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In dynamic complex environments, it is inevitable for UAVs to be damaged due to their confrontational nature. The challenge to minimize the adverse effects of the damage and reallocate the mission is vital for achieving the operational goal. This paper proposes a distributed Multi-UAV mission reallocation method in the case of UAV damage based on the improved consensus-based bundle algorithm (CBBA). Firstly, a dynamic optimization model for Multi-UAV mission reallocation is established based on an improved resource update model. Secondly, a distributed damage inspection method based on the heartbeat hold mechanism is proposed for real-time monitoring of UAV conditions, which could enable the rapid response to UAV damage events. Furthermore, the CBBA is improved by introducing a timeliness parameter to adjust the bidding strategy and optimizing the mission selection strategy based on the time-order priority insertion principle to generate mission reallocation plans quickly. Through numerical examples, the results show that the proposed method can effectively reallocate Multi-UAV missions under damage events and has superior performance compared with original the CBBA, the particle swarm optimization (PSO) algorithm, and the performance impact (PI) algorithm. The proposed method has a faster solving speed, while the obtained solution has higher mission reallocation effectiveness.

As one of the most essential parts of the military operation, the ability of weapon systems to achieve the operation mission is vital to the success of a military operation. Focusing on the problem of weapon system effectiveness assessment (WSEA) under interval uncertainty, this paper proposed WSEA method that utilized the interval-valued evidential reasoning (ER) algorithm, the analytical hierarchy process (AHP), and the two-grade interval ranking method to properly deal with interval uncertainty in the assessment as well as provide reliable assessment results. Firstly, the AHP is used to determine the weight of different attributes of the weapon system based on experts’ knowledge, which could enhance the subjectiveness of the assessment result. Then, the interval-valued ER algorithm is applied to combine the assessment of different attributes under interval uncertainty and provide the overall assessment result. Finally, the two-grade interval ranking method is utilized to compare and rank the assessment results of different weapons. Case study shows that the proposed method could provide reliable and accurate results for weapon effectiveness assessment. Comparison results and sensitivity analysis results further confirm that the proposed method is not only as effective as existing methods with precise data, but also has the ability to provide reliable results under uncertainty. In conclusion, by utilizing the interval-valued ER algorithm, the AHP and the two-grade interval ranking method, the proposed method provides a novel and effective way for weapon effectiveness assessment under uncertainty, especially interval uncertainty.

Dependence assessment, which is to assess the influence of the operator's failure of a task on the failure probability of subsequent tasks, is an important part in Human reliability analysis (HRA). The technique for human error rate prediction (THERP) has been widely applied to assess the dependence in HRA. However, due to the complexity of the real world, various kinds of uncertainty could occur in dependence assessment problem, and how to properly express and deal with uncertainty especially interval uncertainty remains a pressing issue. In this article, a novel method based on the interval evidential reasoning (IER) algorithm is proposed to assess dependence in HRA under interval uncertainty. First, dependence influential factors are identified and their functional relationship is determined. Then, judgments on these factors provided by the analysts are represented using interval belief distributions. Next, the interval evidential reasoning algorithm is employed to aggregate interval belief distributions of different factors according to their functional relationship while considering the credibility of the interval belief distribution. Finally, the conditional human error probability (CHEP) is calculated based on the fused interval belief distribution, where the upper and lower values are determined by assigning belief degree to the highest and lowest grade of the corresponding grade interval, respectively. Two numerical examples demonstrate that the proposed method not only properly deals with interval uncertainty using interval belief distribution and IER algorithm, but also provides a novel and effective way for dependence assessment in HRA.

With the continuous development and application of modeling technology, civil aircraft designers urgently need to apply model-based systems engineering (MBSE) theory and methods into the field of civil aircraft development. However, the existing modeling tools, which combined with MBSE methodologies, only focus on one particular area, such as requirement modeling, functional modeling and design synthesis. This kind of low coupling design system makes it difficult to link the upstream and downstream designers in the early stage of system development. This paper introduces the MBSE development process of civil aircraft and then establishes an integrated platform composed of six work stations for civil aircraft development based on this process. Commercially available modeling tools, such as IBM Rational DOORS and enterprise architect (EA), are conducted in the platform. The development of flight control system is taken as an example to test the feasibility and efficiency of our integrated platform in logic layer. This platform can complete the activities of aircraft life cycle function analysis, requirement and model management. The loose coupling between the modules and the high cohesive nature allows different system engineers to maintain their design independence and consistency at different stages of development.

The ‘danmu’ system in the Chinese video-sharing social media platform Bilibili, in allowing comments to sync to any specific playback time, has constructed a community where separated viewers can join in the seemingly simultaneous conversations at the same time they watch videos. The participatory design of such co-presence demonstrates the political and cultural gesture of speaking out and exchanging ideas as the main orientation of the platform. Through danmu comments are the living networks connected both in the videos they are attached to, and in Bilibili as a whole. Despite the instantaneous emotions displayed in individual danmu comments, there are affective connections that shape the temporal quality of participation. The playfulness embedded in the interface design has further directed the gratification of speaking out on the platform.

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.