This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting o...This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting of a PSS joint and a strut with fixed length.A general approximation algorithm is used to solve the problem.To avoid the extraction of root in the approximation process,the forward displacement analysis of the 8-PSS redundant parallel manipulator is transformed into another equivalent problem on the assumption that the strut is extensible while the slider is fixed.The problem is solved by a modified approximation algorithm which predicates that the manipulator will move along a pose vector to reduce the difference between the desired configuration and an instantaneous one,and the best movement should be with minimum norm and least quadratic sum.The characteristic of this modified algorithm is that its convergence domain is larger than that of the general approximation algorithm.Simulation results show that the modelified algorithm is general and can be used for the forward displacement analysis of the redundant parallel manipulator actuated by a revolute joint.展开更多
The Rock-soil interface is a common geological interface.Due to mechanical differences between soil and rock,the stress waves generated by underground blasting undergo intense polarization when crossing the rock-soil ...The Rock-soil interface is a common geological interface.Due to mechanical differences between soil and rock,the stress waves generated by underground blasting undergo intense polarization when crossing the rock-soil interface,making propagation laws difficult to predict.Currently,the characteristics of the impact of the rock-soil interface on blasting stress waves remain unclear.Therefore,the vibration field caused by cylindrical charge blasting in elastic rock and partial-saturation poro-viscoelastic soil was solved.A forward algorithm for the underground blasting vibration field in rock-soil sites was proposed,considering medium damping and geometric diffusion effects of stress waves.Further investigation into the influence of rock and soil parameters and blasting source parameters revealed the following conclusions:stress waves in soil exhibit dispersion,causing peak particle velocity(PPV)to display a discrete distribution.Soil parameters affect PPV attenuation only within the soil,while blasting source parameters affect PPV attenuation throughout the entire site.Multi-wave coupling effects induced by the rocksoil interface result in zones of enhanced and attenuated PPV within the site.The size of the enhancement zone is inversely correlated with the distance from the blasting source and positively correlated with the blasting source attenuation rate and burial depth,providing guidance for selecting explosives and blasting positions.Additionally,PPV attenuation rate increases with distance from the rock-soil interface,but an amplification effect occurs near the interface,most noticeable at 0.1 m.Thus,a sufficient safety distance from the rock-soil interface is necessary during underground blasting.展开更多
In this paper, a practical Werner-type continued fraction method for solving matrix valued rational interpolation problem is provided by using a generalized inverse of matrices. In order to reduce the continued fracti...In this paper, a practical Werner-type continued fraction method for solving matrix valued rational interpolation problem is provided by using a generalized inverse of matrices. In order to reduce the continued fraction form to rational function form of the interpolants, an efficient forward recurrence algorithm is obtained.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement wh...This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.展开更多
Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation ...Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation to optics,thereby leveraging the benefits of low power consumption,low latency,and high parallelism.The current training paradigm for ONNs primarily relies on backpropagation(BP).However,the reliance is incompatible with potential unknown processes within the system,which necessitates detailed knowledge and precise mathematical modeling of the optical process.In this paper,we present a pre-sensor multilayer ONN with nonlinear activation,utilizing a forward-forward algorithm to directly train both optical and digital parameters,which replaces the traditional backward pass with an additional forward pass.Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy,achieving a maximum enhancement of 9.0%.It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system.The proposed training method addresses the limitations of BP,paving the way for applications with a broader range of physical transformations in ONNs.展开更多
MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene regulators.In animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs...MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene regulators.In animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs with imperfect complementary pairing.Although bioinformatics investigations have resulted in a number of target prediction tools,all of these have a common shortcoming—a high false positive rate.Therefore,it is important to further filter the predicted targets.In this paper,based on miRNA:target duplex,we construct a second-order Hidden Markov Model,implement Baum-Welch training algorithm and apply this model to further process predicted targets.The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%,specificity of 55.10%and accuracy of 69.62%by 10-fold crossvalidation experiments.In order to further verify the applicability of the algorithm,previously collected datasets,including 195 positive and 38 negative,are chosen to test it,with consistent results.We believe that our method will provide some guidance for experimental biologists,especially in choosing miRNA targets for validation.展开更多
基金Funded by the National Natural Science Foundation of China(Grant No.50905102)the China Postdoctoral Science Foundation(Grant No.200801199)the Natural Science Foundation of Guangdong Province(Grant No.8351503101000001)
文摘This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting of a PSS joint and a strut with fixed length.A general approximation algorithm is used to solve the problem.To avoid the extraction of root in the approximation process,the forward displacement analysis of the 8-PSS redundant parallel manipulator is transformed into another equivalent problem on the assumption that the strut is extensible while the slider is fixed.The problem is solved by a modified approximation algorithm which predicates that the manipulator will move along a pose vector to reduce the difference between the desired configuration and an instantaneous one,and the best movement should be with minimum norm and least quadratic sum.The characteristic of this modified algorithm is that its convergence domain is larger than that of the general approximation algorithm.Simulation results show that the modelified algorithm is general and can be used for the forward displacement analysis of the redundant parallel manipulator actuated by a revolute joint.
