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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Programmable mixed-kernel based on MoTe2/MoS2heterojunction for support vector machine learning 认领 引用
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作者 Xinyu Huang Jiapeng Du +3 位作者 Langlang Xu Lei Tong Xiangxiang Yu Lei Ye 《Journal of Semiconductors》 EI CAS CSCD 2026年第3期110-116,共7页
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware... The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe2/MoS2type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability. 展开更多
关键词 programmable mixed-kernel heterojunction support vector machine
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An Optimized Multiclass Support Vector Machine for Recognizing EEG Signals based on a Lower-Limb Exoskeleton 认领 引用
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作者 Kamel K.Mohammed Heba M.Afify Aboul Ella Hassanien 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1605-1620,共16页
The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper... The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper presents a remarkable model to improve the estimation of the EEG signal and further help improve the control performance for the lower-limb prosthesis,and then improve the rehabilitation.It is based on an optimized Multiclass Support Vector Machine(MSVM)using Snake Optimizer(SO)to get the best possible parameter tuning for classifying different cognitive tasks to control of lower-limb exoskeleton.A public EEG dataset for a lower-limb exoskeleton using Motor Imagery(MI)during the control of the prosthesis and attention to gait(Att)on two surfaces,including flat(Experience)and non-flat(Slopes),has been used as benchmark data sets for this work.The results of the proposed model revealed the superiority of this technique in accuracy,compared with two optimization methods,including Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By comparing the outcomes of SO-MSVM with state of the arts,it achieved an accuracy of more than 85%for MI and Att metric,demonstrating intriguing results for solving the rehabilitation challenge.The devised technique could help people with neurological conditions who have trouble using manual controls. 展开更多
关键词 Electroencephalography(EEG) Brain-Machine Interface(BMI) Multiclass Support Vector Machine(MSVM) Genetic Algorithm(GA) Particle Swarm Optimization(PSO) and Snake Optimization(SO)
An Improved Support Vector Machine Method for Fault Diagnosis of Inter-Turn Short Circuit in PMSM with Enhanced Fault Representation 认领 引用
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作者 Yue Su Shukuan Zhang +2 位作者 Jinghao Jiao Jiankang Zhong Qianxi Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期613-629,共17页
This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn shortcircuit(ITSC)faults in permanent magnet synchronous motors(PMSMs).The synergistic of a SABO-optimized VMD for enhance... This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn shortcircuit(ITSC)faults in permanent magnet synchronous motors(PMSMs).The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed.Firstly,mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals.Then,the SABO algorithm is used to optimize the VMD parameters,followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions(IMFs),and the time-domain parameters of the optimal IMF are calculated to obtain feature vectors.Finally,the fault type is predicted using an SVM optimized by the Moth-Flame Optimizer(MFO).Simulation results show that the accuracy of fault diagnosis can reach 93.6%,indicating that the proposed method can achieve accurate diagnosis of ITSC faults and effectively improve the accuracy of fault diagnosis. 展开更多
关键词 Permanent magnet synchronous motor inter-turn short circuit fault support vector machine fault diagnosis
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Prediction of total nitrogen in water based on UV spectroscopy and Bayesian optimized least squares support vector machine 认领 引用 被引量：1
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作者 ZHENG Peichao YANG Qin +3 位作者 LI Chenglin YIN Xukun WANG Jinmei GUO Lianbo 《Optoelectronics Letters》 EI 2025年第11期698-704,共7页
The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei... The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R2=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance. 展开更多
关键词 Bayesian optimization eutrophication total nitrogen tn bayesian optimized least squares support vector machine lssvm least squares support vector machine assessing surface water water quality total nitrogen
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Robust 3D object tracking with X-Triplet markers:A machine learning-based approach 认领 引用
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作者 Cai Meng Yue Zeng +2 位作者 Hongbin Shen Xinliang Deng Diansheng Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期145-154,共10页
