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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.In this study, 3000 experimental data are used for the purpose of prediction.The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R2

Keywords： prediction     strain     ensemble unit     rank analysis     error matrix    

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Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station

Chenglong ZHANG,Mo LI,Ping GUO

Frontiers of Agricultural Science and Engineering 2017, Volume 4, Issue 1,   Pages 81-96 doi: 10.15302/J-FASE-2016112

Abstract: Investigating long-term variation and prediction of streamflow are critical to regional water resourcetechnique was used to simulate these stochastic components with normal distribution, and thus a new ensemble

Keywords： Monte Carlo     nonstationary     trend detection     streamflow prediction     decomposition and ensemble     Yingluoxia    

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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 MIMOIn 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     Instancetransfer     Parameter prediction    

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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) forBased on this framework, we used eight numerical weather prediction products from the International GrandGlobal Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets ofThe framework’s application to the Chitan Basin in China revealed that the numerical weather predictionThe accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows

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

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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 (An inverse proportional weighting method that considers the leave-one-out prediction error is presented

Keywords： laser beam welding     parameter optimization     metamodel     multi-objective    

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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 learning

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

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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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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 ensemblebased bi-directional gated recurrent unit (EEMDN-SABiGRU) model on Spark for accurate passenger hotspot predictionpassenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of predictionFinally, 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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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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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 (DCNN

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

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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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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Title Author Date Type Operation

Ensemble unit and AI techniques for prediction of rock strain

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

Journal Article

Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station

Chenglong ZHANG,Mo LI,Ping GUO

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

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

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

Journal Article

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

Xinbin WU; Junjie LI; Linlin WANG

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

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

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

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

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

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article