One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne...One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
为尽可能地提高大气加权平均温度的估计精度,利用神经网络所建立的大气加权平均温度模型,却未顾及大气加权平均温度的长期变化趋势。因此,本文利用全球气候第五代再分析数据集和大气逐层数据,考虑大气加权平均温度的年际变化,基于多层...为尽可能地提高大气加权平均温度的估计精度,利用神经网络所建立的大气加权平均温度模型,却未顾及大气加权平均温度的长期变化趋势。因此,本文利用全球气候第五代再分析数据集和大气逐层数据,考虑大气加权平均温度的年际变化,基于多层感知器建立了湟水流域大气加权平均温度模型,并使用探空数据与已有的六种模型进行了比较验证分析。结果表明:本模型的年均偏差和均方根误差分别为–0.01 K、2.71 K;均方根误差相比于Bevis式、双因子、多因子、全球气压温度3(global pressure and temperature 3,GPT3)、改进的GPT3模型、谢劭峰等(2022)方法分别减小了32%、23%、15%、14%、7%、5%。验证了引入年际变化因子可进一步提高神经网络模型的精度,建立了目前湟水流域精度相对最优的大气加权平均温度模型。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12072217).
文摘One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
文摘为尽可能地提高大气加权平均温度的估计精度,利用神经网络所建立的大气加权平均温度模型,却未顾及大气加权平均温度的长期变化趋势。因此,本文利用全球气候第五代再分析数据集和大气逐层数据,考虑大气加权平均温度的年际变化,基于多层感知器建立了湟水流域大气加权平均温度模型,并使用探空数据与已有的六种模型进行了比较验证分析。结果表明:本模型的年均偏差和均方根误差分别为–0.01 K、2.71 K;均方根误差相比于Bevis式、双因子、多因子、全球气压温度3(global pressure and temperature 3,GPT3)、改进的GPT3模型、谢劭峰等(2022)方法分别减小了32%、23%、15%、14%、7%、5%。验证了引入年际变化因子可进一步提高神经网络模型的精度,建立了目前湟水流域精度相对最优的大气加权平均温度模型。