Object tracking in 3D space is a classical problem in computer vision.In this paper,an efficient and robust X-Triplet detection method is proposed based on the support vector machine(SVM) and an adjacent matrix for lo... Object tracking in 3D space is a classical problem in computer vision.In this paper,an efficient and robust X-Triplet detection method is proposed based on the support vector machine(SVM) and an adjacent matrix for locating and tracking objects through stereo vision with minimal feature points.The X-Triplet,denoted as Tri-X,is a composite marker consisting of three sequential X-corners.The definition and types of Tri-X markers are introduced at first.Then a fast and robust X-corner detector based on the block search strategy and SVM is proposed to extract X-corner candidates with sub-pixel locations and orientations.Thereafter the X-corner adjacent matrix(XAM) is constructed using the orientation angle error to describe the possibility that any X-corner pair form a valid edge vector.The Tri-X candidates are then extracted efficiently from the XAM.Finally once the Tri-X markers are detected in binocular images,their 6D pose information can be recovered through stereo matching and triangulation technique.When multiple targets are involved simultaneously,different Tri-X markers can be utilized to identify different objects.Experimental results show that the proposed method outperformed the state-of-the-art in terms of both accuracy and efficiency for Tri-X marker detection.In localization precision test,it achieved 0.1 mm error for the position and 1° error for the orientation.Our method exhibits great potential for utilization in user-defined specific tracking tasks,offering flexibility and adaptability to various tracking requirements,especially multi-tool tracking in medical robotics. 展开更多
关键词 Object tracking Localization Tri-X composite marker Support vector machine X-corner adjacent matrix
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Predicting gastrointestinal bleeding and audio biomarkers based on machine learning analysis of bowel sounds 认领 引用
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作者 Lei Zhang Meng-Yue Wang +6 位作者 Shi-Yu Wei Cheng Su Sen-Yi Hu Xiao-Yang Ren Ya-Ping Liu Chang Liu Yong Wan 《World Journal of Gastroenterology》 SCIE CAS 2026年第14期81-95,共15页
BACKGROUND Early diagnosis of upper gastrointestinal bleeding(UGIB)relies on invasive endoscopy and laboratory tests,which carry procedural risks and diagnostic delays.The pathophysiological relationship between bowel... BACKGROUND Early diagnosis of upper gastrointestinal bleeding(UGIB)relies on invasive endoscopy and laboratory tests,which carry procedural risks and diagnostic delays.The pathophysiological relationship between bowel sounds(BSs)as a noninvasive monitoring metric and UGIB remains to be elucidated.AIM To investigate the feasibility of BS acoustic signatures as UGIB screening biomarkers,analyze their pathological correlations with hematological indices,and construct a machine learning-assisted diagnostic model.METHODS A prospective study enrolled 40 UGIB patients(endoscopy-confirmed within 24 hours)and 40 age-/sex-matched healthy controls.BS signals were recorded at the right lower umbilical quadrant using a G-200 device(60 seconds/subject,4 kHz sampling).After denoising via variational mode decomposition,78-dimensional features were extracted across four domains:Time-domain,frequency-domain,time-frequency domain,and nonlinear dynamics.Weighted feature importance was calculated using an integrated strategy and gradient-optimized feature subsets were used to train four classifiers:Support vector machine,random forest,logistic regression,and K-nearest neighbor.SHapley Additive exPlanations analysis was conducted on the features of the optimal model.Model performance was evaluated by fivefold cross-validation.Spearman’s correlation analysis was performed to assess key BS features against red blood cell count,hemoglobin,hematocrit,C-reactive protein(CRP),and high-sensitivity CRP.RESULTS The support vector machine classifier with 25-feature subsets achieved optimal performance(area under the curve>0.89),significantly outperforming other models.Acoustic feature importance analysis identified band_Energy and Mel-frequency cepstral coefficient variance as core biomarkers(cumulative contribution>60%).Key pathological correlations included:(1)Significant negative correlations between spectral centroid and red blood cell count/hemoglobin/hematocrit(P<0.01);(2)Positive correlation between wavelet entropy and these hematological parameters(P<0.05),suggesting multiscale microcirculatory flow fluctuations;and(3)Positive wavelet energy correlations with CRP/high-sensitivity-CRP(P<0.05).CONCLUSION Multidimensional BS features enable noninvasive UGIB screening.Their strong correlation with anemia/inflammation indicators reveals an acoustic-hemato-physiological coupling mechanism,providing a novel paradigm for early UGIB monitoring. 展开更多
关键词 Bowel sound analysis Upper gastrointestinal bleeding Support vector machine Machine learning Non-invasive diagnosis
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Support Vector-Guided Class-Incremental Learning:Discriminative Replay with Dual-Alignment Distillation 认领 引用
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作者 Moyi Zhang Yixin Wang Yu Cheng 《Computers, Materials & Continua》 SCIE EI 2026年第3期2040-2061,共22页
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo... Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics. 展开更多
关键词 Class-incremental learning catastrophic forgetting support vector machine knowledge distillation
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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh 认领 引用 被引量：2
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作者 Liyao Yang Hongyan Ma +1 位作者 Yingda Zhang Wei He 《Energy Engineering》 EI 2025年第1期243-264,共22页
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int... Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance. 展开更多
关键词 State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm
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Predicting Marine Fuels with Unusual Wax Appearance Temperatures Using One-Class Support Vector Machines 认领 引用
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作者 Njideka Chima-Amaeshi Chris O’Malley Mark Willis 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第6期1208-1217,共10页
Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the l... Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the lowest temperature at which the wax begins to form.When crude oil cools to its WAT,wax crystals precipitate,forming deposits on pipelines as the solubility limit is reached.Therefore,WAT is a crucial quality assurance parameter,especially when dealing with modern fuel oil blends.In this study,we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values.The dataset provided by Intertek PLC-a total quality assurance provider of inspection,testing,and certification services-includes industrial data that is imbalanced,with a higher proportion of high-WAT samples compared to low-WAT samples.The objective is to predict marine fuel oil blends with unusually high WAT values(>35℃)without relying on time-consuming and irregular laboratory-based measurements.The results demonstrate that the developed model,based on the one-class support vector machine(OCSVM)algorithm,achieved a Recall of 96,accurately predicting 96%of fuel samples with WAT>35℃.For standard binary classification,the Recall was 85.7.The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils. 展开更多
关键词 Marine fuel One-class support vector machines Wax appearance temperature Wax Machine learning
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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles 认领 引用
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作者 Ali Alqarni 《Computers, Materials & Continua》 SCIE EI 2025年第1期389-406,共18页
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie... Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance. 展开更多
关键词 Support vector machine big data IoV privacy-preserving
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Study on Flow Stress Constitutive Relationship of GH738 Superalloy Based on Machine Learning 认领 引用
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作者 Wang Shuangjian Wang Kelu +4 位作者 Lu Cuiyuan Luo Baile Xu Bing ZhouShixin Wang Panzhi 《稀有金属材料与工程》 SCIE EI CAS CSCD 北大核心 2026年第6期1437-1450,共14页
Hot compression experiments were conducted on GH738 superalloy using Gleeble 3500 thermal simulation machine at deformation temperature of 9801100°C and strain rate of 0.0010.1 s-1 to study the flow stress beh... Hot compression experiments were conducted on GH738 superalloy using Gleeble 3500 thermal simulation machine at deformation temperature of 9801100°C and strain rate of 0.0010.1 s-1 to study the flow stress behavior of the alloy.Three machine learning algorithms,namely random forest(RF),support vector machine(SVM),and genetic algorithm-back propagation(GA-BP)neural networks,were employed to establish constitutive relationship models for the flow stress behavior of GH738 superalloy.Subsequently,these models were compared and analyzed in terms of their predictive accuracy.The results indicate that the flow stress of GH738 superalloy decreases with the increase in deformation temperature and the decrease in strain rate.The correlation coefficients for the RF,SVM,and GA-BP constitutive relationship models are determined as 0.921,0.998,and 0.999,while the average absolute relative errors as 14.587%,2.112%,and 0.901%,respectively.The results demonstrate that SVM and GA-BP constitutive relationship models have better prediction accuracy than RF model in predicting the flow stress behavior of GH738 superalloy.It can provide a theoretical basis for the calculation of deformation resistance and forging tonnage under different deformation conditions,and it can also provide reliable flow stress data for numerical simulation of forging process. 展开更多
关键词 GH738 superalloy constitutive relationship model random forest support vector machine GA-BP network
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Predicting Marine Fuel with High Sulphur Content Using Machine Learning Algorithms 认领 引用
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作者 Njideka Chima-Amaeshi Chris O’Malley Mark Willis 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期617-629,共13页
Marine transportation is a significant source of air pollution especially around coastal areas with maritime vessels creating 12%of global sulphur oxides emission in 2014 alone.In compliance with International Maritim... Marine transportation is a significant source of air pollution especially around coastal areas with maritime vessels creating 12%of global sulphur oxides emission in 2014 alone.In compliance with International Maritime Organisation(IMO)regulations,the determination of sulphur content of marine fuels is typically carried out using lengthy laboratory-based analyses.The regulations prohibit the use of High-Sulphur Fuel Oil(HSFO)(>0.5%by weight of Sulphur)in Emission Control Areas(ECA).There is a need for a more efficient means of predicting Sulphur content and differentiating between HSFO and Very Low Sulphur Fuel Oil(VLSFO)samples.This study compares the application of a Support Vector Machine(SVM)and Agglomerative Hierarchical Clustering(AHC)algorithm enhanced with Principal Component Analysis for dimensionality reduction purposes to predict HSFO and VLSFO marine fuel samples based on near-infrared(NIR)industrial data from North Sea operations correlated with laboratory-measured sulphur values instead of relying on lengthy laboratory-based measurements.The study also compares the effect of normalising the data by setting the area under the curve to one and standardising it by subtracting the mean of predictor variables and scaling by standard deviation.The results show that although>70%of HSFO samples were accurately predicted with the SVM,a better result was achieved using the unsupervised learning approach of AHC/PCA with>80%of HSFO samples correctly predicted despite the imbalance in the industrial data providing an effective model for the rapid and well-informed decision-making tool for vessel operators.Normalising the area under the curve to one produced similar results to using standardised data. 展开更多
