Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead p...Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.展开更多
Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for mo...Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as 展开更多
Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using t...Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.展开更多
On the basis of a well-established binomial structure and the socalled Poisson-Lindley distribution,a new two-parameter discrete distribution is introduced.Its properties are studied from both the theoretical and prac...On the basis of a well-established binomial structure and the socalled Poisson-Lindley distribution,a new two-parameter discrete distribution is introduced.Its properties are studied from both the theoretical and practical sides.For the theory,we discuss the moments,survival and hazard rate functions,mode and quantile function.The statistical inference on the model parameters is investigated by the maximum likelihood,moments,proportions,least square,and weighted least square estimations.A simulation study is conducted to observe the performance of the bias and mean square error of the obtained estimates.Then,applications to two practical data sets are given.Finally,we construct a new flexible count data regression model called the binomial-Poisson Lindley regression model with two practical examples in the medical area.展开更多
Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all ...Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all aspects of travel behavior, including travel frequency, travel distances, mode choice and time-of-day choice. At the same time, car ownership affects residential location choices, as households owning cars are less likely to choose urban neighborhoods than households without cars. This paper describes a new microscopic auto-ownership model that has been estimated with survey data. The model is fully integrated with a land use and a transportation model to capture： （1） how owning a car affects travel behavior and location choice; and （2） how the built environment and the transportation needs affect auto-ownership decisions. The model has been validated against census data and is fully operational.展开更多
针对现有气吸式高速精密排种器遇负压骤降时易发生大量漏播的技术难题,设计了一种在排种盘上同时设有吸孔、导种槽和取种槽3种种子拾取机构的气吸机械复合式大豆精密排种器,其中导种槽引导种子向取种槽运动,取种槽拾取种子,同时吸孔产...针对现有气吸式高速精密排种器遇负压骤降时易发生大量漏播的技术难题,设计了一种在排种盘上同时设有吸孔、导种槽和取种槽3种种子拾取机构的气吸机械复合式大豆精密排种器,其中导种槽引导种子向取种槽运动,取种槽拾取种子,同时吸孔产生吸力促进种子的拾取,通过3种拾取机构共同改变种群运移行为,保证气流负压骤降情况下的排种性能;通过离散元仿真设计和理论建模分析等方法,研究关键设计参数对种群运移规律的影响,并对关键部件几何结构参数进行优化设计;通过回归分析和多因素试验得出作业速度、取种槽和导种槽几何结构尺寸、负压均对排种器播种效果有显著影响,并得出排种器最优结构参数为:导种槽倾角45°、导种槽深度2 mm、取种槽上边宽度9.5 mm、取种槽下边宽度7.3 mm、取种槽深度5.7 mm、取种槽前后槽面宽度9.5 mm,在该几何结构条件下,当作业速度不大于8.6 km/h、负压不小于1.6 k Pa时,播种粒距合格率不小于95%;通过排种器的田间验证试验,最优结构参数条件下该排种器播种粒距合格率为93.67%、重播率为3.32%、漏播率为3.01%;通过台架对比试验得出当负压降至1.1 k Pa时,该排种器相较于勃农气吸式排种器和MASCHIO气吸式排种器,粒距合格率分别提高6.48、1.92个百分点,当负压降至0.6 k Pa时,粒距合格率分别提高9.12、4.25个百分点。展开更多
基金supported by the National Natural ScienceFoundation of China(Grant No.41672360)Science and Technology Commission of Shanghai Munici-pality(Grant No.17DZ1203800)Shanghai Shentong Metro Group Co.,Ltd.(Grant No.17DZ1203804).
文摘Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.
文摘Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as
基金supported in part by HKRGC GrantHKU Strategic Theme Grant on Computational SciencesNational Natural Science Foundation of China under Grant Nos.10971075 and 11271144
文摘Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.
文摘On the basis of a well-established binomial structure and the socalled Poisson-Lindley distribution,a new two-parameter discrete distribution is introduced.Its properties are studied from both the theoretical and practical sides.For the theory,we discuss the moments,survival and hazard rate functions,mode and quantile function.The statistical inference on the model parameters is investigated by the maximum likelihood,moments,proportions,least square,and weighted least square estimations.A simulation study is conducted to observe the performance of the bias and mean square error of the obtained estimates.Then,applications to two practical data sets are given.Finally,we construct a new flexible count data regression model called the binomial-Poisson Lindley regression model with two practical examples in the medical area.
文摘Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all aspects of travel behavior, including travel frequency, travel distances, mode choice and time-of-day choice. At the same time, car ownership affects residential location choices, as households owning cars are less likely to choose urban neighborhoods than households without cars. This paper describes a new microscopic auto-ownership model that has been estimated with survey data. The model is fully integrated with a land use and a transportation model to capture： （1） how owning a car affects travel behavior and location choice; and （2） how the built environment and the transportation needs affect auto-ownership decisions. The model has been validated against census data and is fully operational.
文摘针对现有气吸式高速精密排种器遇负压骤降时易发生大量漏播的技术难题,设计了一种在排种盘上同时设有吸孔、导种槽和取种槽3种种子拾取机构的气吸机械复合式大豆精密排种器,其中导种槽引导种子向取种槽运动,取种槽拾取种子,同时吸孔产生吸力促进种子的拾取,通过3种拾取机构共同改变种群运移行为,保证气流负压骤降情况下的排种性能;通过离散元仿真设计和理论建模分析等方法,研究关键设计参数对种群运移规律的影响,并对关键部件几何结构参数进行优化设计;通过回归分析和多因素试验得出作业速度、取种槽和导种槽几何结构尺寸、负压均对排种器播种效果有显著影响,并得出排种器最优结构参数为:导种槽倾角45°、导种槽深度2 mm、取种槽上边宽度9.5 mm、取种槽下边宽度7.3 mm、取种槽深度5.7 mm、取种槽前后槽面宽度9.5 mm,在该几何结构条件下,当作业速度不大于8.6 km/h、负压不小于1.6 k Pa时,播种粒距合格率不小于95%;通过排种器的田间验证试验,最优结构参数条件下该排种器播种粒距合格率为93.67%、重播率为3.32%、漏播率为3.01%;通过台架对比试验得出当负压降至1.1 k Pa时,该排种器相较于勃农气吸式排种器和MASCHIO气吸式排种器,粒距合格率分别提高6.48、1.92个百分点,当负压降至0.6 k Pa时,粒距合格率分别提高9.12、4.25个百分点。
