A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model wi...A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model with estimation procedure and tests for goodness-of-fit and under (or over) dispersion are shown and applied to road safety data. Two correlated outcome variables considered in this study are number of cars involved in an accident and number of casualties for given number of cars.展开更多
The factorial design within a conditional model is utilized when the effects of one factor in a factorial experiment hold greater significance under each fixed level of another factor.This paper investigates the gener...The factorial design within a conditional model is utilized when the effects of one factor in a factorial experiment hold greater significance under each fixed level of another factor.This paper investigates the generalized minimum aberration(N,sp)-design,where each factor is s-level,with s being any prime or prime power.Via utilizing the method of complementary designs,the authors explore the design with a pair of conditional and conditioning factors.The proposed approach applies not only to regular designs but also to nonregular designs.Additionally,the findings can be extrapolated to encompass designs under the two pairs conditional model.The findings presented in this paper not only strengthen but also generalize the existing knowledge in this field.展开更多
Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate b...Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are p...A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.展开更多
The objective of this work is to develop a novel feature for traffic flow models, when traffic queues on two-way arterials periodically extend until then they block an upstream signal in oversaturated conditions. The ...The objective of this work is to develop a novel feature for traffic flow models, when traffic queues on two-way arterials periodically extend until then they block an upstream signal in oversaturated conditions. The new model, proposed as conditional cell transmission model （CCTM） has been developed with two improvements. First, cell transmission model （CTM） is expanded for two-way arterials by taking account of all diverging and merging activities at intersections. Second, a conditional cell is added to simulate periodic spillback and blockages at an intersection. The results of experiments for a multilane, two-way, three-signal sample network demonstrate that CCTM can accommodate various traffic demands and accurate representation of blockages at intersections. The delay of left turns is underestimated by 40 % in moderate conditions and by 58% in oversamrated condition when using the CTM rather than CCTM. Finally, the consistency between HCS 2000 and CCTM shows that CCTM is a reliable methodology of modeling traffic flow in oversaturated condition.展开更多
A novel conditional cell transmission model （CCTM） is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition. To test the performance of the CCTM, ...A novel conditional cell transmission model （CCTM） is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition. To test the performance of the CCTM, a series of experiments for sensitivity analysis were designed and performed for a multilane, two-way, three-signal sample network. Experiment 1 shows that the model is performed in a logical and expected manner with variations in traffic demand with time and direction. Experiment 2 shows when the possibility of the occurrence of a useful gap increases to 60% and 100%, the delays in left rams decrease by 5% and 15%, respectively. In Experiment 3, comparing the possibility of a conditional cell of 0 with 100%, delay of left turn and delay of the entire network were underestimated by 58% and 11%, respectively. Hence, sensitivity analysis demonstrates that by reflecting local drivers＇ behaviors properly, the CCTM provides an accurate representation of traffic flow in simulating oversaturated traffic conditions.展开更多
In this paper, by making use of the Hadamard product of matrices, a natural and reasonable generalization of the univariate GARCH (Generalized Autoregressive Conditional heteroscedastic) process introduced by Bollersl...In this paper, by making use of the Hadamard product of matrices, a natural and reasonable generalization of the univariate GARCH (Generalized Autoregressive Conditional heteroscedastic) process introduced by Bollerslev (J. Econometrics 31(1986), 307-327) to the multivariate case is proposed. The conditions for the existence of strictly stationary and ergodic solutions and the existence of higher-order moments for this class of parametric models are derived.展开更多
To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively ap...To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.展开更多
Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmit...Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the <span style="font-family:Verdana;">main </span><span style="font-family:Verdana;">effects of genes and gene-covariate interaction</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;">. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results </span><span style="font-family:Verdana;">showed M235T is associat</span></span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female</span><span style="font-family:Verdana;">.</span>展开更多
