In order to explore the quantitative method of metal magnetic memory testing（MMMT） and clarify the relationship between Hp（y）, the normal component of spontaneous stray field, and applied stress or residual stress...In order to explore the quantitative method of metal magnetic memory testing（MMMT） and clarify the relationship between Hp（y）, the normal component of spontaneous stray field, and applied stress or residual stress, the static tensile tests of 0.45%C steel sheet specimens are carried out on a servo hydraulic MTS810 machine. Hp（y） values are measured during the test process by an EMS-2003 metal magnetic memory diagnostic apparatus and a non-magnetic electric control displacement instrument. Residual stresses of some points on the surface of a specimen are measured by a Stress Tech X-Stress 3000 X-ray diffraction instrument. The results show that the same variation rules of Hp（y） value versus applied tensile stress are presented under the different conditions of load-on and load-off. However, the same rule does not exist between the Hp（y） value and residual stress. The variation of Hp（y） value reflects the history of applied tensile stress.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some m...This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some mild regularity assumptions, the pointwise strong convergence, the uniform weak consistency with convergence rates and the joint asymptotic distribution of the estimators are established. A simulation study is carried out to illustrate the performance of the proposed estimators.展开更多
基金This project is supported by National Natural Science Foundation of China (No.50235030,No.50505052).
文摘In order to explore the quantitative method of metal magnetic memory testing（MMMT） and clarify the relationship between Hp（y）, the normal component of spontaneous stray field, and applied stress or residual stress, the static tensile tests of 0.45%C steel sheet specimens are carried out on a servo hydraulic MTS810 machine. Hp（y） values are measured during the test process by an EMS-2003 metal magnetic memory diagnostic apparatus and a non-magnetic electric control displacement instrument. Residual stresses of some points on the surface of a specimen are measured by a Stress Tech X-Stress 3000 X-ray diffraction instrument. The results show that the same variation rules of Hp（y） value versus applied tensile stress are presented under the different conditions of load-on and load-off. However, the same rule does not exist between the Hp（y） value and residual stress. The variation of Hp（y） value reflects the history of applied tensile stress.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金supported by National Natural Science Foundation of China(Grant No.11171147)Qing Lan Project,Jiangsu Province,and the Cultivation Fund of the Key Scientific and Technical Innovation Project,Ministry of Education of China(Grant No.708044)
文摘This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some mild regularity assumptions, the pointwise strong convergence, the uniform weak consistency with convergence rates and the joint asymptotic distribution of the estimators are established. A simulation study is carried out to illustrate the performance of the proposed estimators.
