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
The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with...The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.展开更多
The armored cable used in a deep-sea remotely operated vehicle(ROV) may undergo large displacement motion when subjected to dynamic actions of ship heave motion and ocean current. A novel geometrically exact finite el...The armored cable used in a deep-sea remotely operated vehicle(ROV) may undergo large displacement motion when subjected to dynamic actions of ship heave motion and ocean current. A novel geometrically exact finite element model for two-dimensional dynamic analysis of armored cable is presented. This model accounts for the geometric nonlinearities of large displacement of the armored cable, and effects of axial load and bending stiffness. The governing equations are derived by consistent linearization and finite element discretization of the total weak form of the armored cable system, and solved by the Newmark time integration method. To make the solution procedure avoid falling into the local extreme points, a simple adaptive stepping strategy is proposed. The presented model is validated via actual measured data. Results for dynamic configurations, motion and tension of both ends of the armored cable, and resonance-zone are presented for two numerical cases, including the dynamic analysis under the case of only ship heave motion and the case of joint action of ship heave motion and ocean current. The dynamics analysis can provide important reference for the design or product selection of the armored cable in a deep-sea ROV system so as to improve the safety of its marine operation under the sea state of 4 or above.展开更多
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 the National Natural Science Foundation of China(61573017)
文摘The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.
基金Project(2008AA09Z201)supported by the National High Technology Research and Development Program of China
文摘The armored cable used in a deep-sea remotely operated vehicle(ROV) may undergo large displacement motion when subjected to dynamic actions of ship heave motion and ocean current. A novel geometrically exact finite element model for two-dimensional dynamic analysis of armored cable is presented. This model accounts for the geometric nonlinearities of large displacement of the armored cable, and effects of axial load and bending stiffness. The governing equations are derived by consistent linearization and finite element discretization of the total weak form of the armored cable system, and solved by the Newmark time integration method. To make the solution procedure avoid falling into the local extreme points, a simple adaptive stepping strategy is proposed. The presented model is validated via actual measured data. Results for dynamic configurations, motion and tension of both ends of the armored cable, and resonance-zone are presented for two numerical cases, including the dynamic analysis under the case of only ship heave motion and the case of joint action of ship heave motion and ocean current. The dynamics analysis can provide important reference for the design or product selection of the armored cable in a deep-sea ROV system so as to improve the safety of its marine operation under the sea state of 4 or above.
