The lattice Boltzmann method （LBM） is coupled with the multiple-relaxation- time （MRT） collision model and the three-dimensional 19-discrete-velocity （D3Q19） model to resolve intermittent behaviors on small scal...The lattice Boltzmann method （LBM） is coupled with the multiple-relaxation- time （MRT） collision model and the three-dimensional 19-discrete-velocity （D3Q19） model to resolve intermittent behaviors on small scales in isotropic turbulent flows. The high- order scaling exponents of the velocity structure functions, the probability distribution functions of Lagrangian accelerations, and the local energy dissipation rates are investi- gated. The self-similarity of the space-time velocity structure functions is explored using the extended self-similarity （ESS） method, which was originally developed for velocity spatial structure functions. The scaling exponents of spatial structure functions at up to ten orders are consistent with the experimental measurements and theoretical results, implying that the LBM can accurately resolve the intermittent behaviors. This valida~ tion provides a solid basis for using the LBM to study more complex processes that are sensitive to small scales in turbulent flows, such as the relative dispersion of pollutants and mesoscale structures of preferential concentration of heavy particles suspended in turbulent flows.展开更多
Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matri...Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.展开更多
A new response-spectrum mode superposition method, entirely in real value form, is developed to analyze the maximum structural response under earthquake ground motion for generally damped linear systems with repeated ...A new response-spectrum mode superposition method, entirely in real value form, is developed to analyze the maximum structural response under earthquake ground motion for generally damped linear systems with repeated eigenvalues and defective eigenvectors. This algorithm has clear physical concepts and is similar to the complex complete quadratic combination （CCQC） method previously established. Since it can consider the effect of repeated eigenvalues, it is called the CCQC-R method, in which the correlation coefficients of high-order modal responses are enclosed in addition to the correlation coefficients in the normal CCQC method. As a result, the formulas for calculating the correlation coefficients of high-order modal responses are deduced in this study, including displacement, velocity and velocity-displacement correlation coefficients. Furthermore, the relationship between high-order displacement and velocity covariance is derived to make the CCQC-R algorithm only relevant to the high-order displacement response spectrum. Finally, a practical step-by-step integration procedure for calculating high-order displacement response spectrum is obtained by changing the earthquake ground motion input, which is evaluated by comparing it to the theory solution under the sine-wave input. The method derived here is suitable for generally linear systems with classical or non-classical damping.展开更多
Automatic modulation classification(AMC)is an essential technique in both civil and military applications.While deep learning has surpassed traditional methods in accuracy,distinguishing high-order modulations remain ...Automatic modulation classification(AMC)is an essential technique in both civil and military applications.While deep learning has surpassed traditional methods in accuracy,distinguishing high-order modulations remain challenging.Current efforts prioritize complex network designs,neglecting the integration of deep features and tailored feature engineering to reslove high-order ambiguities.Therefore,a multi-feature extraction framework is proposed,which directly concatenates the deep feature extracted by a newly designed lightweight neural network and the proposed spectrum secondary features or de-noised high-order statistical features.The proposed features and lightweight network both demonstrate superior overall accuracy than other competing features or networks.Furthermore,the effectiveness of the feature extraction framework is also validated.The average classification accuracy on high-order modulation sets reaches 67.39% on the RadioML2018.01A dataset,increasing more than 2%compared with the other competitive networks under the framework.The results indicate the effectiveness of the proposed feature extraction framework for its representational ability by combing the deep features with the proposed domain features.展开更多
数据是智能电网建设的战略资源乃至主要驱动力。如何处理智能电网中呈现海量、多样、实时、真实等4个特征的4 Vs数据,并从中提取信息,是电力系统大数据建设所面临的核心问题。描述了大数据的特征和引入了随机矩阵理论作为基础,以及提出...数据是智能电网建设的战略资源乃至主要驱动力。如何处理智能电网中呈现海量、多样、实时、真实等4个特征的4 Vs数据,并从中提取信息,是电力系统大数据建设所面临的核心问题。描述了大数据的特征和引入了随机矩阵理论作为基础,以及提出电力系统大数据的应用思路和架构。具体电力应用方面,介绍了所开发的早期事件发现、事件诊断和定位、相关性分析、可视化3D Power Map辅助展示等一系列功能。在此基础上,建立起以随机矩阵为理论基础,以数据为主要驱动力的电力系统认知体系框架,并探讨其与传统经典认知方案的区别。进一步设计案例考查了其对坏数据的鲁棒能力,其结果表明,随机矩阵理论这种工具可以有效地处理电网中的复杂数据,具有很好的学术研究意义和工程应用价值。另通过仿真算例验证了随机矩阵方案对数据异步的鲁棒性。展开更多
基金Project supported by the Science Challenge Program(No.TZ2016001)the National Natural Science Foundation of China(Nos.11472277,11572331,11232011,and 11772337)+2 种基金the Strategic Priority Research Program,Chinese Academy of Sciences(CAS)(No.XDB22040104)the Key Research Program of Frontier Sciences,CAS(No.QYZDJ-SSW-SYS002)the National Basic Research Program of China(973 Program)(No.2013CB834100)
文摘The lattice Boltzmann method （LBM） is coupled with the multiple-relaxation- time （MRT） collision model and the three-dimensional 19-discrete-velocity （D3Q19） model to resolve intermittent behaviors on small scales in isotropic turbulent flows. The high- order scaling exponents of the velocity structure functions, the probability distribution functions of Lagrangian accelerations, and the local energy dissipation rates are investi- gated. The self-similarity of the space-time velocity structure functions is explored using the extended self-similarity （ESS） method, which was originally developed for velocity spatial structure functions. The scaling exponents of spatial structure functions at up to ten orders are consistent with the experimental measurements and theoretical results, implying that the LBM can accurately resolve the intermittent behaviors. This valida~ tion provides a solid basis for using the LBM to study more complex processes that are sensitive to small scales in turbulent flows, such as the relative dispersion of pollutants and mesoscale structures of preferential concentration of heavy particles suspended in turbulent flows.
