Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth st...Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness,burial depth,working face length,and roof category on the height of a WFFZ,we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height.Based on data of WFFZ height and its influence index obtained from field observations,a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine.The reliability and superiority of the prediction model were verified by a comparative study and an engineering application.The results show that the main factors affecting WFFZ height in the study area are coal seam thickness,burial depth,working face length,and roof category.Compared with multiple-linear-regression and back-propagation neural-network approaches,the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.展开更多
To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the ...To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.展开更多
The secondary Bjerknes force plays a significant role in the evolution of bubble clusters.However,due to the complex dependence of the force on multiple parameters,it is highly non-trivial to include its effects in th...The secondary Bjerknes force plays a significant role in the evolution of bubble clusters.However,due to the complex dependence of the force on multiple parameters,it is highly non-trivial to include its effects in the simulations of bubble clusters.In this paper,machine learning is used to develop a data-driven model for the secondary Bjerknes force between two insonated bubbles as a function of the equilibrium radii of the bubbles,the distance between the bubbles,the amplitude and the center frequency of the ultrasound wave.The sign of the force may change with the phase difference between the oscillating bubbles.Meanwhile,the magnitude of the force varies over several orders of magnitude,which poses a serious challenge for the usual machine learning models.To overcome this difficulty,the magnitudes and the signs of the force are separated and modelled separately.A nonlinear regression is obtained with a feed-forward network model for the logarithm of the magnitude,whereas the sign is modelled by a support-vector machine model.The principle,the practical aspects related to the training and validation of the machine models are introduced.The predictions from the models are checked against the values computed from the Keller-Miksis equations.The results show that the models are extremely efficient while providing accurate estimate of the force.The models make it computationally feasible for the future simulations of the bubble clusters to include the effects of the secondary Bjerknes force.展开更多
Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).T...Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).This method considers the directional overcurrent relays(DOCRs)for the transmission system,whereas in previous studies,the effect of protective mechanisms on the transient stability was largely ignored.When protective relays are activated in power system,the configuration of the power system is altered to mitigate the risk of the power system becoming unstable.The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system.In addition,WLS-SVM is employed for an online assessment of the transient stability.WLS-SVM not only is effective in response due to its faster speed,but also is resistant to noise and has excellent performance against the measurement errors of PMUs.To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm,principal component analysis is used.The findings of the suggested technique reveal that it has higher accuracy and optimum performance,as compared to the extreme learning machine method,the adaptive neuro-fuzzy inference system method,and the back-propagation neural network method.The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.展开更多
This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used t...This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model.In this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed model.Experimental results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating datasets.The model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision Trees.The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.展开更多
An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental...An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.展开更多
Defect detection assurance on production lines machine-vision-based surface is important in quality This paper presents a fast defect detection method using the weighted least-squares model. We assume that an inspecti...Defect detection assurance on production lines machine-vision-based surface is important in quality This paper presents a fast defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.展开更多
In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process tak...In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.展开更多
In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithm...In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.展开更多
Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump rubber.The DRC analysis requires prolonged laboratory validation.To develop fast and effective DRC determination met...Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump rubber.The DRC analysis requires prolonged laboratory validation.To develop fast and effective DRC determination methods,this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches.This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging.A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples.To obtain an average spectrum to represent a cup lump rubber sample,the spectral data were collected by locating and scanning for point-based and area-based measurement,respectively.The spectral data were calibrated using partial least squares regression(PLSR)and the least-squares support vector machine(LS-SVM)methods against the reference values.The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach.The best predictions of PLSR represented by the coefficient of determination(R2),the root mean square error of prediction(RMSEP)and the residual predictive deviation(RPD)were 0.99,0.72%and 15.17,while the best prediction of LS-SVM were 0.99,0.64%and 16.83,respectively.In summary,the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.展开更多
文摘Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness,burial depth,working face length,and roof category on the height of a WFFZ,we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height.Based on data of WFFZ height and its influence index obtained from field observations,a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine.The reliability and superiority of the prediction model were verified by a comparative study and an engineering application.The results show that the main factors affecting WFFZ height in the study area are coal seam thickness,burial depth,working face length,and roof category.Compared with multiple-linear-regression and back-propagation neural-network approaches,the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.
