled Structures (TWS) play a crucial role in enhancing both the collision safety and lightweight characteristics of vehicles. Structural design and optimization have demonstrated significant potential in improving their crashworthiness and overall weight reduction. Several interconnected factors, including the cross-sectional shape and parameters of distinctive structural characteristics (known as induction grooves), impact the TWS’ resistance to collisions. Another critical aspect in the optimization of TWS design is striking the delicate balance between the surrogate model’s effectiveness and precision. Taking all these factors into account, this paper conducts an in-depth investigation into the cross-sectional shape, grooves, and other geometric attributes of various TWSs through a comparative analysis. It reveals that the thin-walled structure with a Double-Ribbed Rectangle cross-section (DR-TWS) proves to be the optimal choice, particularly in low-speed axial impact scenarios. Furthermore, we present an enhanced Least Squares Support Vector Machine (LSSVM) method, augmented with an adaptive [Formula: see text]-distribution Sparrow Search Algorithm (TSSA), referred to as the TSSA-LSSVM surrogate model. The optimized design of the DR-TWS is realized through the integration of the TSSA-LSSVM surrogate model with the adaptive hybrid multi-objective particle swarm optimization (AHMOPSO) algorithm. This paper not only provides substantial theoretical support but also offers valuable guidelines for the application of TWSs in the automotive industry.

Antenna Shape Modeling based on Histogram of Oriented Gradients Feature

A least square support vector machine (SVM) model is proposed for shape modeling of slot antennas. The slot image is mapped into the electromagnetic response by the SVM model. A modified shape-changing technique is also proposed to describe the antenna geometry by the quadratic uniform B-spline curve and generate the slot images. In the model, the histogram of oriented gradients feature is extracted from the slot images to show the appearance and shape of the slot. The relationship between the histogram of oriented gradient features and the electromagnetic responses is preliminarily built on SVM and the transfer function. Then a radial basis function network is used for error correction. The effectiveness of the proposed model is confirmed with an example of a tri-band microstrip-fed slot antenna. Compared with the convolutional neural network (CNN), the feature extracted by CNN is substituted by the histogram of oriented gradients feature, and the proposed model shows the same accuracy and the improvement of training efficiency.

Prediction of Depth-Averaged Velocity for Flow Though Submerged Vegetation Using Least Squares Support Vector Machine with Bayesian Optimization

Prediction of Depth-Averaged Velocity for Flow Though Submerged Vegetation Using Least Squares Support Vector Machine with Bayesian Optimization

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO2–Crude Oil Minimum Miscibility Pressure

The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time‐consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2–crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine‐tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO‐LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.

Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals.

Rehabilitation robots have gained considerable focus in recent years, aiming to assist immobilized patients in regaining motor capabilities in their limbs. However, most current rehabilitation robots are designed specifically for either upper or lower limbs. This limits their ability to facilitate coordinated movement between upper and lower limbs and poses challenges in accurately identifying patients' intentions for multi-limbs coordinated movement. This research presents a multi-postures upper and lower limb cooperative rehabilitation robot (U-LLCRR) to address this gap. Additionally, the study proposes a method that can be adjusted to accommodate multi-channel surface electromyographic (sEMG) signals. This method aims to accurately identify upper and lower limb coordinated movement intentions during rehabilitation training. By using genetic algorithms and dissimilarity evaluation, various features are optimized. The Sine-BWOA-LSSVM (SBL) classification model is developed using the improved Black Widow Optimization Algorithm (BWOA) to enhance the performance of the Least Squares Support Vector Machine (LSSVM) classifier. Discrete movement recognition studies are conducted to validate the exceptional precision of the SBL classification model in limb movement recognition, achieving an average accuracy of 92.87%. Ultimately, the U-LLCRR undergoes online testing to evaluate continuous motion, specifically the movements of "Marching in place with arm swinging". The results show that the SBL classification model maintains high accuracy in recognizing continuous motion intentions, with an average identification rate of 89.25%. This indicates its potential usefulness in future rehabilitation robot-active training methods, which will be a promising tool for a wide range of applications in the fields of healthcare, sports, and beyond.

A dual-scale hybrid prediction model for UAV demand power: Based on VMD and SSA optimization algorithm

The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy.

