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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
1
作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Development and validation of machine learningbased in-hospital mortality predictive models for acute aortic syndrome in emergency departments 认领 引用
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作者 Yuanwei Fu Yilan Yang +6 位作者 Hua Zhang Daidai Wang Qiangrong Zhai Lanfang Du Nijiati Muyesai YanxiaGao Qingbian Ma 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第1期43-49,共7页
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita... BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation. 展开更多
关键词 Emergency department Acute aortic syndrome Mortality Predictive model Machine learning Algorithms
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Improved Whale Optimization-Based Neural Network Predictive Control for Industrial Refrigeration Systems 认领 引用
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作者 Qi Li Menghan Yang +1 位作者 Shifa Cui Kun Han 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第3期1-14,共14页
The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such ... The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such systems a significant and challenging research problem.To address this,a novel Neural Network Predictive Control(NNPC)algorithm is proposed,which integrates an Improved Deep Belief Network(IDBN)with an Improved Whale Optimization Algorithm(IWOA).First,the IDBN acts as a high-precision nonlinear prediction model,significantly improving multi-step prediction accuracy.Second,the IWOA is employed to optimize the predictive controller,featuring three major improvements:an improved population initialization,a modified convergence factor update mechanism,and an added disturbance strategy,which collectively accelerate convergence and enhance global search capability.Finally,simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. 展开更多
关键词 industrial refrigeration systems model predictive control time⁃delay whale optimization algorithm deep belief network
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Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用 被引量：1
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作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
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Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine 认领 引用
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作者 Zixuan Zong Tingting Long +3 位作者 Huaqing Dong Guoqiang Huang Xiao Meng Mohammadamin Azimi 《Structural Durability & Health Monitoring》 EI 2026年第3期480-495,共16页
This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KEL... This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KELM model addresses the limitations of traditional evaluation methods and single machine learning models in residual strength prediction.A dataset comprising 80 samples from burst tests and finite element simulations was used to validate the model.Results demonstrate that the SSA-KELM model achieves superior prediction accuracy with a maximum relative error of 13.54%and minimum relative error of 0.20%.The model’s mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are 0.658%,0.780%,and 4.38%,respectively,significantly outperforming conventional machine learning models and traditional assessment methods.This research provides a reliable tool for evaluating pipeline integrity and maintenance planning. 展开更多
关键词 Pipeline corrosion residual strength prediction kernel extreme learning machine sparrow search algorithm machine learning
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Trajectory tracking near asteroids using relaxed Lyapunov-based model predictive control 认领 引用
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作者 YU Zhitong SHANG Haibin +1 位作者 ZHAO Zichen ZHANG Xuefen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期670-686,共17页
Lyapunov-based model predictive control(LMPC)is an effective approach for trajectory tracking because of its wellguaranteed and easy-to-implement stability.However,traditional LMPC utilizes pre-designed auxiliary cont... Lyapunov-based model predictive control(LMPC)is an effective approach for trajectory tracking because of its wellguaranteed and easy-to-implement stability.However,traditional LMPC utilizes pre-designed auxiliary controllers to estimate the domain of attraction(DOA)and construct stability constraints,which inevitably reduces its stable domain and degrades tracking performance.For this problem,this paper proposes a relaxed LMPC(RLMPC)which is designed independently of auxiliary controllers.The control Lyapunov function(CLF)is firstly introduced to decouple the DOA and auxiliary control,alleviating the conservatism in traditional LMPC.Subsequently,a multi-resolution sampling-based search algorithm is developed to estimate the DOA,where the state space is partitioned into hyper-rectangles.A verification condition is derived to extend the verification validity of sampling points to all states within hyper-rectangles,thereby reducing DOA estimation error.Based on the auxiliary-controller-independent DOA(ACI-DOA)and CLF,stability constraints are formulated to ensure stability for RLMPC,while relaxing the stable domain of RLMPC to the entire ACI-DOA.Furthermore,a convergence rate adaptive adjustment technology is developed to enhance the convergence rate while balancing it with control effort.Through numerical simulations involving asteroid orbiting missions,the proposed method is found to significantly expand the stable domain and improve tracking performance. 展开更多
关键词 Lyapunov-based model predictive control(LMPC) trajectory tracking auxiliary-controller-independent domain of attraction(DOA) sampling-based search algorithm control Lyapunov function(CLF) asteroid orbiting mission
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Construction and validation of a machine learning algorithm-based predictive model for difficult colonoscopy insertion 认领 引用
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作者 Ren-Xuan Gao Xin-Lei Wang +6 位作者 Ming-Jie Tian Xiao-Ming Li Jia-Jia Zhang Jun-Jing Wang Jing Gao Chao Zhang Zhi-Ting Li 《World Journal of Gastrointestinal Endoscopy》 2025年第7期149-161,共13页
BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intr... BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification. 展开更多