文摘To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.
基金support provided by the Guangzhou Science(Technology)Research Project(Grant 201704030010)the special fund project of Science and Technology Innovation Strategy of Guangdong Province(Grant PDJH2020B0185).
文摘The secondary Bjerknes force plays a significant role in the evolution of bubble clusters.However,due to the complex dependence of the force on multiple parameters,it is highly non-trivial to include its effects in the simulations of bubble clusters.In this paper,machine learning is used to develop a data-driven model for the secondary Bjerknes force between two insonated bubbles as a function of the equilibrium radii of the bubbles,the distance between the bubbles,the amplitude and the center frequency of the ultrasound wave.The sign of the force may change with the phase difference between the oscillating bubbles.Meanwhile,the magnitude of the force varies over several orders of magnitude,which poses a serious challenge for the usual machine learning models.To overcome this difficulty,the magnitudes and the signs of the force are separated and modelled separately.A nonlinear regression is obtained with a feed-forward network model for the logarithm of the magnitude,whereas the sign is modelled by a support-vector machine model.The principle,the practical aspects related to the training and validation of the machine models are introduced.The predictions from the models are checked against the values computed from the Keller-Miksis equations.The results show that the models are extremely efficient while providing accurate estimate of the force.The models make it computationally feasible for the future simulations of the bubble clusters to include the effects of the secondary Bjerknes force.
文摘Weighted least-square support vector machine(WLS-SVM)is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units(PMUs).This method considers the directional overcurrent relays(DOCRs)for the transmission system,whereas in previous studies,the effect of protective mechanisms on the transient stability was largely ignored.When protective relays are activated in power system,the configuration of the power system is altered to mitigate the risk of the power system becoming unstable.The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system.In addition,WLS-SVM is employed for an online assessment of the transient stability.WLS-SVM not only is effective in response due to its faster speed,but also is resistant to noise and has excellent performance against the measurement errors of PMUs.To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm,principal component analysis is used.The findings of the suggested technique reveal that it has higher accuracy and optimum performance,as compared to the extreme learning machine method,the adaptive neuro-fuzzy inference system method,and the back-propagation neural network method.The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.
基金the Project No.B2014-02-21(Hanoi University of Mining and Geology,Vietnam)supported by the Geographic Information System group,University College of Southeast Norway.
文摘This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model.In this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed model.Experimental results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating datasets.The model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision Trees.The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.
基金This project was financially supported by the National Natural Science Foundation of China (No. 69889050)
文摘An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.
文摘Defect detection assurance on production lines machine-vision-based surface is important in quality This paper presents a fast defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.
基金financial support from Natural Sciences and Engineering Research Council of Canada (NSERC), Innovate NL, and Statoil Canada
文摘In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.
基金This research is sponsored by the Project:“Support of research and development activities of the J.Selye University in the field of Digital Slovakia and creative industry”of the Research&Innovation Operational Programme(ITMS code:NFP313010T504)co-funded by the European Regional Development Fund.
文摘In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.
基金The authors acknowledge the financial support and a research grant provided by the Thailand Research Fund (TRF) and the Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Thailand.
文摘Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump rubber.The DRC analysis requires prolonged laboratory validation.To develop fast and effective DRC determination methods,this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches.This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging.A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples.To obtain an average spectrum to represent a cup lump rubber sample,the spectral data were collected by locating and scanning for point-based and area-based measurement,respectively.The spectral data were calibrated using partial least squares regression(PLSR)and the least-squares support vector machine(LS-SVM)methods against the reference values.The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach.The best predictions of PLSR represented by the coefficient of determination(R2),the root mean square error of prediction(RMSEP)and the residual predictive deviation(RPD)were 0.99,0.72%and 15.17,while the best prediction of LS-SVM were 0.99,0.64%and 16.83,respectively.In summary,the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.