基金supported by the Flood Control Foundation of Yellow River Conservancy Commissionthe 2007 Key Supporting Project on Undergraduate Graduation Thesis of North-western Polytechnical University.
文摘Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.
基金Natural Science Foundation of China under Grant Nos.51478440 and 51108429National Key Technology R&D Program under Grant No.2012BAK15B01
文摘A new response-spectrum mode superposition method, entirely in real value form, is developed to analyze the maximum structural response under earthquake ground motion for generally damped linear systems with repeated eigenvalues and defective eigenvectors. This algorithm has clear physical concepts and is similar to the complex complete quadratic combination （CCQC） method previously established. Since it can consider the effect of repeated eigenvalues, it is called the CCQC-R method, in which the correlation coefficients of high-order modal responses are enclosed in addition to the correlation coefficients in the normal CCQC method. As a result, the formulas for calculating the correlation coefficients of high-order modal responses are deduced in this study, including displacement, velocity and velocity-displacement correlation coefficients. Furthermore, the relationship between high-order displacement and velocity covariance is derived to make the CCQC-R algorithm only relevant to the high-order displacement response spectrum. Finally, a practical step-by-step integration procedure for calculating high-order displacement response spectrum is obtained by changing the earthquake ground motion input, which is evaluated by comparing it to the theory solution under the sine-wave input. The method derived here is suitable for generally linear systems with classical or non-classical damping.
基金supported by the National Natural Science Foundation of China(12273054).
文摘Automatic modulation classification(AMC)is an essential technique in both civil and military applications.While deep learning has surpassed traditional methods in accuracy,distinguishing high-order modulations remain challenging.Current efforts prioritize complex network designs,neglecting the integration of deep features and tailored feature engineering to reslove high-order ambiguities.Therefore,a multi-feature extraction framework is proposed,which directly concatenates the deep feature extracted by a newly designed lightweight neural network and the proposed spectrum secondary features or de-noised high-order statistical features.The proposed features and lightweight network both demonstrate superior overall accuracy than other competing features or networks.Furthermore,the effectiveness of the feature extraction framework is also validated.The average classification accuracy on high-order modulation sets reaches 67.39% on the RadioML2018.01A dataset,increasing more than 2%compared with the other competitive networks under the framework.The results indicate the effectiveness of the proposed feature extraction framework for its representational ability by combing the deep features with the proposed domain features.
文摘数据是智能电网建设的战略资源乃至主要驱动力。如何处理智能电网中呈现海量、多样、实时、真实等4个特征的4 Vs数据,并从中提取信息,是电力系统大数据建设所面临的核心问题。描述了大数据的特征和引入了随机矩阵理论作为基础,以及提出电力系统大数据的应用思路和架构。具体电力应用方面,介绍了所开发的早期事件发现、事件诊断和定位、相关性分析、可视化3D Power Map辅助展示等一系列功能。在此基础上,建立起以随机矩阵为理论基础,以数据为主要驱动力的电力系统认知体系框架,并探讨其与传统经典认知方案的区别。进一步设计案例考查了其对坏数据的鲁棒能力,其结果表明,随机矩阵理论这种工具可以有效地处理电网中的复杂数据,具有很好的学术研究意义和工程应用价值。另通过仿真算例验证了随机矩阵方案对数据异步的鲁棒性。