This paper aims to assess the performances of different model initialization conditions(ICs)and lateral boundary conditions between two global models(GMs),i.e.,the European Centre for Medium-Range Weather Forecasts(EC...This paper aims to assess the performances of different model initialization conditions(ICs)and lateral boundary conditions between two global models(GMs),i.e.,the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP),on the accuracy of the Global/Regional Assimilation and Prediction System(GRAPES)forecasts for south China.A total of 3-month simulations during the rainy season were examined and a specific case of torrential rain over Guangdong Province was verified.Both ICs exhibited cold biases over south China,as well as a strong dry bias over the Pearl River Delta(PRD).In particular,the ICs from the ECMWF had a stronger cold bias over the PRD region and a more detailed structure than NCEP.In general,the NCEP provided a realistic surface temperature compared to the ECMWF over south China.Moreover,GRAPES initialized by the NCEP had better simulations of both location and intensity of precipitation than by the ECWMF.The results presented in this paper could be used as a general guideline to the operational numerical weather prediction that uses regional models driven by the GMs.展开更多
In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of ...In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.展开更多
Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for ...Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.展开更多
An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating s...An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.展开更多
This work presents the application of the recently developed “Fifth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (5<sup>th</sup>-CASAM-N)” to a simplified Bernoulli ...This work presents the application of the recently developed “Fifth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (5<sup>th</sup>-CASAM-N)” to a simplified Bernoulli model. The 5<sup>th</sup>-CASAM-N builds upon and incorporates all of the lower-order (i.e., the first-, second-, third-, and fourth-order) adjoint sensitivities analysis methodologies. The Bernoulli model comprises a nonlinear model response, uncertain model parameters, uncertain model domain boundaries and uncertain model boundary conditions, admitting closed-form explicit expressions for the response sensitivities of all orders. Illustrating the specific mechanisms and advantages of applying the 5<sup>th</sup>-CASAM-N for the computation of the response sensitivities with respect to the uncertain parameters and boundaries reveals that the 5<sup>th</sup>-CASAM-N provides a fundamental step towards overcoming the curse of dimensionality in sensitivity and uncertainty analysis.展开更多
We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain s...We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain some insight on the meaning of the energy conditions, we illustrate the evolutions of four energy conditions with the model parameter ε for different n. By analysis we give the constraints on the model parameters ε.展开更多
Jacket-type offshore platforms are widely used for oil, gas field, and energy development in shallow water. The design of a jacket structure is highly dependent on target environmental variables. This study focuses on...Jacket-type offshore platforms are widely used for oil, gas field, and energy development in shallow water. The design of a jacket structure is highly dependent on target environmental variables. This study focuses on a strategy to estimate design loads for offshore jacket structures based on an environmental contour approach. In addition to the popular conditional distribution model, various classes of bivariate copulas are adopted to construct joint distributions of environmental variables. Analytical formulations of environmental contours based on various models are presented and discussed in this study. The design loads are examined by dynamic response analysis of jacket platform. Results suggest that the conditional model is not recommended for use in estimating design loads in sampling locations due to poor fitting results. Independent copula produces conservative design loads and the extreme response obtained using the conditional model are smaller than those determined by copulas. The suitability of a model for contour construction varies with the origin of wave data. This study provides a reference for the design load estimation of jacket structures and offers an alternative procedure to determine the design criteria for offshore structures.展开更多
A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal ...A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.展开更多
