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Strip flatness prediction of cold rolling based on ensemble methods

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Journal of Iron and Steel Research International 2024年第1期31卷 237-251页

作者： Wu-quan Yang Zhi-ting Zhao Liang-yu Zhu Xun-yang Gao Li Wang School of Mechanical and Power Engineering Shenyang University of Chemical TechnologyShenyang110142LiaoningChina

Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling,the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was ***,based on linear regression(LR),K nearest neighbors(KNN),support vector regression,regression trees(RT),and backpropagation neural network(BPN),bagging,boosting,and stacking ensemble methods were used for ensemble ***,three existing ensemble models,i.e.,random forest,extreme random tree(ET)and extreme gradient boosting,were used to conduct experiments and compare the *** research shows that bagging,boosting,and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model,which is increased by 5.28%,6.51%,and 5.32%,*** the same time,the stacking ensemble method improves both the simple model and the complex model,and the improvement effect on the simple base model is the greatest,which is 4.69%higher than that of the base model *** all of the ensemble models,the stacking ensemble model of level-1(ET,AdaBoost-RT,LR,BPN)paired with level-2(LR)was discovered to be the best model(EALB-LR)and can be further studied for industrial applications.

关键词： Tandem cold rolling Flatness prediction Machine learning ensemble method

Time-sensitive prediction of NO_(2) concentration in China using an ensemble machine learning model from multi-source data

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Journal of Environmental Sciences 2024年第3期137卷 30-40页

作者： Chenliang Tao Man Jia Guoqiang Wang Yuqiang Zhang Qingzhu Zhang Xianfeng Wang Qiao Wang Wenxing Wang Big Data Research Center for Ecology and Environment Environment Research InstituteShandong UniversityQingdao 266237China Shandong Provincial Eco-environment Monitoring Center Jinan 250101China

Nitrogen dioxide(NO_(2))poses a critical potential risk to environmental quality and public health.A reliable machine learning(ML)forecasting framework will be useful to provide valuable information to support government *** on the data from1609 air quality monitors across China from 2014-2020,this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide *** ensemble ML model incorporates a residual connection to the gated recurrent unit(GRU)network and adopts the advantage of Transformer,extreme gradient boosting(XGBoost)and GRU with residual connection network,resulting in a 4.1%±1.0%lower root mean square error over XGBoost for the test *** ensemble model shows great prediction performance,with coefficient of determination of 0.91,0.86,and 0.77 for 1-hr,3-hr,and 24-hr averages for the test results,*** particular,this model has achieved excellent performance with low spatial uncertainty in Central,East,and North China,the major site-dense *** the interpretability analysis based on the Shapley value for different temporal resolutions,we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions,while the impact of meteorological conditions would be ever-prominent for the *** with existing models for different spatiotemporal scales,the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO_(2),which will help developing effective control policies.

关键词： Air quality prediction Deep learning ensemble method Nitrogen dioxide Spatiotemporal covariates

Performance-weighted-voting model:An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

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Quantitative Biology 2020年第4期8卷 347-358页

作者： Yawei Li Yuan Luo Department of Preventive Medicine Northwestern UniversityFeinberg School of MedicineChicagoIL 60611USA

Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic *** machine learning techniques can be used to help with cancer analysis and ***:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer *** ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five *** weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression *** final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted ***:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting *** addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall ***:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be *** addition,our model presents a good strategy for using ensemble systems for cancer type classification.

关键词： cancer type classification ensemble method performance-weighted-voting model linear regression single-nucleotide polymorphism

A systematic machine learning method for reservoir identification and production prediction

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Petroleum Science 2023年第1期20卷 295-308页

作者： Wei Liu Zhangxin Chen Yuan Hu Liuyang Xu Department of Chemical and Petroleum Engineering University of CalgaryABCanada Rockeast Energy Ltd. CalgaryABCanada Jilin Oilfield CNPCChangchunJilin130000China

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and *** learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir *** forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil *** this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production *** production models are based on the reservoir identification *** realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML *** methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir *** XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting *** thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir *** validate the superiority of the ML methods,reference models using overall reservoir thickness were built for *** models based on effective thickness outperformed the reference models in every evaluation *** prediction accuracy of the ML models using effective thickness were 10%higher than that of reference *** the personal error or data distortion existing in traditional methods,this novel system realizes r

关键词： Reservoir identification Production prediction Machine learning ensemble method

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security ensemble method Light gradient boosting machine Machine learning Intrusion detection

Constrained voting extreme learning machine and its application

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Journal of Systems Engineering and Electronics 2021年第1期32卷 209-219页

作者： MIN Mengcan CHEN Xiaofang XIE Yongfang School of Automation Central South UniversityChangsha 410083China

Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by ***,its good performance is based on a large number of hidden layer *** the increase of the nodes in the hidden layers,the computation cost is greatly *** this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class *** the same time,to improve the accuracy of the proposed method,the voting selection is *** proposed method is evaluated on public benchmark *** experimental results show that the proposed algorithm is superior to the original ELM ***,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

关键词： extreme learning machine(ELM) majority voting ensemble method sample based learning superheat degree(SD)

A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

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Computer Modeling in Engineering & Sciences 2019年第12期121卷 815-834页

作者： Sasan Saqaeeyan Hamid Haj Seyyed Javadi Hossein Amirkhani Department of Computer Engineering Borujerd BranchIslamic Azad UniversityBorujerdIran Department of Mathematics and Computer Science Shahed UniversityTehranIran Computer Engineering and Information Technology Department University of QomQomIran

Anomaly detection in smart homes provides support to enhance the health and safety of people who live *** to the previous studies done on this topic,less attention has been given to hybrid *** paper presents a two-steps hybrid probabilistic anomaly detection model in the smart ***,it employs various algorithms with different characteristics to detect anomalies from sensory ***,it aggregates their results using a Bayesian *** this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base *** evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.

关键词： Smart homes sensory data anomaly detection Bayesian networks ensemble method

Human-machine interactive streaming anomaly detection by online self-adaptive forest

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Frontiers of Computer Science 2023年第2期17卷 145-156页

作者： Qingyang LI Zhiwen YU Huang XU Bin GUO School of Computer Science Northwestern Polytechnical UniversityXi'an 710129China

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score *** real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly *** this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human *** particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the *** main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model *** approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark *** results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

关键词： anomaly detection human-machine interaction human feedback random space tree ensemble method

Random dynamic analysis of vertical train–bridge systems under small probability by surrogate model and subset simulation with splitting

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Railway Engineering Science 2020年第3期28卷 305-315页

作者： Huoyue Xiang Ping Tang Yuan Zhang Yongle Li School of Civil Engineering Southwest Jiaotong UniversityChengdu 610031China

The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the ***,the coupling model of train–bridge systems is ***,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic *** main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme ***,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is *** results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.

关键词： Train–bridge system ensemble method Surrogate model Nonlinear autoregressive with exogenous input Subset simulation with splitting Small probability