Multilevel Command and Control Supernetwork Modeling Based on Attribute Synergy Prioritization

Chen, Bo; Yu, Haoyang; Wang, Yun-Ming; Gao, Xiue; Xu, Yan

doi:10.1109/access.2019.2903520

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Due to the complexity of actual social networks, we make the following assumptions on the designed model before conducting research [39][40][41][42]:…”

Section: Dr-scir Network Public Opinion Propagation Modelmentioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

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The DR-SCIR network public opinion propagation model was employed to study the characters of S-state users stopping transmitting information for the first time and secondary transmission of immune users. The model takes into account symmetry and complexity such as direct immunization and social reinforcement effect, proposes the probability of direct immunity Psr and the probability of transform from the immune state to the hesitant state Prc, and divides public opinion information into positive public opinion and negative public opinion based on whether the public opinion information is confirmed. Simulation results show that, when direct immunity Psr = 0.5, the density of I-state nodes in the model decreased by 54.12% at the peak index; when the positive social reinforcement effect factor b = 10, the density of I-state nodes in the model increased by 16.67% at the peak index; and when the negative social reinforcement effect factor b = -10, the density of I-state nodes in the model decreased by 55.36% at the peak index. It shows that increasing the positive social reinforcement effect factor b can promote the spread of positive public opinion, reducing the negative social reinforcement effect factor b can control the spread of negative public opinion, and direct immunization can effectively suppress the spread of public opinion. This model can help us better analyze the rules of public opinion on social networks, so as to maintain a healthy and harmonious network and social environment.

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“…Due to the complexity of actual social networks, we make the following assumptions on the designed model before conducting research [39][40][41][42]:…”

Section: Dr-scir Network Public Opinion Propagation Modelmentioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

Self Cite

View full text Add to dashboard Cite

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“…The super network includes task sub-network, capability sub-network and system subnetwork. Chen [23] put forward a multi-layer command and control supernetwork model including sensing subnet, command subnet, and firepower subnet. Based on the capability generation mechanism, it needs to take capability requirements into consideration to construct the ESoS, where the ESoS should include equipment, functions, and command and control structure.…”

Section: A the Equipment System-of-systems Architecturementioning

confidence: 99%

An Optimal Searching Algorithm for the Equipment System-of-Systems Architecture Space With Uncertain Capabilities

et al. 2020

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Systematic operations make modern wars more and more complicated, thus it is a challenge to design, develop, and select an excellent System-of-Systems. In this paper, we propose an equipment Systemof-Systems architecture (ESoSA) formalization framework based on the super network and an architecture space searching algorithm. First, the formal definition of ESoSA and the problem of ESoSA space searching problem framework are introduced, where the searching problem is transferred into a multi-agent dynamic programming problem. Each agent searches for the optimal architecture in its corresponding architecture space, and different architecture spaces may overlap with each other. Second, the objective of the problem is to choose several optimal architectures with maximized sampling rewards and minimized cumulative searching costs. Third, a sequential search algorithm based on decision indicators is proposed to solve the problem. Finally, the proposed algorithm is evaluated by simulation experiments, where the experimental results show that the proposed algorithm is with high quality, and outperforms other benchmark algorithms. INDEX TERMS super-network based architecture, multi-agent dynamic programming, sequential search I. INTRODUCTION T HE interconnection between weapons has become more and more diverse currently [1]. Particularly, the widespread use of unmanned systems has led to major changes in the combat methods of modern warfare [2], [3], and it is a challenging issue to study warfare. The equipment system-of-systems (ESoS) [4] is a manifestation of the SoS 1 in the field of warfare. The ESoS is such complicated, and how to study the ESoS is a problem that needs to be solved urgently. Fortunately, the architecture provides an effective way to study this problem. The architecture is the combination of component systems in the ESoS and the interaction among component systems and with the external environment [7], [8]. In systems engineering, the ESoS architecture runs through the entire process, including requirement

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“…Zhao et al [20] established a weapon equipment system super-network model based on the network-centric mode, and a granular analysis is proposed to reduce the complexity of weapon system generation schemes based on the theory of quotient space. A multi-layer command and control super-network model is proposed based on attribute collaborative prioritization and hypergraph theory, including three levels and five types of networks [21]. The above super-network models are studied from the perspectives of equipment systems, functions and the command and control structure respectively.…”

Section: B the Super-network Modelmentioning

confidence: 99%

An Optimal Approach for Combat System-of-Systems Architecture Search Under Uncertainty

et al. 2019

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The systematic and network-centric warfare becomes complicated currently. It is a challenge problem to design and choose the optimal combat system-of-systems to complete missions. In order to solve this challenge, we propose a super-network model based combat system-of-systems architecture (CSoSA) and a scheme space exploration algorithm in this paper. First, a formal definition of CSoSA is given based on the operational capability generation elements. The framework for CSoSA scheme space exploration problem is then presented. The goal of the problem is to select the optimal architecture with maximized expected rewards and minimized cumulative costs. In addition, a novel and optimal algorithm for CSoSA dynamic exploration is proposed based on the greedy search algorithm. Finally, we evaluated our algorithm through simulation experiments, where the results showed that our algorithm is significantly better than several benchmark algorithms. In general, the proposed architecture framework and the exploration algorithm can assist commanders in making further decisions. INDEX TERMSCombat system-of-systems, architecture modeling, super-network model, greedy search, dynamic planning.

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Multilevel Command and Control Supernetwork Modeling Based on Attribute Synergy Prioritization

Cited by 8 publications

References 34 publications

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

An Optimal Searching Algorithm for the Equipment System-of-Systems Architecture Space With Uncertain Capabilities

An Optimal Approach for Combat System-of-Systems Architecture Search Under Uncertainty

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