In this work, an enhanced treatment of the solid boundaries is proposed for smoothed particle hydrodynamics with implicit time integration scheme （Implicit SPH）. Three types of virtual particles, i.e., boundary part...In this work, an enhanced treatment of the solid boundaries is proposed for smoothed particle hydrodynamics with implicit time integration scheme （Implicit SPH）. Three types of virtual particles, i.e., boundary particles, image particles and mirror particles, are used to impose boundary conditions. Boundary particles are fixed on the solid boundary, and each boundary particle is associated with two fixed image particles inside the fluid domain and two fixed mirror particles outside the fluid domain. The image particles take the flow properties through fluid particles with moving least squares （MLS） interpolation and the properties of mirror particles can be obtained by the corresponding image particles. A repulsive force is also applied for boundary particles to prevent fluid particles from unphysical penetra- tion through solid boundaries. The new boundary treatment method has been validated with five numerical examples. All the numerical results show that Implicit SPH with this new boundary-treatment method can obtain accurate results for non-Newtonian fluids as well as Newtonian fluids, and this method is suitable for complex solid boundaries and can be easily extended to 3D problems.展开更多
Isothermal hot compression tests of as-cast high-Cr ultra-super-critical（USC） rotor steel with columnar grains perpendicular to the compression direction were carried out in the temperature range from 950 to 1250...Isothermal hot compression tests of as-cast high-Cr ultra-super-critical（USC） rotor steel with columnar grains perpendicular to the compression direction were carried out in the temperature range from 950 to 1250°C at strain rates ranging from 0.001 to 1 s^（-1）. The softening mechanism was dynamic recovery（DRV） at 950°C and the strain rate of 1 s^（-1）, whereas it was dynamic recrystallization（DRX） under the other conditions. A modified constitutive equation based on the Arrhenius model with strain compensation reasonably predicted the flow stress under various deformation conditions, and the activation energy was calculated to be 643.92 kJ ×mol^（-1）. The critical stresses of dynamic recrystallization under different conditions were determined from the work-hardening rate（θ）–flow stress（σ） and-θ/σ–σ curves. The optimum processing parameters via analysis of the processing map and the softening mechanism were determined to be a deformation temperature range from 1100 to 1200°C and a strain-rate range from 0.001 to 0.08 s^（-1）, with a power dissipation efficiency η greater than 31%.展开更多
文摘A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model with estimation procedure and tests for goodness-of-fit and under (or over) dispersion are shown and applied to road safety data. Two correlated outcome variables considered in this study are number of cars involved in an accident and number of casualties for given number of cars.
基金supported by the National Natural Science Foundation of China under Grant Nos.12301318 and 12101357Beijing Institute of Technology Research Fund Program for Young Scholars and Natural Science Foundation of Shandong under Grant No.ZR2021QA080。
文摘The factorial design within a conditional model is utilized when the effects of one factor in a factorial experiment hold greater significance under each fixed level of another factor.This paper investigates the generalized minimum aberration(N,sp)-design,where each factor is s-level,with s being any prime or prime power.Via utilizing the method of complementary designs,the authors explore the design with a pair of conditional and conditioning factors.The proposed approach applies not only to regular designs but also to nonregular designs.Additionally,the findings can be extrapolated to encompass designs under the two pairs conditional model.The findings presented in this paper not only strengthen but also generalize the existing knowledge in this field.
基金supported by the National Natural Science Foundation of China(Grant No.52109010)the Postdoctoral Science Foundation of China(Grant No.2021M701047)the China National Postdoctoral Program for Innovative Talents(Grant No.BX20200113).
文摘Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
文摘A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.
基金Project(51108343) supported by the National Natural Science Foundation of ChinaProject(06121) supported by University of Transportation Center for Alabama,USA
文摘The objective of this work is to develop a novel feature for traffic flow models, when traffic queues on two-way arterials periodically extend until then they block an upstream signal in oversaturated conditions. The new model, proposed as conditional cell transmission model （CCTM） has been developed with two improvements. First, cell transmission model （CTM） is expanded for two-way arterials by taking account of all diverging and merging activities at intersections. Second, a conditional cell is added to simulate periodic spillback and blockages at an intersection. The results of experiments for a multilane, two-way, three-signal sample network demonstrate that CCTM can accommodate various traffic demands and accurate representation of blockages at intersections. The delay of left turns is underestimated by 40 % in moderate conditions and by 58% in oversamrated condition when using the CTM rather than CCTM. Finally, the consistency between HCS 2000 and CCTM shows that CCTM is a reliable methodology of modeling traffic flow in oversaturated condition.