Genetic algorithm based detection of breast cancer using least square-support vector machine classifier

Breast tumors are a dangerous disease among women worldwide. They are the second leading cause of death among all forms of cancers in women. Their early detection is critical to increasing the survival rate of women. Mammography is a reliable screening technique in the early detection of abnormal breast tissue severity. Radiologist abnormalities in the breast tissue, radiologists employ mammography. However, detecting breast abnormalities through digital diagnostic techniques by a radiologist could be time consuming. Consequently, computerized studying of digital mammography has emerged via the development of CAD systems. Several CAD systems have been developed for breast cancer detection. However, obtaining a satisfactory performance of CAD systems is a challenging task. We propose a CAD architecture for the classification of breast tissues as either benign or malignant using an LS-SVM classifier with various kernels namely linear, quadratic, polynomial, MLP, and RBF kernels. From the experimental outputs, it is clear that GA based LS-SVM classifier with RBF kernel outputs classification accuracy of 94.59% for normal/abnormal case classification is better, when it is compared with all other kernels. It is also stated that GA based LS-SVM classifier with RBF kernel produces a better classification accuracy of 98.26% for benign/malignant case classification when it is compared with other reported works.

Optimisation Analysis of Enterprise Environmental Cost Accounting Based on Support Vector Machine Model

Abstract Environmental cost accounting, as a developing field, has been implemented in enterprises for only a brief duration, revealing several areas necessitating enhancements. This paper presents an environmental cost accounting method based on Support Vector Machines (SVM) to address the challenges posed by large and complex data sets in enterprise ecological cost accounting. The technique employs the Radial Basis Function (RBF) kernel to optimize the SVM model, derives the linear regression equation for the Least Squares SVM (LS-SVM) model, and preprocesses enterprise environmental cost data. It integrates Material Flow Cost Accounting (MFCA) to extract essential environmental cost-related data for enterprises. In the empirical application within a tested enterprise, the total cost attributed to resource loss amounted to 1,423,002.55 yuan, representing 4.89% of total expenses, with material costs accounting for the highest share at 86.35%. The analysis suggests that enterprises should prioritize monitoring and managing material costs to minimize resource wastage. Regarding the accounting for external environmental damage, sulfur dioxide and fluoride emissions from material quantity center 1 were identified as the predominant pollutants, exceeding 90% of emissions. This highlights the need for targeted energy-saving and emission-reduction measures for these pollutants to mitigate their environmental impact.

Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification.

Fish head cutting is one of the most important processes during fish pre-processing. At present, the identification of cutting positions mainly depends on manual experience, which cannot meet the requirements of large-scale production lines. In this paper, a fast and contactless identification method of cutting position was carried out by using a constructed line laser data acquisition system. The fish surface data were collected by a linear laser scanning sensor, and Principal Component Analysis (PCA) was used to reduce the dimensions of the dorsal and abdominal boundary data. Based on the dimension data, Least Squares Support Vector Machines (LS-SVMs), Particle Swarm Optimization-Back Propagation (PSO-BP) networks, and Long and Short Term Memory (LSTM) neural networks were applied for fish head cutting position identification model establishment. According to the results, the LSTM model was considered to be the best prediction model with a determination coefficient (R2) value, root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD) of 0.9480, 0.2957, 0.1933, and 3.1426, respectively. This study demonstrated the reliability of combining line laser scanning techniques with machine learning using LSTM to identify the fish head cutting position accurately and quickly. It can provide a theoretical reference for the development of intelligent processing and intelligent cutting equipment for fish.

Least square-support vector machine based brain tumor classification system with multi model texture features

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques.

Prediction of the Productivity Ratio of Perforated Wells Using Least Squares Support Vector Machine with Particle Swarm Optimization

The productivity ratio is a vital metric for assessing the efficiency of perforated completions. Accurate and rapid prediction of this ratio is essential for optimizing the perforation design. In this study, we propose a novel approach that combines three-dimensional finite element numerical simulation and machine learning techniques to predict the productivity ratio of perforated wells. Initially, we obtain the productivity ratio of perforated wells under various perforation parameters using three-dimensional finite element numerical simulation. This generates a sample set for machine learning. Subsequently, we employ the least squares support vector machine (LSSVM) algorithm to establish a prediction model for the productivity ratio of perforated wells. To optimize the parameters of the LSSVM algorithm, we utilize the particle swarm optimization (PSO) algorithm. We compare our proposed PSO-LSSVM model with that established based on other parameter optimization methods and machine learning algorithms, such as Grid search-LSSVM, PSO-ANN, and PSO-RF. Our results demonstrate that the PSO-LSSVM model exhibits rapid convergence, high prediction accuracy, and strong generalization ability in predicting the productivity ratio of perforated wells. This research provides a valuable reference and guidance for optimizing perforation design. Additionally, it offers new insights into predicting the productivity of complex completions.