基金supported by the National Natural Science Foundation of China(Grant Nos.41972286 and 42102329).
文摘The Rock-soil interface is a common geological interface.Due to mechanical differences between soil and rock,the stress waves generated by underground blasting undergo intense polarization when crossing the rock-soil interface,making propagation laws difficult to predict.Currently,the characteristics of the impact of the rock-soil interface on blasting stress waves remain unclear.Therefore,the vibration field caused by cylindrical charge blasting in elastic rock and partial-saturation poro-viscoelastic soil was solved.A forward algorithm for the underground blasting vibration field in rock-soil sites was proposed,considering medium damping and geometric diffusion effects of stress waves.Further investigation into the influence of rock and soil parameters and blasting source parameters revealed the following conclusions:stress waves in soil exhibit dispersion,causing peak particle velocity(PPV)to display a discrete distribution.Soil parameters affect PPV attenuation only within the soil,while blasting source parameters affect PPV attenuation throughout the entire site.Multi-wave coupling effects induced by the rocksoil interface result in zones of enhanced and attenuated PPV within the site.The size of the enhancement zone is inversely correlated with the distance from the blasting source and positively correlated with the blasting source attenuation rate and burial depth,providing guidance for selecting explosives and blasting positions.Additionally,PPV attenuation rate increases with distance from the rock-soil interface,but an amplification effect occurs near the interface,most noticeable at 0.1 m.Thus,a sufficient safety distance from the rock-soil interface is necessary during underground blasting.
文摘In this paper, a practical Werner-type continued fraction method for solving matrix valued rational interpolation problem is provided by using a generalized inverse of matrices. In order to reduce the continued fraction form to rational function form of the interpolants, an efficient forward recurrence algorithm is obtained.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
基金The authors would like to thank Covenant University Centre for Research Innovation and Discovery(CUCRID)Ota,Nigeria for its support in making the publication of this research possible.
文摘This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.
基金National Key Research and Development Program of China(2024YFE0203600)National Natural Science Foundation of China(62135009)Beijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science Park(Z221100005322010)。
文摘Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation to optics,thereby leveraging the benefits of low power consumption,low latency,and high parallelism.The current training paradigm for ONNs primarily relies on backpropagation(BP).However,the reliance is incompatible with potential unknown processes within the system,which necessitates detailed knowledge and precise mathematical modeling of the optical process.In this paper,we present a pre-sensor multilayer ONN with nonlinear activation,utilizing a forward-forward algorithm to directly train both optical and digital parameters,which replaces the traditional backward pass with an additional forward pass.Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy,achieving a maximum enhancement of 9.0%.It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system.The proposed training method addresses the limitations of BP,paving the way for applications with a broader range of physical transformations in ONNs.
基金This work is supported by The National Natural Science Foundation of China(Grant No.30871341)the grants from the National Key S&T Special Project of China(Nos.2008ZX10002-017,2008ZX10002-020,and 2009ZX09103-686)+1 种基金Shanghai Key Discipline of China(No.S30104)Education Commission Key Discipline Construction Project(No.J50101).
文摘MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene regulators.In animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs with imperfect complementary pairing.Although bioinformatics investigations have resulted in a number of target prediction tools,all of these have a common shortcoming—a high false positive rate.Therefore,it is important to further filter the predicted targets.In this paper,based on miRNA:target duplex,we construct a second-order Hidden Markov Model,implement Baum-Welch training algorithm and apply this model to further process predicted targets.The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%,specificity of 55.10%and accuracy of 69.62%by 10-fold crossvalidation experiments.In order to further verify the applicability of the algorithm,previously collected datasets,including 195 positive and 38 negative,are chosen to test it,with consistent results.We believe that our method will provide some guidance for experimental biologists,especially in choosing miRNA targets for validation.
