Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwe...Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwest Pacific. Yet, few studies have published to promote accurate habitat identification of stomatopods, obstructing scientific management and conservation of these valuable organisms. This study provides an ensemble modeling framework for habitat suitability modeling of stomatopods, utilizing the O. oratoria stock in the Bohai Sea as an example. Two modeling techniques(i.e., generalized additive model(GAM) and geographical weighted regression(GWR)) were applied to select environmental predictors(especially the selection between two types of sediment metrics) that better characterize O. oratoria distribution and build separate habitat suitability models(HSM). The performance of the individual HSMs were compared on interpolation accuracy and transferability.Then, they were integrated to check whether the ensemble model outperforms either individual model, according to fishers’ knowledge and scientific survey data. As a result, grain-size metrics of sediment outperformed sediment content metrics in modeling O. oratoria habitat, possibly because grain-size metrics not only reflect the effect of substrates on burrow development, but also link to sediment heat capacity which influences individual thermoregulation. Moreover, the GWR-based HSM outperformed the GAM-based HSM in interpolation accuracy,while the latter one displayed better transferability. On balance, the ensemble HSM appeared to improve the predictive performance overall, as it could avoid dependence on a single model type and successfully identified fisher-recognized and survey-indicated suitable habitats in either sparsely sampled or well investigated areas.展开更多
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir mode...Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.展开更多
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiv...Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.展开更多
Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manu...Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.展开更多
N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computati...N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.展开更多
Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable perform...Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.展开更多
Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based...Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.展开更多
针对目前采用经验模态分解(empirical model decomposition,EMD)得到的系列子信号构建的磨机负荷参数软测量模型泛化性能差、难以进行清晰物理解释,以及EMD算法存在的模态混叠等问题,本文提出了基于选择性融合多尺度筒体振动频谱的建模...针对目前采用经验模态分解(empirical model decomposition,EMD)得到的系列子信号构建的磨机负荷参数软测量模型泛化性能差、难以进行清晰物理解释,以及EMD算法存在的模态混叠等问题,本文提出了基于选择性融合多尺度筒体振动频谱的建模方法.首先采用EMD、集合EMD(ensemble EMD,EEMD)、希尔伯特振动分解(Hilbert vibration decomposition,HVD)共3种多组分信号自适应分解算法获得磨机筒体振动多尺度子信号的集合,接着通过相关性分析剔除虚假无关部分,然后再将与原始信号相关性强的那部分多尺度子信号变换至频域,进而更有利于构建这些多尺度频谱与磨机负荷参数间的映射模型,最后通过改进分支定界选择性集成(improved branch and bound based selective ensemble,IBBSEN)算法建立软测量模型,实现对多源多尺度筒体振动频谱的最优选择性信息融合.基于实验球磨机运行数据的仿真实验表明所提方法在模型可解释性和泛化性能上均优于之前研究所提出方法.展开更多
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
基金The National Natural Science Foundation of China under contract No.31902375the David and Lucile Packard Foundation+1 种基金the Innovation Team of Fishery Resources and Ecology in the Yellow Sea and Bohai Sea under contract No.2020TD01the Special Funds for Taishan Scholars Project of Shandong Province。
文摘Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwest Pacific. Yet, few studies have published to promote accurate habitat identification of stomatopods, obstructing scientific management and conservation of these valuable organisms. This study provides an ensemble modeling framework for habitat suitability modeling of stomatopods, utilizing the O. oratoria stock in the Bohai Sea as an example. Two modeling techniques(i.e., generalized additive model(GAM) and geographical weighted regression(GWR)) were applied to select environmental predictors(especially the selection between two types of sediment metrics) that better characterize O. oratoria distribution and build separate habitat suitability models(HSM). The performance of the individual HSMs were compared on interpolation accuracy and transferability.Then, they were integrated to check whether the ensemble model outperforms either individual model, according to fishers’ knowledge and scientific survey data. As a result, grain-size metrics of sediment outperformed sediment content metrics in modeling O. oratoria habitat, possibly because grain-size metrics not only reflect the effect of substrates on burrow development, but also link to sediment heat capacity which influences individual thermoregulation. Moreover, the GWR-based HSM outperformed the GAM-based HSM in interpolation accuracy,while the latter one displayed better transferability. On balance, the ensemble HSM appeared to improve the predictive performance overall, as it could avoid dependence on a single model type and successfully identified fisher-recognized and survey-indicated suitable habitats in either sparsely sampled or well investigated areas.
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
基金supported by The Ministry of Trade,Industry,and Energy(20172510102090,20142520100440,20162010201980)Global PhD Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2015H1A2A1030756)supported by the National Research Foundation of Korea(NRF)Grant(No.2018R1C1B5045260).
文摘Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.
文摘Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPIP:1614-611-1442)from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.
基金supported by the National Natural Science Foundation of China(Nos.62071079 and 61803065).
文摘N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.
基金supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057)Postdoctoral Science Foundation of China (No. 20100471464)
文摘Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.
基金supported by Ningxia Key R&D Program (Key)Project (2023BDE02001)Ningxia Key R&D Program (Talent Introduction Special)Project (2022YCZX0013)+2 种基金North Minzu University 2022 School-Level Research Platform“Digital Agriculture Empowering Ningxia Rural Revitalization Innovation Team”,Project Number:2022PT_S10Yinchuan City School-Enterprise Joint Innovation Project (2022XQZD009)“Innovation Team for Imaging and Intelligent Information Processing”of the National Ethnic Affairs Commission.
文摘Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.
文摘针对目前采用经验模态分解(empirical model decomposition,EMD)得到的系列子信号构建的磨机负荷参数软测量模型泛化性能差、难以进行清晰物理解释,以及EMD算法存在的模态混叠等问题,本文提出了基于选择性融合多尺度筒体振动频谱的建模方法.首先采用EMD、集合EMD(ensemble EMD,EEMD)、希尔伯特振动分解(Hilbert vibration decomposition,HVD)共3种多组分信号自适应分解算法获得磨机筒体振动多尺度子信号的集合,接着通过相关性分析剔除虚假无关部分,然后再将与原始信号相关性强的那部分多尺度子信号变换至频域,进而更有利于构建这些多尺度频谱与磨机负荷参数间的映射模型,最后通过改进分支定界选择性集成(improved branch and bound based selective ensemble,IBBSEN)算法建立软测量模型,实现对多源多尺度筒体振动频谱的最优选择性信息融合.基于实验球磨机运行数据的仿真实验表明所提方法在模型可解释性和泛化性能上均优于之前研究所提出方法.