With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has ...With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.展开更多
Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a n...Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising： （1） two types of artificial neural networks （ANN） namely multi linear perceptron （MLP） and radial based function （RBF）; （2） an advancement of genetic programming namely linear genetic programming （LGP）; and （3） a support vector machine （SVM） technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error （RMSE）, Nash-Sutcliffe efficiency coefficient （NS）, mean absolute relative error （MARE） and, correlation coeffi- cient statistics （R） were used to choose the best predictive model. The comparison of estimation accu- racies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.展开更多
European air transport network(EATN)and Chinese air transport network(CATN),as two important air transport systems in the world,are facing increasingly spatial hazards,such as extreme weathers and natural disasters. I...European air transport network(EATN)and Chinese air transport network(CATN),as two important air transport systems in the world,are facing increasingly spatial hazards,such as extreme weathers and natural disasters. In order to reflect and compare impact of spatial hazards on the two networks in a practical way,a new spatial vulnerability model(SVM)is proposed in this paper,which analyzes vulnerability of a network system under spatial hazards from the perspectives of network topology and characteristics of hazards. Before introduction of the SVM,two abstract networks for EATN and CATN are established with a simple topological analysis by traditional vulnerability method. Then,the process to study vulnerability of an air transport network under spatial hazards by SVM is presented. Based on it,a comparative case study on EATN and CATN under two representative spatial hazard scenarios,one with an even spatial distribution,named as spatially uniform hazard,and the other with an uneven spatial distribution that takes rainstorm hazard as an example,is conducted. The simulation results show that both of EATN and CATN are robust to spatially uniform hazard,but vulnerable to rainstorm hazard. In the comparison of the results of the two networks that only stands from the points of network topology and characteristics of hazard without considering certain unequal factors,including airspace openness and flight safety importance in Europe and China,EATN is more vulnerable than CATN under rainstorm hazard. This suggests that when the two networks grow to a similar developed level in future,EATN needs to pay more attention to the impact of rainstorm hazard.展开更多
Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study,...Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study, we develop an image algorithm for preserved eggshell＇s crack detection by using natural light and polarized image. Four features including crack length, crack state coefficient, maximum projection and angular point are extracted from the natural light image by morphology calculus algorithms. The support vector machines（SVM） model with radial basis kernel function is established using the four features with an accuracy of about 92%. The detection accuracy is improved to 94% by using a new characteristic parameter of crack length on polarization image. The Multi-information fusion analysis indicates the potential for cracks detection by a real-time synthesis imaging system.展开更多
A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and le...A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented ...Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.展开更多
Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy...Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.展开更多
To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods b...To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves.The experiment starts directly from the multimodal physiological characteristics to explore pilots’behavior.Electroencephalography,electrocardiogram,and eye movement were recorded simultaneously.Extracted multimodal features of ground missions,air missions,and cruise mission were trained to generate support vector machine behavior model based on supervised learning.The results showed that different behaviors affects different multiple rhythm features,which are power spectra of theθwaves of EEG,standard deviation of normal to normal,root mean square of standard deviation and average gaze duration.The different physiological characteristics of the pilots could also be distinguished using an SVM model.Therefore,the multimodal physiological data can contribute to future research on the behavior activities of pilots.The result can be used to design and improve pilot training programs and automation interfaces.展开更多
Monitoring and analyzing expression levels of multiple biomarkers in biological samples can improve disease risk prediction and guide precision medicine but suffers from high cost and being time-consuming.Here,we cons...Monitoring and analyzing expression levels of multiple biomarkers in biological samples can improve disease risk prediction and guide precision medicine but suffers from high cost and being time-consuming.Here,we construct a fast molecular classifier based on freeze-thaw cycling that implements an in silico support vector machine(SVM)classifier model at the molecular level with a panel of disease-related biomarkers expression patterns for rapid disease diagnosis.The molecular classifier employs DNA reaction networks as the computing module and repeated dehydration and concentration process as the driving force to implement a set of simplified mathematical operations(such as multiplication,summation and subtraction)for efficient classification of complex input patterns.We demonstrate that the fast DNA-based molecular classifier enables precise cancer diagnosis within a short turnaround time in synthetic samples compared to those of free diffusion classifiers.We envision that this all-in-one molecular classifier will create more opportunities for inexpensive,accurate,and rapid disease diagnosis,prognosis and therapy,particularly in emergency departments or the point of care.展开更多
基金supported by a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918 and 202103)。
文摘With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.
