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A novel ensemble model for predicting the performance of a novel vertical slot fishway

Aydin SHISHEGARAN, Mohammad SHOKROLLAHI, Ali MIRNOROLLAHI, Arshia SHISHEGARAN, Mohammadreza MOHAMMAD KHANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1418-1444 doi: 10.1007/s11709-020-0664-x

Abstract: We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models. Multiple linear regression, multiple log equation regression, gene expression programming, and combinations of these models are employed to predict the maximum turbulence, maximum velocity, resting area, and water depth of the middle pool in the fishway. The statistical parameters and error terms, including the coefficient of determination, root mean square error, normalized square error, maximum positive and negative errors, and mean absolute percentage error were employed to evaluate and compare the accuracy of the models. We also conducted a parametric study. The independent variables include the opening between baffles ( ), the ratio of the length of the large and small baffles, the volume flow rate, and the angle of the large baffle. The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and .

Keywords： novel vertical slot fishway     parametric study     finite volume method     ensemble model     gene expression programming    

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Robust ensemble of metamodels based on the hybrid error measure

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 3,   Pages 623-634 doi: 10.1007/s11465-021-0641-7

Abstract: In this work, a robust ensemble of metamodels (EMs) is proposed by combining three regression stand-alone

Keywords： metamodel     ensemble of metamodels     hybrid error measure     stochastic problem    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNNLastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Basedon Multi-Factor Analysis and a Multi-Model Ensemble Article

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Engineering 2021, Volume 7, Issue 12,   Pages 1751-1765 doi: 10.1016/j.eng.2020.10.023

Abstract: analysis, adaptive decomposition, and an optimization-based ensemble.The consideration of multiple factors makes model fusion more effective.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimizationFinally, a multi-factor ensemble model for DO is obtained by weighted allocation.② the proposed model is superior to other comparison models; and ③ the forecasting model can be used

Keywords： Time-series multi-step forecasting     Multi-factor analysis     Empirical wavelet transform decomposition     Multi-modeloptimization ensemble    

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Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0703-5

Abstract: This study proposes a multi-objective optimization framework by combining an ensemble of metamodels (

Keywords： laser beam welding     parameter optimization     metamodel     multi-objective    

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Evaluation of the impact of multi-source uncertainties on meteorological and hydrological ensemble forecasting Article

Zhangkang Shu, Jianyun Zhang, Lin Wang, Junliang Jin, Ningbo Cui, Guoqing Wang, Zhouliang Sun, Yanli Liu, Zhenxin Bao, Cuishan Liu

Engineering 2023, Volume 24, Issue 5,   Pages 213-229 doi: 10.1016/j.eng.2022.06.007

Abstract: In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) forframework, we used eight numerical weather prediction products from the International Grand Global EnsembleThe hydrological model structure uncertainty was more prominent than the parameter uncertainty.In addition, the structures and parameters of the hydrological model and their interactions contributedBy accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting

Keywords： Meteorological and hydrological forecasting     Uncertainty estimation     Bayesian model averaging     Ensembleprediction     Multi-model    

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Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised Research Article

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1814-1827 doi: 10.1631/FITEE.2200053

Abstract: As an indispensable part of process monitoring, the performance of relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new strategy is performed in which enhanced is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords： Semi-supervised     Active learning     Ensemble learning     Mixture discriminant analysis     Fault classification    

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 564-575 doi: 10.1007/s11709-022-0829-x

Abstract: This paper introduces the idea of ensemble deep learning.At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learningFurthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited

Keywords： water conveyance tunnels     siltation images     remotely operated vehicles     deep learning     ensemble learning    

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Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems Research Article

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 275-288 doi: 10.1631/FITEE.2200169

Abstract: In this paper we propose an ensemble-transfer-learning-based channel method for asymmetric massive MIMOminimum description length (MDL) criterion to reduce the computation complexity and negative impact on modelIn addition, the method is introduced to support the prediction model in new propagation conditions,

Keywords： Asymmetric massive multiple-input multiple-output (MIMO) system     Channel model     Ensemble learning     Instance    

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Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 858-870 doi: 10.1007/s11709-022-0831-3

Abstract: Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain.The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R2model, based on the experimental data, has the potential to be a new option for engineers to

Keywords： prediction     strain     ensemble unit     rank analysis     error matrix    

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Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 11, doi: 10.1007/s11783-022-1574-z

Abstract:

● Diurnal patterns of CH4 and CO2 are clearly extracted using EEMD.

Keywords： Methane     Carbon dioxide     Diurnal pattern     Ensemble empirical mode decomposition     South China Sea     Sea    

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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1059-1068 doi: 10.1631/FITEE.1400398

Abstract: A novel ensemble classifier is proposed in this paper.method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble

Keywords： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Conceptual study on incorporating user information into forecasting systems

Jiarui HAN, Qian YE, Zhongwei YAN, Meiyan JIAO, Jiangjiang XIA

Frontiers of Environmental Science & Engineering 2011, Volume 5, Issue 4,   Pages 533-542 doi: 10.1007/s11783-010-0246-6

Abstract: This research took advantage of the recently implemented TIGGE (THORPEX interactive grand global ensemble

Keywords： user-end information     user-oriented     interactive forecasting system     TIGGE (THORPEX interactive grand global ensemble    

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Interactive image segmentation with a regression based ensemble learning paradigm Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 1002-1020 doi: 10.1631/FITEE.1601401

Abstract: This paper presents a novel interactive image segmentation method via a regression-based ensemble modelNext, a support vector regression (SVR) based decision fusion model is adopted to integrate the resultsFinally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation

Keywords： Interactive image segmentation     Multivariate adaptive regression splines (MARS)     Ensemble learning     Thin-plate    

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A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1316-1331 doi: 10.1631/FITEE.2200621

Abstract: imbalance problem between supply and demand for taxis and passengers, this paper proposes a distributed ensemblenormalization of spatial attention mechanism based bi-directional gated recurrent unit (EEMDN-SABiGRU) modelFinally, the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using

Keywords： Passenger hotspot prediction     Ensemble empirical mode decomposition (EEMD)     Spatial attention mechanism    

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Title Author Date Type Operation

A novel ensemble model for predicting the performance of a novel vertical slot fishway

Aydin SHISHEGARAN, Mohammad SHOKROLLAHI, Ali MIRNOROLLAHI, Arshia SHISHEGARAN, Mohammadreza MOHAMMAD KHANI

Journal Article

Robust ensemble of metamodels based on the hybrid error measure

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Basedon Multi-Factor Analysis and a Multi-Model Ensemble

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Journal Article

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Journal Article

Evaluation of the impact of multi-source uncertainties on meteorological and hydrological ensemble forecasting

Zhangkang Shu, Jianyun Zhang, Lin Wang, Junliang Jin, Ningbo Cui, Guoqing Wang, Zhouliang Sun, Yanli Liu, Zhenxin Bao, Cuishan Liu

Journal Article

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Journal Article

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

Journal Article

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Journal Article

Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

Journal Article

Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea

Journal Article

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

Journal Article

Conceptual study on incorporating user information into forecasting systems

Jiarui HAN, Qian YE, Zhongwei YAN, Meiyan JIAO, Jiangjiang XIA

Journal Article

Interactive image segmentation with a regression based ensemble learning paradigm

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Journal Article

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

Journal Article