基金Project(51108343)supported by the National Natural Science Foundation of ChinaProject(06121)supported by University of Transportation Center for Alabama,USA
文摘A novel conditional cell transmission model （CCTM） is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition. To test the performance of the CCTM, a series of experiments for sensitivity analysis were designed and performed for a multilane, two-way, three-signal sample network. Experiment 1 shows that the model is performed in a logical and expected manner with variations in traffic demand with time and direction. Experiment 2 shows when the possibility of the occurrence of a useful gap increases to 60% and 100%, the delays in left rams decrease by 5% and 15%, respectively. In Experiment 3, comparing the possibility of a conditional cell of 0 with 100%, delay of left turn and delay of the entire network were underestimated by 58% and 11%, respectively. Hence, sensitivity analysis demonstrates that by reflecting local drivers＇ behaviors properly, the CCTM provides an accurate representation of traffic flow in simulating oversaturated traffic conditions.
文摘In this paper, by making use of the Hadamard product of matrices, a natural and reasonable generalization of the univariate GARCH (Generalized Autoregressive Conditional heteroscedastic) process introduced by Bollerslev (J. Econometrics 31(1986), 307-327) to the multivariate case is proposed. The conditions for the existence of strictly stationary and ergodic solutions and the existence of higher-order moments for this class of parametric models are derived.
文摘To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.
文摘Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the <span style="font-family:Verdana;">main </span><span style="font-family:Verdana;">effects of genes and gene-covariate interaction</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;">. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results </span><span style="font-family:Verdana;">showed M235T is associat</span></span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female</span><span style="font-family:Verdana;">.</span>
基金National Key R&D Program of China(2018YFC1506901)National Natural Science Foundation of China(41505084)Guangzhou Science and Technology Project(201804020038)
文摘This paper aims to assess the performances of different model initialization conditions(ICs)and lateral boundary conditions between two global models(GMs),i.e.,the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP),on the accuracy of the Global/Regional Assimilation and Prediction System(GRAPES)forecasts for south China.A total of 3-month simulations during the rainy season were examined and a specific case of torrential rain over Guangdong Province was verified.Both ICs exhibited cold biases over south China,as well as a strong dry bias over the Pearl River Delta(PRD).In particular,the ICs from the ECMWF had a stronger cold bias over the PRD region and a more detailed structure than NCEP.In general,the NCEP provided a realistic surface temperature compared to the ECMWF over south China.Moreover,GRAPES initialized by the NCEP had better simulations of both location and intensity of precipitation than by the ECWMF.The results presented in this paper could be used as a general guideline to the operational numerical weather prediction that uses regional models driven by the GMs.
基金Project(51108343) supported by the National Natural Science Foundation of ChinaProject(06121) supported by University of Transportation Center for Alabama, USA
文摘In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41705082, 41475101, 41690122(41690120))a Chinese Academy of Sciences Strategic Priority Project-the Western Pacific Ocean System (Grant Nos. XDA11010105 and XDA11020306)+1 种基金the National Programme on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI06 and GASI-IPOVAI-01-01)the China Postdoctoral Science Foundation, and a Qingdao Postdoctoral Application Research Project
文摘Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.
基金supported by the National Natural Science Foundation of China (Nos. 51178018 and 71031001)
文摘An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.
文摘This work presents the application of the recently developed “Fifth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (5<sup>th</sup>-CASAM-N)” to a simplified Bernoulli model. The 5<sup>th</sup>-CASAM-N builds upon and incorporates all of the lower-order (i.e., the first-, second-, third-, and fourth-order) adjoint sensitivities analysis methodologies. The Bernoulli model comprises a nonlinear model response, uncertain model parameters, uncertain model domain boundaries and uncertain model boundary conditions, admitting closed-form explicit expressions for the response sensitivities of all orders. Illustrating the specific mechanisms and advantages of applying the 5<sup>th</sup>-CASAM-N for the computation of the response sensitivities with respect to the uncertain parameters and boundaries reveals that the 5<sup>th</sup>-CASAM-N provides a fundamental step towards overcoming the curse of dimensionality in sensitivity and uncertainty analysis.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11175077 and 11575075the Natural Science Foundation of Liaoning Province under Grant No L201683666
文摘We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain some insight on the meaning of the energy conditions, we illustrate the evolutions of four energy conditions with the model parameter ε for different n. By analysis we give the constraints on the model parameters ε.