关键词 Machine learning Marine fuel Support vector machines Agglomerative hierarchical clustering High sulphur fuel oil Very low sulphur fuel oil
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Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere 认领 引用 被引量：2
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作者 Mao-xiang Chu Xiao-ping Liu +1 位作者 Rong-fen Gong Jie Zhao 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2018年第7期706-716,共11页
Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated f... Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency. 展开更多
关键词 Strip steel surface defect Multi-class classification Supporting vector machine Adjustable hyper-sphere
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Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction 认领 引用 被引量：39
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作者 史秀志 周健 +2 位作者 吴帮标 黄丹 魏威 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2012年第2期432-441,共10页
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50... Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable. 展开更多
关键词 rock fragmentation blasting mean panicle size （X50） support vector machines （SVMs） prediction
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Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation 认领 引用 被引量：13
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作者 Senlin CHEN Zhenghong GAO +2 位作者 Xinqi ZHU Yiming DU Chao PANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第10期2499-2509,共11页
Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hy... Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hysteresis loop that exists in the actual maneuvering process of the aircraft.Here,an excitation input suitable for nonlinear system identification is introduced to model unsteady aerodynamic forces with any motion in the amplitude and frequency ranges based on the Least Squares Support Vector Machines(LS-SVMs).In the selection of the input form,avoiding the use of reduced frequency as a parameter makes the model more universal.After model training is completed,the method is applied to predict the lift coefficient,drag coefficient and pitching moment coefficient of the RAE2822 airfoil,in sine and sweep motions under the conditions of plunging and pitching at Mach number 0.8.The predicted results of the initial unstable process and the final stable process are in close agreement with the Computational Fluid Dynamics(CFD)data,demonstrating the feasibility of the model for nonlinear unsteady aerodynamics modeling and the effectiveness of the input design approach. 展开更多
关键词 Aerodynamics models Forced vibration Input design Least squares support vector machines Nonlinear system System identification Unsteady aerodynamics
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Lithofacies identi cation using support vector machine based on local deep multi-kernel learning 认领 引用 被引量：21
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作者 Xing-Ye Liu Lin Zhou +1 位作者 Xiao-Hong Chen Jing-Ye Li 《Petroleum Science》 SCIE CAS CSCD 2020年第4期954-966,共13页
Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacie... Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacies and seismic information that is affected by many factors is complicated.Machine learning has received extensive attention in recent years,among which support vector machine(SVM) is a potential method for lithofacies classification.Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem,which needs to be solved by means of the kernel function.Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification.However,it is very difficult to determine the kernel function and the parameters,which is restricted by human factors.Besides,its computational efficiency is low.A lithofacies classification method based on local deep multi-kernel learning support vector machine(LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed.The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information.The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification.Both the model data test results and the field data application results certify advantages of the method.This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM. 展开更多
关键词 Lithofacies discriminant Support vector machine Multi-kernel learning Reservoir prediction Machine learning
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Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection 认领 引用 被引量：14
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作者 毛勇 夏铮 +2 位作者 尹征 孙优贤 万征 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第2期233-239,共7页
This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an e... This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved. 展开更多
关键词 fuzzy support vector machine parameter tuning fault diagnosis key variable identification
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Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms 认领 引用 被引量：15
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作者 Chuanqi Li Jian Zhou +1 位作者 Kun Du Daniel Dias 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2023年第8期1019-1036,共18页
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet... Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability. 展开更多
关键词 Underground pillar stability Hard rock Support vector machine Metaheuristic algorithms
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