文摘Most of the water quality models previously developed and used in dissolved oxygen （DO） prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising： （1） two types of artificial neural networks （ANN） namely multi linear perceptron （MLP） and radial based function （RBF）; （2） an advancement of genetic programming namely linear genetic programming （LGP）; and （3） a support vector machine （SVM） technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error （RMSE）, Nash-Sutcliffe efficiency coefficient （NS）, mean absolute relative error （MARE） and, correlation coeffi- cient statistics （R） were used to choose the best predictive model. The comparison of estimation accu- racies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.
基金This work was supported in part by the National Key Research and Development Program of China(No.2018YFC0823706-02)the Fundamental Research Funds for the Central Universities of China(No.3122019057).
文摘European air transport network(EATN)and Chinese air transport network(CATN),as two important air transport systems in the world,are facing increasingly spatial hazards,such as extreme weathers and natural disasters. In order to reflect and compare impact of spatial hazards on the two networks in a practical way,a new spatial vulnerability model(SVM)is proposed in this paper,which analyzes vulnerability of a network system under spatial hazards from the perspectives of network topology and characteristics of hazards. Before introduction of the SVM,two abstract networks for EATN and CATN are established with a simple topological analysis by traditional vulnerability method. Then,the process to study vulnerability of an air transport network under spatial hazards by SVM is presented. Based on it,a comparative case study on EATN and CATN under two representative spatial hazard scenarios,one with an even spatial distribution,named as spatially uniform hazard,and the other with an uneven spatial distribution that takes rainstorm hazard as an example,is conducted. The simulation results show that both of EATN and CATN are robust to spatially uniform hazard,but vulnerable to rainstorm hazard. In the comparison of the results of the two networks that only stands from the points of network topology and characteristics of hazard without considering certain unequal factors,including airspace openness and flight safety importance in Europe and China,EATN is more vulnerable than CATN under rainstorm hazard. This suggests that when the two networks grow to a similar developed level in future,EATN needs to pay more attention to the impact of rainstorm hazard.
基金Supported by the Fundamental Funds for Central University(2662014BQ062)
文摘Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study, we develop an image algorithm for preserved eggshell＇s crack detection by using natural light and polarized image. Four features including crack length, crack state coefficient, maximum projection and angular point are extracted from the natural light image by morphology calculus algorithms. The support vector machines（SVM） model with radial basis kernel function is established using the four features with an accuracy of about 92%. The detection accuracy is improved to 94% by using a new characteristic parameter of crack length on polarization image. The Multi-information fusion analysis indicates the potential for cracks detection by a real-time synthesis imaging system.
文摘A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.
基金This project is supported by Shanghai Automobile Industry Corporation Technology Foundation, China(No.0224).
文摘Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.
基金supported by the National Key R&D Program of China“R&D of security technologies and equipment for national oil and gas reserves”(No.:2017YFC0805800)National Natural Science Foundation of China“Model study of hydrogen-damage induced failure and integration evaluation for X80 pipe steel”(No.:51874322).
文摘Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.
文摘To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves.The experiment starts directly from the multimodal physiological characteristics to explore pilots’behavior.Electroencephalography,electrocardiogram,and eye movement were recorded simultaneously.Extracted multimodal features of ground missions,air missions,and cruise mission were trained to generate support vector machine behavior model based on supervised learning.The results showed that different behaviors affects different multiple rhythm features,which are power spectra of theθwaves of EEG,standard deviation of normal to normal,root mean square of standard deviation and average gaze duration.The different physiological characteristics of the pilots could also be distinguished using an SVM model.Therefore,the multimodal physiological data can contribute to future research on the behavior activities of pilots.The result can be used to design and improve pilot training programs and automation interfaces.
基金supported by the National Science Foundation of China(Grant Nos.21991134,22074041)the Shanghai Science and Technology Committee(STCSM)(23ZR1479600).
文摘Monitoring and analyzing expression levels of multiple biomarkers in biological samples can improve disease risk prediction and guide precision medicine but suffers from high cost and being time-consuming.Here,we construct a fast molecular classifier based on freeze-thaw cycling that implements an in silico support vector machine(SVM)classifier model at the molecular level with a panel of disease-related biomarkers expression patterns for rapid disease diagnosis.The molecular classifier employs DNA reaction networks as the computing module and repeated dehydration and concentration process as the driving force to implement a set of simplified mathematical operations(such as multiplication,summation and subtraction)for efficient classification of complex input patterns.We demonstrate that the fast DNA-based molecular classifier enables precise cancer diagnosis within a short turnaround time in synthetic samples compared to those of free diffusion classifiers.We envision that this all-in-one molecular classifier will create more opportunities for inexpensive,accurate,and rapid disease diagnosis,prognosis and therapy,particularly in emergency departments or the point of care.
