交通流量因受周期性特征、突发状况等多重因素影响,现有模型的预测精度无法满足实际要求.对此,本文提出了基于误差补偿的多模态协同交通流预测模型(Multimodal Collaborative traffic flow prediction model based on Error Compensatio...交通流量因受周期性特征、突发状况等多重因素影响,现有模型的预测精度无法满足实际要求.对此,本文提出了基于误差补偿的多模态协同交通流预测模型(Multimodal Collaborative traffic flow prediction model based on Error Compensation,MCEC).针对传统预测模型不能兼顾时间序列和协变量的问题,提出基于小波分析的特征拓展方法,该方法引入聚类算法得到节假日标签特征,将拥堵指数、交通事故图、天气信息作为拓展特征,对特征进行多尺度分解.在训练阶段,为达到充分学习各部分数据、最优匹配模型的效果,采用差分整合移动平均自回归模型(Autoreg Ressive Integrated Moving Average Model,ARIMA)、长短期记忆神经网络(Long Short-Term Memory network,LSTM)、限制动态时间规整技术(Dynamic Time Warping,DTW)以及自注意力机制(Self-Attention),设计了多模态协同模型训练.在误差补偿阶段,将得到的相应过程值输入基于支持向量机回归(Support Vector Regression,SVR)的误差补偿模块,对各分量的误差进行学习、补偿,并重构得到预测结果.使用公开的高速公路数据集对MCEC进行验证,在多个时间间隔下对比实验结果表明,MCEC在交通流量预测中的平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)达到17.02%,比LSTM-SVR、ConvLSTM(Convolutional Long Short-Term Memory network)、ST-GCN(Spatial Temporal Graph Convolutional Networks)、MFFB(Multi-stream Feature Fusion Block)、Transformer等预测模型具有更高的预测精度,MCEC模型具有较好的有效性与合理性.展开更多
In the past two decades,model averaging,as a way to solve model uncertainty,has attracted more and more attention.In this paper,the authors propose a jackknife model averaging(JMA) method for the quantile single-index...In the past two decades,model averaging,as a way to solve model uncertainty,has attracted more and more attention.In this paper,the authors propose a jackknife model averaging(JMA) method for the quantile single-index coefficient model,which is widely used in statistics.Under model misspecification,the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile loss.Simulation experiments are conducted to compare the JMA method with several model selections and model averaging methods,and the results show that the proposed method has a satisfactory performance.The method is also applied to a real dataset.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
Insight into average oil pressure in gas reservoirs and changes in production （time）, play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, ...Insight into average oil pressure in gas reservoirs and changes in production （time）, play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, average reservoir pressure can be conducted only when producing wells are shut in. This is regarded as a pressure build-up test. During the test, the wellbore pressure is recorded as a function of time. Currently, the only available method with which to obtain average reservoir pressure is to conduct an extended build-up test. It must then be evaluated using Homer or MDH （Miller, Dyes and Huchinson） valuation procedures. During production, average reservoir pressure declines due to fluid withdrawal from the wells and therefore, the average reservoirpressure is updated, periodically. A significant economic loss occurs during the entire pressure build-up test when producing wells are shut in. In this study, a neural network model has been established to map a nonlinear time-varying relationship which controls reservoir production history in order to predict and interpolate average reservoir pressure without closing the producing wells. This technique is suitable for constant and variable flow rates.展开更多
通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄...通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.U23A2064 and 12031005。
文摘In the past two decades,model averaging,as a way to solve model uncertainty,has attracted more and more attention.In this paper,the authors propose a jackknife model averaging(JMA) method for the quantile single-index coefficient model,which is widely used in statistics.Under model misspecification,the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile loss.Simulation experiments are conducted to compare the JMA method with several model selections and model averaging methods,and the results show that the proposed method has a satisfactory performance.The method is also applied to a real dataset.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
文摘Insight into average oil pressure in gas reservoirs and changes in production （time）, play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, average reservoir pressure can be conducted only when producing wells are shut in. This is regarded as a pressure build-up test. During the test, the wellbore pressure is recorded as a function of time. Currently, the only available method with which to obtain average reservoir pressure is to conduct an extended build-up test. It must then be evaluated using Homer or MDH （Miller, Dyes and Huchinson） valuation procedures. During production, average reservoir pressure declines due to fluid withdrawal from the wells and therefore, the average reservoirpressure is updated, periodically. A significant economic loss occurs during the entire pressure build-up test when producing wells are shut in. In this study, a neural network model has been established to map a nonlinear time-varying relationship which controls reservoir production history in order to predict and interpolate average reservoir pressure without closing the producing wells. This technique is suitable for constant and variable flow rates.
文摘通过一维杆的一维传热的分组显式数值求解,分析热弹性效应的存在及规律,得出随着时间的增长,温升—热变形之间的关系会逐渐趋近稳态,但不可能获得绝对的稳态;在传热过程中,随着距离增加,温度衰减很快,离热源越远的点的热弹性效环应越窄。提出用非线性时序模型与前向神经网络相结合的模型(Nonlinear auto-regressive moving average neural network with exogenousinputs,NARMAX-NN)来辨识热弹性效应。用NARMAX-NN模型对高速进给系统试验台的热动态特性进行建模,获得良好的效果。此方法比多变量回归模型、反馈神经网络模型及广义最小二乘输出误差模型有更好的精度和鲁棒性,能精确地对复杂结构、多热源的时变非线性热误差特性进行建模和预测。