关键词 Colonoscopy Difficulty of colonoscopy insertion Machine learning algorithms Predictive model Logistic regression Least absolute shrinkage and selection operator regression Random forest
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A Feature Selection Method for Software Defect Prediction Based on Improved Beluga Whale Optimization Algorithm 认领 引用 被引量：1
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作者 Shaoming Qiu Jingjie He +1 位作者 Yan Wang Bicong E 《Computers, Materials & Continua》 SCIE EI 2025年第6期4879-4898,共20页
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ... Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models. 展开更多
关键词 Software defect prediction feature selection beluga optimization algorithm triangular wandering strategy cauchy mutation reverse learning
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Predictive Ecological Cooperative Control of Electric Vehicles Platoon on Hilly Roads 认领 引用
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作者 Bingbing Li Weichao Zhuang +4 位作者 Boli Chen Hao Zhang Sheng Yu Jianrun Zhang Guodong Yin 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期360-373,共14页
The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailin... The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailing eco-CACC implementations still exhibit limitations in fully harnessing the potential energy savings.This can be attributed to the intricate nature of the problem,characterized by its high nonlinearity and non-convexity,making it challenging for conventional solving methods to find solutions.In this paper,a novel strategy based on a decentralized model predictive control(MPC)framework,called predictive ecological cooperative control(PECC),is proposed for vehicle platoon control on hilly roads,aiming to maximize the overall energy efficiency of the platoon.Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories,this strategy employs an approach based on the combination of a long short-term memory network(LSTM)and genetic algorithm(GA)optimization(GA-LSTM)to predict the future speed of the leading vehicle.Notably,a function named the NotchFilter function(NF(?))is introduced to transform the hard state constraints in the eco-CACC problem,thereby alleviating the burden of problem-solving.Finally,through simulation comparisons between PECC and a strategy based on the common eco-CACC modifications,the effectiveness of PECC in improving platoon energy efficiency is demonstrated. 展开更多
关键词 Electric vehicles platoon Model predictive control Energy efficiency Cooperative adaptive cruise control Genetic algorithm
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APPLICATION OF PREDICTIVE CONTROL ALGORITHM IN SPEED CONTROL SYSTEM 认领 引用 被引量：4
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作者 Chen Jida Zhang Qian Liao Liqing 《Journal of Central South University》 SCIE EI CAS 1995年第2期48-53,共6页
In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it a... In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system 展开更多
关键词 predictive control algorithm digital trigger PID comtrol
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Nonlinear model predictive control based on support vector machine and genetic algorithm 认领 引用 被引量：16
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作者 冯凯 卢建刚 陈金水 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2048-2052,共5页
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ... This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection. 展开更多
关键词 Support vector machine Genetic algorithm Nonlinear model predictive control Neural network Modeling
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Rockburst prediction based on data preprocessing and hyperband-RNN-DNN 认领 引用
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作者 Yong Fan Chenxi Yin +2 位作者 Guangdong Yang Shengyong Ding Bin Tian 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期446-464,共19页
A data preprocessing workflow is proposed to address key challenges in rockburst data analysis,including dimensionality differences among various sample features,variations in data values within the same feature,missi... A data preprocessing workflow is proposed to address key challenges in rockburst data analysis,including dimensionality differences among various sample features,variations in data values within the same feature,missing data,poor data consistency,and sample class imbalance.The workflow is divided into four steps.Each step introduces multiple algorithms,which are sequentially combined according to the order of the four steps.Then,these coupled algorithms are utilized to preprocess the rockburst data set.The rockburst data set contains 459 samples,and the maximum tangential stress(MTS),the uniaxial compressive strength(UCS),the uniaxial tensile strength(UTS),the elastic strain energy index(WET),the rock stress factor(SCF),and the rock brittleness coefficient(B)are selected as the feature parameters.Subsequently,three architectures,Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Recurrent Neural Network(RNN),are used to evaluate the data sets processed by different coupled algorithms.The hyperband algorithm is introduced to optimize the hyperparameters of the RNN model,and the prediction accuracy of different architectures is compared between the RNN model with dense layers and without dense layers.Finally,a rockburst prediction model based on data preprocessing and the Hyperband-DNN model is developed.The prediction results show that data preprocessing can significantly improve the model prediction accuracy;the model architecture with the highest prediction accuracy can be found quickly using the hyperband algorithm;and adding the dense layer can improve the stability and prediction accuracy of the model. 展开更多
关键词 data preprocessing hyperband algorithm hyperparameter optimization RNN-DNN rockburst prediction
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NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM 认领 引用 被引量：1
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作者 刘宝坤 王慧 李光泉 《Transactions of Tianjin University》 CAS 1998年第2期48-50,共3页
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data... A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness. 展开更多
关键词 neural networks(NN) predictive control hierarchical genetic algorithms nonlinear system
