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
Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep...Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.展开更多
Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services...Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.展开更多
Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power ...Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.展开更多
To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized r...To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.展开更多
Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
Objective To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography(CTA)using subjective and objective metrics,with a focus on vessel edge sharpness.Metho...Objective To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography(CTA)using subjective and objective metrics,with a focus on vessel edge sharpness.Methods This prospective study enrolled 45 patients who underwent ultra-low-voltage(60 kVp)head and neck CTA.Image datasets were reconstructed with filtered back-projection(FBP),ClearView(CV)and ClearInfinity(CI)algorithms at low(30%),medium(50%),and high(70%)strengths.Image quality was assessed subjectively and objectively via the Kruskal‒Wallis test for multiple comparisons.Objective parameters,including edge rise slope(ERS)and edge rise distance(ERD),were analyzed via the Friedman test of multiple comparisons statistics.Results Subjective assessments favored the CI50 reconstruction algorithm,demonstrating superior or satisfactory results compared to the other algorithms,with significantly better vessel delineation,edge definition and diagnostic confidence(all P<0.05).Objective analysis revealed that the CV50 and CV70 algorithms significantly reduced ERS and/or elevated ERD(both P<0.05).However,the CI50 algorithm maintained comparable vessel edge sharpness(P>0.05)across all evaluated head and neck vascular segments when compared with the FBP algorithm.Conclusions The CI50 reconstruction algorithm optimizes image quality in 60 kVp head and neck CTA.It provides vessel edge sharpness comparable to FBP while offering superior vessel delineation,edge definition,and diagnostic confidence compared to FBP and CV algorithm.These findings suggest that CI50 has the potential to improve diagnostic accuracy in low-dose vascular imaging.展开更多
Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack ro...Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack robustness.This study proposes a subgrade compaction strategy that utilizes a heterogeneous dataset to estimate compaction quality across diverse scenarios while maintaining model accuracy.Field compaction tests are conducted in four distinct scenarios,considering various construction parameters.Compaction models are developed using several machine learning algorithms.The datasets are thoroughly assessed in terms of quality,diversity and similarity.The proposed model exhibits good performance in new scenarios by incorporating an additional 5%e8%of new data for retraining.The model's generalization capability is enhanced by conducting a limited number of field tests,which are labor-saving and time-efficient.The model's accuracy consistently improves across diverse scenarios and optimal algorithms.The proposed compaction strategy adopts a physics-and-data dual-driven approach,aimed at practical engineering applications and guiding the compaction procedure.展开更多
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 the National Natural Science Foundation of China(Grant Nos.51979253,51879245)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.CUGCJ1821).
文摘Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.
基金The authors extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group no KSRG-2022-030.
文摘Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.
文摘Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.
基金The National Key Research and Development Plan(No.2019YFB2006402)Talent Introduction Fund Project of Hubei Polytechnic University(No.17xjz01R)Key Scientific Research Project of Hubei Polytechnic University(No.22xjz02A)。
文摘To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
基金the Grant from the National Key Research and Development Program of China(No.2024YFC2419300)the National Natural Science Foundation of China(No.82471967)+1 种基金the Hubei Provincial Key Research and Development Program(No.2024BCB008)the Hubei Provincial Natural Science Foundation of China(No.2025AFB733).
文摘Objective To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography(CTA)using subjective and objective metrics,with a focus on vessel edge sharpness.Methods This prospective study enrolled 45 patients who underwent ultra-low-voltage(60 kVp)head and neck CTA.Image datasets were reconstructed with filtered back-projection(FBP),ClearView(CV)and ClearInfinity(CI)algorithms at low(30%),medium(50%),and high(70%)strengths.Image quality was assessed subjectively and objectively via the Kruskal‒Wallis test for multiple comparisons.Objective parameters,including edge rise slope(ERS)and edge rise distance(ERD),were analyzed via the Friedman test of multiple comparisons statistics.Results Subjective assessments favored the CI50 reconstruction algorithm,demonstrating superior or satisfactory results compared to the other algorithms,with significantly better vessel delineation,edge definition and diagnostic confidence(all P<0.05).Objective analysis revealed that the CV50 and CV70 algorithms significantly reduced ERS and/or elevated ERD(both P<0.05).However,the CI50 algorithm maintained comparable vessel edge sharpness(P>0.05)across all evaluated head and neck vascular segments when compared with the FBP algorithm.Conclusions The CI50 reconstruction algorithm optimizes image quality in 60 kVp head and neck CTA.It provides vessel edge sharpness comparable to FBP while offering superior vessel delineation,edge definition,and diagnostic confidence compared to FBP and CV algorithm.These findings suggest that CI50 has the potential to improve diagnostic accuracy in low-dose vascular imaging.
基金supported by the National Natural Science Foundation of China(Grant Nos.52038005 and 52278342)the Natural Science Foundation of Tianjin Municipal(Grant No.23JCJQJC00160).
文摘Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack robustness.This study proposes a subgrade compaction strategy that utilizes a heterogeneous dataset to estimate compaction quality across diverse scenarios while maintaining model accuracy.Field compaction tests are conducted in four distinct scenarios,considering various construction parameters.Compaction models are developed using several machine learning algorithms.The datasets are thoroughly assessed in terms of quality,diversity and similarity.The proposed model exhibits good performance in new scenarios by incorporating an additional 5%e8%of new data for retraining.The model's generalization capability is enhanced by conducting a limited number of field tests,which are labor-saving and time-efficient.The model's accuracy consistently improves across diverse scenarios and optimal algorithms.The proposed compaction strategy adopts a physics-and-data dual-driven approach,aimed at practical engineering applications and guiding the compaction procedure.