基金supported by the National Key Research and Development Program (No. 2016YFC0303401)the National Natural Science Foundation of China (No. 51779236)the National Natural Science Foundation of China–Shandong Joint Fund Project (No. U1706226)。
文摘Jacket-type offshore platforms are widely used for oil, gas field, and energy development in shallow water. The design of a jacket structure is highly dependent on target environmental variables. This study focuses on a strategy to estimate design loads for offshore jacket structures based on an environmental contour approach. In addition to the popular conditional distribution model, various classes of bivariate copulas are adopted to construct joint distributions of environmental variables. Analytical formulations of environmental contours based on various models are presented and discussed in this study. The design loads are examined by dynamic response analysis of jacket platform. Results suggest that the conditional model is not recommended for use in estimating design loads in sampling locations due to poor fitting results. Independent copula produces conservative design loads and the extreme response obtained using the conditional model are smaller than those determined by copulas. The suitability of a model for contour construction varies with the origin of wave data. This study provides a reference for the design load estimation of jacket structures and offers an alternative procedure to determine the design criteria for offshore structures.
基金Project(50809058)supported by the National Natural Science Foundation of China
文摘A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.
基金supported by the National Natural Science Foundation of China(51276192)
文摘In this work, an enhanced treatment of the solid boundaries is proposed for smoothed particle hydrodynamics with implicit time integration scheme （Implicit SPH）. Three types of virtual particles, i.e., boundary particles, image particles and mirror particles, are used to impose boundary conditions. Boundary particles are fixed on the solid boundary, and each boundary particle is associated with two fixed image particles inside the fluid domain and two fixed mirror particles outside the fluid domain. The image particles take the flow properties through fluid particles with moving least squares （MLS） interpolation and the properties of mirror particles can be obtained by the corresponding image particles. A repulsive force is also applied for boundary particles to prevent fluid particles from unphysical penetra- tion through solid boundaries. The new boundary treatment method has been validated with five numerical examples. All the numerical results show that Implicit SPH with this new boundary-treatment method can obtain accurate results for non-Newtonian fluids as well as Newtonian fluids, and this method is suitable for complex solid boundaries and can be easily extended to 3D problems.
基金supported by the Major State Basic Research Development Program of China (No.2011CB012900)the National Natural Science Foundation of China (No.51374144)the Shanghai Rising-Star Program (No.14QA1402300)
文摘Isothermal hot compression tests of as-cast high-Cr ultra-super-critical（USC） rotor steel with columnar grains perpendicular to the compression direction were carried out in the temperature range from 950 to 1250°C at strain rates ranging from 0.001 to 1 s^（-1）. The softening mechanism was dynamic recovery（DRV） at 950°C and the strain rate of 1 s^（-1）, whereas it was dynamic recrystallization（DRX） under the other conditions. A modified constitutive equation based on the Arrhenius model with strain compensation reasonably predicted the flow stress under various deformation conditions, and the activation energy was calculated to be 643.92 kJ ×mol^（-1）. The critical stresses of dynamic recrystallization under different conditions were determined from the work-hardening rate（θ）–flow stress（σ） and-θ/σ–σ curves. The optimum processing parameters via analysis of the processing map and the softening mechanism were determined to be a deformation temperature range from 1100 to 1200°C and a strain-rate range from 0.001 to 0.08 s^（-1）, with a power dissipation efficiency η greater than 31%.
