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

Frontiers of Structural and Civil Engineering   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 learning

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

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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: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years.However, many deep learning methods cannot fully extract fault information to recognize mechanical healthTherefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNNseveral 1D DCNN models with different activation functions are trained through dimension reduction learningCompared with other classical deep learning methods, the proposed fault diagnosis method has considerable

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

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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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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 modelwith semi-supervised learning.complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learningThen, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposedFinally, 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-platespline regression (TPSR)     Semi-supervised learning     Support vector regression (SVR)    

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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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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 MIMOAdditionally, the proposed instance-transfer-based method outperforms the method without transfer learning

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

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

Frontiers of Mechanical Engineering 2021, 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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Ensemble unit and AI techniques for prediction of rock strain

Frontiers of Structural and Civil Engineering   Pages 858-870 doi: 10.1007/s11709-022-0831-3

Abstract: Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain.

Keywords： prediction     strain     ensemble unit     rank analysis     error matrix    

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Vibration-based hypervelocity impact identification and localization Research Article

Jiao BAO, Lifu LIU, Jiuwen CAO,jwcao@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 515-529 doi: 10.1631/FITEE.2000483

Abstract: Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

Keywords： Ensemble learning     Synchrosqueezied transform     Gray-level co-occurrence matrix     Image entropy     Distance    

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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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Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial Special Feature on Intelligent Design

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1632-1643 doi: 10.1631/FITEE.1900399

Abstract: Accurate recognition of modern and traditional porcelain styles is a challenging issue in Cantonese porcelain management due to the large variety and complex elements and patterns. We propose a hybrid system with porcelain style identification and image recreation modules. In the identification module, prediction of an unknown porcelain sample is obtained by logistic regression of ensembled neural networks of top-ranked design signatures, which are obtained by discriminative analysis and transformed features in principal components. The synthesis module is developed based on a conditional generative adversarial network, which enables users to provide a designed mask with porcelain elements to generate synthesized images of Cantonese porcelain. Experimental results of 603 Cantonese porcelain images demonstrate that the proposed model outperforms other methods relative to precision, recall, area under curve of receiver operating characteristic, and confusion matrix. Case studies on image creation indicate that the proposed system has the potential to engage the community in understanding Cantonese porcelain and promote this intangible cultural heritage.

Keywords： Cantonese porcelain     Classification     Generative adversarial network     Creative arts    

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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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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 EnsembleBy accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting

Keywords： Meteorological and hydrological forecasting     Uncertainty estimation     Bayesian model averaging     Ensemble    

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Dissolved Oxygen Concentrations Time-Series Forecasting Based on 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: that includes three stages: multi-factor analysis, adaptive decomposition, and an optimization-based ensembleThe 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.

Keywords： forecasting     Multi-factor analysis     Empirical wavelet transform decomposition     Multi-model optimization ensemble    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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Title Author Date Type Operation

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

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

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

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

Robust ensemble of metamodels based on the hybrid error measure

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

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

Journal Article

Ensemble unit and AI techniques for prediction of rock strain

Journal Article

Vibration-based hypervelocity impact identification and localization

Jiao BAO, Lifu LIU, Jiuwen CAO,jwcao@hdu.edu.cn

Journal Article

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

Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Journal Article

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

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

Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article