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The agile earth observation satellite scheduling problem (AEOSSP) with time-dependent transition times is a complex combinational optimization problem that has emerged from the development of large-scale satellite management techniques. To address this problem, we propose a deep reinforcement learning-based construction model (DRL-CM) that consists of five parts: 1) a Markov decision process (MDP); 2) a feature engineering; 3) a constructive heuristic neural network (CHNN); 4) an RL training method; and 5) an evaluation system. Specifically, the CHNN comprises six modules containing three special components that we propose: a dynamic encoder, a dynamic global layer, and a two-stage attention layer. First, we build the MDP of the AEOSSP and the feature engineering with effective features required for decision-making. Second, we design the CHNN to function as the MDP policy and train it with an RL model. Finally, we propose a comprehensive evaluation system for the validation of our model. The experimental results indicate that the proposed DRL-CM outperforms the state-of-the-art algorithm in terms of both optimization speed and quality. In addition, the feature engineering and network architecture built in our model are verified to be effective in comprehensive experiments.

Large-scale satellite constellations lead to a scarcity of spatial spectrum resources, especially for the overlapped spectrum between Low Earth Orbit (LEO) and Geostationary Orbit (GEO)satellites in hybrid constellations. Hence, based on game theory, a distributed spectrum-sharing method is proposed for downlink spectrum sharing in large-scale hybrid satellite constellations. Specifically, the system cycle is divided into equal-spaced topological periods, and the beams of LEO satellites are assigned to LEO ground stations (LGS) during every topological period. A system model based on game theory is also developed to describe the mutual interference of links established between LEO satellites and LGS. Subsequently, the formulated game is proven to be an exact potential game, with at least one pure strategic Nash equilibrium (NE). Along the line, to obtain the NE solution, a dynamic channel allocation algorithm is proposed based on stochastic learning theory, and the convergence is proven. Finally, the simulation results demonstrate the proposed DCASLA’s effectiveness, which can balance the convergence speed and overall network satisfaction.

Renewal-geometric process is used to describe such a non-homogeneous deteriorating process that a system will deteriorate after several consecutive repairs, not after each repair described by the geometric process. In the maintenance domain, the effect of corrective maintenance after failure is generally not repairable as new (e.g. geometrically deteriorating). Preventive maintenance is critical before a system failure, due to economic losses and security threats caused by a sudden shutdown. Therefore, this article assumes that a system is geometrically deteriorating after corrective maintenance, wherein preventive maintenances sequence in the same repair period form a renewal process since it can restore the system to the initial state of the period. Furthermore, a binary policy (N,T)is utilized to minimize the long-run average cost rate, where Nrepresents the number corrective maintenances and Tdenotes the time interval between two consecutive preventive maintenances. In particular, pseudo-age replacement model represents a special case of N=1, which is considered as a generalization of the traditional age-based replacement model. Subsequently, the optimal policy N*can be verified in theory and an asymptotic optimal policy (N*,T*)can be obtained based on a heuristic grid search. Finally, numerical examples verify the effectiveness of this proposed model and show that implementation of preventive maintenance for some repairable systems is superior to no preventive maintenance in both economic and reliability aspects.

The combat system-of-systems in high-tech informative warfare consists of different types of combat systems connected via multiple interconnections, which can be abstracted as types of complex heterogeneous networks. Research on the functional robustness of heterogeneous combat networks (HCNs) is of significant military value for optimizing operation process planning and for improving network survivability in complex electromagnetic battle fields. This study presents an integrated framework, known as the HCN functional robustness (HCNFR) framework to measure the functional robustness of HCNs under various attacking strategies. Specifically, a heterogeneous network model is introduced to characterize HCNs considering different types of functional entities and information flows. Based on this setup, the rescaled operational capability index (ROCI) is first proposed to evaluate the functional robustness of HCNs, and the criterion for functional disintegration of HCNs is established by summing the values of ROCI during the attacking process. Finally, the reliability and effectiveness of HCNFR are illustrated with an empirical case study. The results provide useful insights for operational guidance and design of a more functional and robust combat system-of-systems.

Satellite hardware has reached a level of development that enables imaging satellites to realize applications in the area of meteorology and environmental monitoring. As the requirements in terms of feasibility and the actual profit achieved by satellite applications increase, we need to comprehensively consider the actual status, constraints, unpredictable information, and complicated requirements. The management of this complex information and the allocation of satellite resources to realize image acquisition have become essential for enhancing the efficiency of satellite instrumentation. In view of this, we designed a satellite auto mission planning system, which includes two sub-systems: the imaging satellite itself and the ground base, and these systems would then collaborate to process complicated missions: the satellite mainly focuses on mission planning and functions according to actual parameters, whereas the ground base provides auxiliary information, management, and control. Based on the requirements analysis, we have devised the application scenarios, main module, and key techniques. Comparison of the simulation results of the system, confirmed the feasibility and optimization efficiency of the system framework, which also stimulates new thinking for the method of monitoring environment and design of mission planning systems.

Fifteen N-arylsulfonyl-3-propionylindoles (3a–o) were prepared and preliminarily evaluated as in vitroinhibitors of human immunodeficiency virus type-1 (HIV-1). Three compounds 3c, 3gand 3iexhibited potent anti-HIV-1 activity with effective concentration (EC50) values of 0.8, 4.0 and 1.2 μg/mL, and therapeutic index (TI) values of 11.7, 16.6 and 84.1, respectively. N-(m-Nitro)phenylsulfonyl-3-propionyl-6-methylindole (3i) exhibited the most promising and best activity against HIV-1 replication. The cytotoxicity of these compounds was assessed as well.