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Physics-informed neural network optimized by particle swarm algorithm for accurate prediction of blast-induced peak particle velocity 认领 引用
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作者 Lang Qiu Yujie Zhu +3 位作者 Chen Xu Gaofeng Ren Yingguo Hu Xiaoli Liu 《Intelligent Geoengineering》 2025年第3期126-140,共15页
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred... Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R2enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications. 展开更多
关键词 Peak particle velocity Blast-induced vibration Particle Swarm Optimization algorithm Physics-informed neural network Prediction system
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Viscosity prediction and optimization of CaO-SiO2-MgO-Al2O3slag system based on integrated calibration of multi-source data 认领 引用
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作者 En-Ze Shi Jue Tang +1 位作者 Man-Sheng Chu Hong-Yu Tian 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第6期131-145,共15页
The viscosity of metallurgical slag is a critical parameter influencing process efficiency and product quality.Traditional experimental methods are time-consuming and complex,while existing predictive models often suf... The viscosity of metallurgical slag is a critical parameter influencing process efficiency and product quality.Traditional experimental methods are time-consuming and complex,while existing predictive models often suffer from limited accuracy and generalizability.A highly accurate and robust viscosity prediction and optimization method for the CaO-SiO2-MgO-Al2O3slag system was developed by integrating thermodynamic simulation,experimental measurements,and machine learning.A hybrid dataset was constructed by generating 3000 theoretical viscosity data points using FactSage software and incorporating 255 representative experimental values.To reconcile the discrepancy between simulated and measured data,a nearest-neighbor error compensation strategy was employed,yielding a corrected dataset with improved agreement to experimental observations.Based on this dataset,28 regression algorithms were evaluated.Among them,exponential Gaussian process regression achieves the best performance,with a root mean square error of 0.0195 Pa s and a coefficient of determination(R2)of 0.9681 on the test set—representing improvements of 89.5%and 61.8%compared to uncorrected and purely experimental models,respectively.The model demonstrates strong generalization ability and resistance to overfitting.Furthermore,a genetic algorithm was applied to optimize slag composition and temperature,achieving a minimum predicted viscosity of 0.3298 Pa s under specified constraints.By combining the generalizability of thermodynamic simulations with the precision of experimental data,this method provides a reliable strategy for viscosity prediction and process optimization in the complex slag system and offers potential for broader applications in predicting high-temperature molten material properties. 展开更多
关键词 Viscosity prediction Machine learning Nearest neighbor error correction Genetic algorithm
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Prediction model of rolling force in cold tandem rolling based on dynamic deformation zone coupling and DE-PSO hybrid optimization 认领 引用
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作者 Ji Zhang Zhi-Xuan Wang +1 位作者 Qi Lu Zhen-Hua Bai 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第7期33-48,共16页
To enhance the prediction accuracy of rolling force in cold tandem rolling processes,a deformation resistance-rolling force(DR-RF)coupled model is proposed based on dynamic deformation zone length iteration.This DR-RF... To enhance the prediction accuracy of rolling force in cold tandem rolling processes,a deformation resistance-rolling force(DR-RF)coupled model is proposed based on dynamic deformation zone length iteration.This DR-RF model comprehensively accounts for the influence of material parameters,hot rolling,and cold rolling processes on deformation resistance,establishing a robust framework for cold rolling.To ensure the model's generalization capability across diverse stands and steel grades,a hierarchical progressive optimization strategy is introduced,leveraging a differential evolution-particle swarm optimization(DE-PSO)hybrid algorithm to effectively mitigate local optima.Experimental validation using industrial data demonstrates significant performance improvements.The unoptimized DR-RF model already exhibits superior accuracy compared to the conventional Hill model.Furthermore,DE-PSO model optimized DR-RF model achieves an overall rolling force prediction accuracy of 94.71%,representing a 7.11%improvement over Hill model.Notably,the first stand shows a 13.4%improvement in accuracy,and the third stand achieves the highest accuracy of 96.68%.The average prediction accuracy for 15 steel grades consistently remains within the range of 90.5%-97.2%.DR-RF model coupled with DE-PSO framework provides a robust theoretical foundation and practical solution for steel enterprises to achieve efficient and intelligent rolling across multiple stands and steel grades. 展开更多
关键词 Cold tandem rolling Rolling force prediction Model coupling DE-PSO algorithm Dynamic deformation zone length
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Predicting BDS-3's short-term clock bias using the RIME-WNN model 认领 引用
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作者 Xu Wang Chang Wang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期238-248,共11页
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr... Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites. 展开更多
关键词 Satellite clock bias Wavelet neural network Rime optimization algorithm Prediction
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Optimized Cardiovascular Disease Prediction Using Clustered Butterfly Algorithm 认领 引用 被引量：1
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作者 Kamepalli S.L.Prasanna Vijaya J +2 位作者 Parvathaneni Naga Srinivasu Babar Shah Farman Ali 《Computers, Materials & Continua》 SCIE EI 2025年第10期1603-1630,共28页
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h... Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance. 展开更多
关键词 Cardiovascular disease prediction healthcare management system clustering RoughK-means classification butterfly optimization algorithm
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