ICU patients are vulnerable to medications,especially infusion medications,and the rate and dosage of infusion drugs may worsen the condition.The mortality prediction model can monitor the real-time response of patien...ICU patients are vulnerable to medications,especially infusion medications,and the rate and dosage of infusion drugs may worsen the condition.The mortality prediction model can monitor the real-time response of patients to drug treatment,evaluate doctors’treatment plans to avoid severe situations such as inverse Drug-Drug Interactions(DDI),and facilitate the timely intervention and adjustment of doctor’s treatment plan.The treatment process of patients usually has a time-sequence relation(which usually has the missing data problem)in patients’treatment history.The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network(RNN).However,sometimes,patients’treatment can last for a long period of time,which RNN may not fit for modelling long time sequence data.Therefore,we propose to use the heterogeneous medication events driven LSTM to predict the outcome of the patient,and the Natural Language Processing and Gaussian Process(GP),which can handle noisy,incomplete,sparse,heterogeneous and unevenly sampled patients’medication records.In our work,we emphasize the semantic meaning of each medication event and the sequence of the medication events on patients,while also handling the missing value problem using kernel-based Gaussian process.We compare the performance of LSTM and Phased-LSTM on modelling the outcome of patients’treatment and data imputation using kernel-based Gaussian process and conduct an empirical study on different data imputation approaches.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪...为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。展开更多
传统惯导/卫导组合导航在多元复杂环境下易受干扰,从而导致观测量异常影响导航性能。以无人驾驶车辆为研究对象,展开提升组合导航系统导航精度的研究。采用深度高斯过程(deep Gaussian process,DGP)辅助估计位置的方法减小组合导航误差...传统惯导/卫导组合导航在多元复杂环境下易受干扰,从而导致观测量异常影响导航性能。以无人驾驶车辆为研究对象,展开提升组合导航系统导航精度的研究。采用深度高斯过程(deep Gaussian process,DGP)辅助估计位置的方法减小组合导航误差,提高定位性能。基于DGP的辅助导航方法不仅可以预测无人驾驶车辆的标称轨迹,同时可以预测各时刻位置可信区间的概率分布,为基于深度学习模型的数据融合预测方法提供了严格的理论解释性。真实历史数据下的多重对比实验表明,该算法较传统深度神经网络算法具有更高的精度和可靠性。基于DGP的辅助导航方式能有效提高全球卫星定位系统信号失锁时的导航模型性能,实验表明相对于纯惯性导航系统(integral navigation system,INS)解算和长短期记忆(long and short term memory,LSTM)进行导航信号补偿定位精度分别提高了97.32%和52.13%。展开更多
基金This research is supported by Natural Science Foundation of Hunan Province(No.2019JJ40145)Scientific Research Key Project of Hunan Education Department(No.19A273)Open Fund of Key Laboratory of Hunan Province(2017TP1026).
文摘ICU patients are vulnerable to medications,especially infusion medications,and the rate and dosage of infusion drugs may worsen the condition.The mortality prediction model can monitor the real-time response of patients to drug treatment,evaluate doctors’treatment plans to avoid severe situations such as inverse Drug-Drug Interactions(DDI),and facilitate the timely intervention and adjustment of doctor’s treatment plan.The treatment process of patients usually has a time-sequence relation(which usually has the missing data problem)in patients’treatment history.The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network(RNN).However,sometimes,patients’treatment can last for a long period of time,which RNN may not fit for modelling long time sequence data.Therefore,we propose to use the heterogeneous medication events driven LSTM to predict the outcome of the patient,and the Natural Language Processing and Gaussian Process(GP),which can handle noisy,incomplete,sparse,heterogeneous and unevenly sampled patients’medication records.In our work,we emphasize the semantic meaning of each medication event and the sequence of the medication events on patients,while also handling the missing value problem using kernel-based Gaussian process.We compare the performance of LSTM and Phased-LSTM on modelling the outcome of patients’treatment and data imputation using kernel-based Gaussian process and conduct an empirical study on different data imputation approaches.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。
文摘传统惯导/卫导组合导航在多元复杂环境下易受干扰,从而导致观测量异常影响导航性能。以无人驾驶车辆为研究对象,展开提升组合导航系统导航精度的研究。采用深度高斯过程(deep Gaussian process,DGP)辅助估计位置的方法减小组合导航误差,提高定位性能。基于DGP的辅助导航方法不仅可以预测无人驾驶车辆的标称轨迹,同时可以预测各时刻位置可信区间的概率分布,为基于深度学习模型的数据融合预测方法提供了严格的理论解释性。真实历史数据下的多重对比实验表明,该算法较传统深度神经网络算法具有更高的精度和可靠性。基于DGP的辅助导航方式能有效提高全球卫星定位系统信号失锁时的导航模型性能,实验表明相对于纯惯性导航系统(integral navigation system,INS)解算和长短期记忆(long and short term memory,LSTM)进行导航信号补偿定位精度分别提高了97.32%和52.13%。
