In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of ...In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.展开更多
随着通信技术的发展,频谱感知技术已经成为解决频谱资源稀缺的重要解决手段之一。针对传统的频谱感知方法在低信噪比(Signal to Noise Ratio,SNR)下准确率较低的问题,提出一种基于残差神经网络和注意力机制相结合的正交频分复用(Orthogo...随着通信技术的发展,频谱感知技术已经成为解决频谱资源稀缺的重要解决手段之一。针对传统的频谱感知方法在低信噪比(Signal to Noise Ratio,SNR)下准确率较低的问题,提出一种基于残差神经网络和注意力机制相结合的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)频谱感知方法。将频谱感知问题转化为图像二分类任务。通过分析OFDM信号的循环自相关特征,将其灰度处理以生成循环自相关灰度图像。利用改进后的残差神经网络进行训练,提取这些灰度图像的深层特征,使用测试数据验证所得到的频谱感知模型。仿真实验结果表明,在低SNR条件下,所提方法表现出更出色的频谱感知性能,优于传统频谱感知技术。展开更多
Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up base...Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.展开更多
在带钢冷连轧生产过程中,轧制力预测准确度直接影响产品质量。为提高轧制力预测准确度,提出了基于LSTM-JITRVM(long short term memory-just in time relevance vector machine)的轧制力模型。首先,使用循环自编码网络对输入数据进行深...在带钢冷连轧生产过程中,轧制力预测准确度直接影响产品质量。为提高轧制力预测准确度,提出了基于LSTM-JITRVM(long short term memory-just in time relevance vector machine)的轧制力模型。首先,使用循环自编码网络对输入数据进行深层次特征提取,然后使用局部离群因子算法判断测试样本与其邻域点是否属于同一分布,针对不同的分布使用不同的自学习回归模型进行拟合。仿真结果表明,该模型预测准确度可控制在3%以内,能够实现轧制力的高准确度在线预测。展开更多
The regulatory mechanisms in cellular signaling systems have been studied intensively from the viewpoint that the malfunction of the regulation is thought to be one of the substantial causes of cancer formation. On th...The regulatory mechanisms in cellular signaling systems have been studied intensively from the viewpoint that the malfunction of the regulation is thought to be one of the substantial causes of cancer formation. On the other hand, it is rather difficult to develop the theoretical framework for investigation of the regulatory mechanisms due to their complexity and nonlinearity. In this study, more general approach is proposed for elucidation of characteristics of the stability in cellular signaling systems by construction of mathematical models for a class of cellular signaling systems and stability analysis of the models over variation of the network architectures and the parameter values. The model system is formulated as regulatory network in which every node represents a phosphorylation-dephosphorylation cyclic reaction for respective constituent enzyme. The analysis is performed for all variations of the regulatory networks comprised of two nodes with multiple feedback regulation loops. It is revealed from the analysis that the regulatory networks become mono-stable, bi-stable, tri-stable, or oscillatory and that the negative mutual feedback or positive mutual feedback is favorable for multi-stability, which is augmented by a negatively regulated node with a positive auto-regulation. Furthermore, the multi-stability or the oscillation is more likely to emerge in the case of low value of the Michaelis constant than in the case of high value, implying that the condition of higher saturation levels induces stronger nonlinearity in the networks. The analysis for the parameter regions yielding the multi-stability and the oscillation clarified that the stronger regulation shifts the systems toward multi-stability.展开更多
It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has...It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph(DAG). However, steady directed cyclic graphs(DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph(S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.展开更多
文摘In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.
文摘随着通信技术的发展,频谱感知技术已经成为解决频谱资源稀缺的重要解决手段之一。针对传统的频谱感知方法在低信噪比(Signal to Noise Ratio,SNR)下准确率较低的问题,提出一种基于残差神经网络和注意力机制相结合的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)频谱感知方法。将频谱感知问题转化为图像二分类任务。通过分析OFDM信号的循环自相关特征,将其灰度处理以生成循环自相关灰度图像。利用改进后的残差神经网络进行训练,提取这些灰度图像的深层特征,使用测试数据验证所得到的频谱感知模型。仿真实验结果表明,在低SNR条件下,所提方法表现出更出色的频谱感知性能,优于传统频谱感知技术。
基金supported by the Major Program of the National Natural Science Foundation of China(Grant No.51190091)the National Natural Science Foundation of China(Grant No.51009045)the Open Research Fund Program of the State Key Laboratory of Water Resources and Hydropower Engineering Science of Wuhan University(Grant No.2012B094)
文摘Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.
文摘在带钢冷连轧生产过程中,轧制力预测准确度直接影响产品质量。为提高轧制力预测准确度,提出了基于LSTM-JITRVM(long short term memory-just in time relevance vector machine)的轧制力模型。首先,使用循环自编码网络对输入数据进行深层次特征提取,然后使用局部离群因子算法判断测试样本与其邻域点是否属于同一分布,针对不同的分布使用不同的自学习回归模型进行拟合。仿真结果表明,该模型预测准确度可控制在3%以内,能够实现轧制力的高准确度在线预测。
文摘The regulatory mechanisms in cellular signaling systems have been studied intensively from the viewpoint that the malfunction of the regulation is thought to be one of the substantial causes of cancer formation. On the other hand, it is rather difficult to develop the theoretical framework for investigation of the regulatory mechanisms due to their complexity and nonlinearity. In this study, more general approach is proposed for elucidation of characteristics of the stability in cellular signaling systems by construction of mathematical models for a class of cellular signaling systems and stability analysis of the models over variation of the network architectures and the parameter values. The model system is formulated as regulatory network in which every node represents a phosphorylation-dephosphorylation cyclic reaction for respective constituent enzyme. The analysis is performed for all variations of the regulatory networks comprised of two nodes with multiple feedback regulation loops. It is revealed from the analysis that the regulatory networks become mono-stable, bi-stable, tri-stable, or oscillatory and that the negative mutual feedback or positive mutual feedback is favorable for multi-stability, which is augmented by a negatively regulated node with a positive auto-regulation. Furthermore, the multi-stability or the oscillation is more likely to emerge in the case of low value of the Michaelis constant than in the case of high value, implying that the condition of higher saturation levels induces stronger nonlinearity in the networks. The analysis for the parameter regions yielding the multi-stability and the oscillation clarified that the stronger regulation shifts the systems toward multi-stability.
基金supported by the National Natural Science Foundation of China under Grant 71671103
文摘It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph(DAG). However, steady directed cyclic graphs(DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph(S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.