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment. This model can determine the weight of knowledge transferred from another enterprise or from a big data provider. Numerous simulation experiments are implemented to test the efficiency of the optimization model. Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider, the total discount expectation of profits will increase, and the transfer cost will be reduced. The calculated results are in accordance with the actual economic situation. The optimization model can provide useful decision support for enterprises in a big data environment.

An integrated technology pushing and requirement pulling model is presented to address the problem of selecting a suitable portfolio from several candidate multi-function weapon systems in the defence acquisition and manufacturing process. The optimal weapon system portfolios both satisfy the technical effects and compound capability requirements with relative acquisition and manufacturing constraints. In the first stage, multiple criteria analysis is employed to build a portfolio value hierarchal structure from two main perspectives: a five-level set of technology pushing measures and a two-level set of requirement pulling measures. The developmental maturity level of a technology, its functionality, systems, capability, and portfolio are all captured by technology pushing measures, based on an additive multiple criteria model. The requirement satisfaction level of a system or portfolio is obtained by requirement pulling measures based on linear programming optimization and the first-ignorance model, which is founded on expert opinion, using pairwise comparisons. In the second stage, by means of manufacturing cost consumption and capability requirement constraint assumptions, the dominance structure of the portfolios is designed and all the technology and requirement portfolios are generated as a set of Pareto-optimal solutions, through multi-objective integer programming. An illustrative case study is presented to validate the efficiency of the proposed model, and the computational results are further analysed and discussed.

A novel executable modeling approach is proposed for system-of-systems architecture by taking full advantage of the Department of Defense Architecture Framework (DoDAF) Meta-model (DM2) and defining the common model transformation specification at the metamodel level. This methodology provides more flexibility and adaptability for the automated construction of executable models directly from the architectural data rather than from static models. More specifically, the architectural data metamodel is first established to guide architectural data modeling of core data elements and associations in DM2 as the common and consistent data dictionary for architecture modeling, while the executable formalism metamodel is designed to formally define executable models. Then, the mapping rules between both metamodels are presented as the common transformation specification regardless of which modeling language or methodology is employed in developing architectural descriptions. Finally, eXtensible Markup Language (XML) technology, like XML schema languages and eXtensible Stylesheet Language specifications, is discussed to facilitate the automated transformation of executable models from architectural instance data. The feasibility of the proposed approach is illustrated using a published example where colored Petri net (CPN) is used as the executable formalism.

A data‐centric, capability‐focused approach is proposed to facilitate the architecture modeling and analysis of challenging system‐of‐systems (SoS). This approach abstracts essential information from the underlying complexity with the architecture modeling in a data‐centric and semantically consistent fashion, and allows early understanding and exploration of the logical, behavioral, and performance characteristics to achieve the desired capabilities. More specifically, a high‐level data meta‐model, depicting the semantic relationships of constituent architectural data elements, is first presented to guide the architectural data modeling, which is aligned well with the US Department of Defense Architecture Framework (DoDAF) Meta‐model (DM2). Then, the development of architectural descriptions and the construction of executable models are studied based on the core architectural data elements. Additionally, architecture analysis using static and executable models are discussed, including static analysis, dynamic analysis, and experimental analysis. The feasibility of the foregoing approach is demonstrated with an illustrative example. ©2013 Wiley Periodicals, Inc. Syst Eng 16:

AbstractThe multi-attribute group decision making (MAGDM) problem has received considerable attention in the decision analysis field and fruitful achievements have been reported in the literature. This paper focuses on the MAGDM in which the subjective absolute judgement on alternatives with respect to evaluating attributes are represented by fuzzy numbers. This paper employs the consensus degree to measure the agreement level of a MAGDM solution and develops a new measure degree–departure degree to evaluate how far the decision makers from their initial decision preferences. Based on these two conflicting measure degrees, the decision process of MAGDM is modelled as a multi-objective optimization problem. A decision support model (DSM) for MAGDM is proposed. The proposed DSM, incorporating five implementing phases, aims at obtaining acceptable decision solution(s) by solving the multi-objective optimization problem and conducting an interactive procedure with decision makers. In case study, this paper takes the alternative selection problem about hydroelectric project to illustrate the phases and procedure of the proposed DSM.

The multi-attribute group decision making (MAGDM) problem has received considerable attention in the decision analysis field and fruitful achievements have been reported in the literature. This paper focuses on the MAGDM in which the subjective absolute judgement on alternatives with respect to evaluating attributes are represented by fuzzy numbers. This paper employs the consensus degreeto measure the agreement level of a MAGDM solution and develops a new measure degree-departure degreeto evaluate how far the decision makers from their initial decision preferences. Based on these two conflicting measure degrees, the decision process of MAGDM is modelled as a multi-objective optimization problem. A decision support model (DSM) for MAGDM is proposed. The proposed DSM, incorporating five implementing phases, aims at obtaining acceptable decision solution(s) by solving the multi-objective optimization problem and conducting an interactive procedure with decision makers. In case study, this paper takes the alternative selection problem about hydroelectric project to illustrate the phases and procedure of the proposed DSM.