Objective Our objective is to build a model that explains the association between the exposure to trace elements in the soil and the risk of neural tube defects. Methods We built a function with different parameters t...Objective Our objective is to build a model that explains the association between the exposure to trace elements in the soil and the risk of neural tube defects. Methods We built a function with different parameters to describe the effects of trace elements on neural tube defects. The association between neural tube defects and trace element levels was transformed into an optimization problem using the maximum likelihood method. Results Tin, lead, nickel, iron, copper, and aluminum had typical layered effects （dosage effects） on the prevalence of neural tube defects. Arsenic, selenium, zinc, strontium, and vanadium had no effect, and molybdenum had one threshold value that affected the prevalence of birth defects. Conclusion As an exploratory research work, our model can be used to determine the direction of the effect of the trace element content of cultivated soil on the risk of neural tube defects, which shows the clues by the dosage effect of their toxicological characteristics. Based on our findings, future biogeochemical research should focus on the direct effects of trace elements on human health.展开更多
A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are...A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.展开更多
Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studi...Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.展开更多
大体积混凝土结构被广泛应用于土木、水利等领域的重大工程中,而混凝土抗拉强度低的力学特性决定了其易产生裂纹,因此,发展高效的检测方法,识别大体积混凝土结构中的裂纹信息十分必要.论文提出了一种新的方法,通过提取响应信号频谱中特...大体积混凝土结构被广泛应用于土木、水利等领域的重大工程中,而混凝土抗拉强度低的力学特性决定了其易产生裂纹,因此,发展高效的检测方法,识别大体积混凝土结构中的裂纹信息十分必要.论文提出了一种新的方法,通过提取响应信号频谱中特定频率的幅值特征,基于BP人工神经网络建立幅值特征与裂纹信息间的映射关系,从而有效识别出裂纹信息.首先采用扩展有限元法(eXtended Finite Element Methods, XFEM)和人工吸收边界模型,分别模拟了单裂纹和双裂纹情形下,大量不同裂纹信息下特定位置传感器的响应,分析其频谱曲线并提取特征,建立频谱特征—裂尖位置数据集,以训练人工神经网络,测试集的反演效果显示,该方法具有较好的准确度,可有效识别出裂纹信息.展开更多
基金supported by the National "973" project on Population and Health (No. 2007CB5119001)the National Yang‐Zi Scholar Program, 211 and 985 projects of Peking University (No. 20020903)
文摘Objective Our objective is to build a model that explains the association between the exposure to trace elements in the soil and the risk of neural tube defects. Methods We built a function with different parameters to describe the effects of trace elements on neural tube defects. The association between neural tube defects and trace element levels was transformed into an optimization problem using the maximum likelihood method. Results Tin, lead, nickel, iron, copper, and aluminum had typical layered effects （dosage effects） on the prevalence of neural tube defects. Arsenic, selenium, zinc, strontium, and vanadium had no effect, and molybdenum had one threshold value that affected the prevalence of birth defects. Conclusion As an exploratory research work, our model can be used to determine the direction of the effect of the trace element content of cultivated soil on the risk of neural tube defects, which shows the clues by the dosage effect of their toxicological characteristics. Based on our findings, future biogeochemical research should focus on the direct effects of trace elements on human health.
基金Project (No. 10102010) supported by the National Natural Science Foundation of China
文摘A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.
基金Otokar Otomotiv ve Savunma Sanayi A.S. for the financial support
文摘Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.
文摘大体积混凝土结构被广泛应用于土木、水利等领域的重大工程中,而混凝土抗拉强度低的力学特性决定了其易产生裂纹,因此,发展高效的检测方法,识别大体积混凝土结构中的裂纹信息十分必要.论文提出了一种新的方法,通过提取响应信号频谱中特定频率的幅值特征,基于BP人工神经网络建立幅值特征与裂纹信息间的映射关系,从而有效识别出裂纹信息.首先采用扩展有限元法(eXtended Finite Element Methods, XFEM)和人工吸收边界模型,分别模拟了单裂纹和双裂纹情形下,大量不同裂纹信息下特定位置传感器的响应,分析其频谱曲线并提取特征,建立频谱特征—裂尖位置数据集,以训练人工神经网络,测试集的反演效果显示,该方法具有较好的准确度,可有效识别出裂纹信息.
