This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube s...This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.展开更多
为实现能源多梯度利用并减少环境污染,气-电耦合的综合能源系统(integrated energy system,IES)逐渐占据全球能源结构的重要部分。为明晰系统运行特性并充分挖掘能源子网调度潜力,综合能源系统动态仿真研究逐渐深入。天然气网中,管道传...为实现能源多梯度利用并减少环境污染,气-电耦合的综合能源系统(integrated energy system,IES)逐渐占据全球能源结构的重要部分。为明晰系统运行特性并充分挖掘能源子网调度潜力,综合能源系统动态仿真研究逐渐深入。天然气网中,管道传输过程深刻影响着其动态过程。然而现有管道传输过程时域仿真算法存在着精度与效率的两难。为解决上述问题,提出一种基于时域二端口模型的综合能源系统气网动态仿真算法。首先基于特征线法,构建气网源荷节点关系矩阵,进一步给出时域二端口模型;随后参考特征线法数值格式,构建状态量空间分布矩阵,用于获取气网状态量分布,并提出基于时域二端口模型和分布矩阵的气网动态仿真算法。算例结果表明,所提出的方法具有高效率和高精度两大优势,适合于气-电耦合的综合能源系统的较长时间仿真。展开更多
Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural netwo...Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets.展开更多
土壤养分具有变异性,其变异性特征能够反映区域生态功能和景观格局的动态变化,是土壤的重要属性之一。本文从土壤养分空间变异的研究意义出发,通过对相关文献的阅读分析,简述了国内外关于土壤养分空间变异研究的发展历程和研究现状,分...土壤养分具有变异性,其变异性特征能够反映区域生态功能和景观格局的动态变化,是土壤的重要属性之一。本文从土壤养分空间变异的研究意义出发,通过对相关文献的阅读分析,简述了国内外关于土壤养分空间变异研究的发展历程和研究现状,分析了土壤养分空间变异的来源,简述了地统计学、地理信息系统(Geographic Information System, GIS)、遥感以及人工神经网络等研究方法在土壤养分空间变异研究的应用现状,并对目前研究所存在的问题进行了剖析,在此基础上对未来的研究方向进行了展望。展开更多
In the past decade, numerical modelling has been increasingly used for simulating the mechanical behaviour of naturally fractured rock masses. In this paper, we introduce new algorithms for spatial and temporal analys...In the past decade, numerical modelling has been increasingly used for simulating the mechanical behaviour of naturally fractured rock masses. In this paper, we introduce new algorithms for spatial and temporal analyses of newly generated fractures and blocks using an integrated discrete fracture network (DFN)-finite-discrete element method (FDEM) (DFN-FDEM) modelling approach. A fracture line calculator and analysis technique (i.e. discrete element method (DEM) fracture analysis, DEMFA) calculates the geometrical aspects of induced fractures using a dilation criterion. The resultant two-dimensional (2D) blocks are then identified and characterised using a graph structure. Block tracking trees allow track of newly generated blocks across timesteps and to analyse progressive breakage of these blocks into smaller blocks. Fracture statistics (number and total length of initial and induced fractures) are then related to the block forming processes to investigate damage evolution. The combination of various proposed methodologies together across various stages of modelling processes provides new insights to investigate the dependency of structure's resistance on the initial fracture configuration.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.51278217)
文摘This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.
文摘为实现能源多梯度利用并减少环境污染,气-电耦合的综合能源系统(integrated energy system,IES)逐渐占据全球能源结构的重要部分。为明晰系统运行特性并充分挖掘能源子网调度潜力,综合能源系统动态仿真研究逐渐深入。天然气网中,管道传输过程深刻影响着其动态过程。然而现有管道传输过程时域仿真算法存在着精度与效率的两难。为解决上述问题,提出一种基于时域二端口模型的综合能源系统气网动态仿真算法。首先基于特征线法,构建气网源荷节点关系矩阵,进一步给出时域二端口模型;随后参考特征线法数值格式,构建状态量空间分布矩阵,用于获取气网状态量分布,并提出基于时域二端口模型和分布矩阵的气网动态仿真算法。算例结果表明,所提出的方法具有高效率和高精度两大优势,适合于气-电耦合的综合能源系统的较长时间仿真。
基金the National Natural Science Foundation of China(No.61732007)Strategic Priority Research Program of Chinese Academy of Sciences(XDB32050200,XDC01020000).
文摘Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets.
文摘土壤养分具有变异性,其变异性特征能够反映区域生态功能和景观格局的动态变化,是土壤的重要属性之一。本文从土壤养分空间变异的研究意义出发,通过对相关文献的阅读分析,简述了国内外关于土壤养分空间变异研究的发展历程和研究现状,分析了土壤养分空间变异的来源,简述了地统计学、地理信息系统(Geographic Information System, GIS)、遥感以及人工神经网络等研究方法在土壤养分空间变异研究的应用现状,并对目前研究所存在的问题进行了剖析,在此基础上对未来的研究方向进行了展望。
文摘In the past decade, numerical modelling has been increasingly used for simulating the mechanical behaviour of naturally fractured rock masses. In this paper, we introduce new algorithms for spatial and temporal analyses of newly generated fractures and blocks using an integrated discrete fracture network (DFN)-finite-discrete element method (FDEM) (DFN-FDEM) modelling approach. A fracture line calculator and analysis technique (i.e. discrete element method (DEM) fracture analysis, DEMFA) calculates the geometrical aspects of induced fractures using a dilation criterion. The resultant two-dimensional (2D) blocks are then identified and characterised using a graph structure. Block tracking trees allow track of newly generated blocks across timesteps and to analyse progressive breakage of these blocks into smaller blocks. Fracture statistics (number and total length of initial and induced fractures) are then related to the block forming processes to investigate damage evolution. The combination of various proposed methodologies together across various stages of modelling processes provides new insights to investigate the dependency of structure's resistance on the initial fracture configuration.
