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This paper considers the task reassignment problem for distributed multiple Unmanned Aerial Vehicle (multi-UAV) systems in dynamic environment. For a dynamic reassignment problem in a multi-UAV system, the task information may be subject to different dynamic events, and many existing task allocation algorithms require much computation and communication resource to achieve a feasible solution. Hence, this paper proposes a distributed method to cope with dynamic events that occur online during the execution of original schedules. First, a distributed framework for determining the processing strategy according to the types of dynamic events is introduced. Second, a partial reassignment algorithm (PRA) is proposed to support the framework and an incremental subteam formation mechanism and a partial releasing mechanism are developed to release the computation and communication burden. Furthermore, a modified inclusion phase to maximize assignment (MIP-MA) is also proposed in PRA to maximize the number of task allocations. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution with less data exchanges and runtime.

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

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.

Weapon system operational effectiveness evaluation is of significant importance to weapon system development, and it can be viewed as a multiple criteria decision-making problem with qualitative information, precise data, interval data, and even missing information. Furthermore, due to the complexity of weapon systems and military operations, using prior knowledge such as experiment data, simulation data, and experts’ knowledge could enhance the accuracy and reliability of the evaluation result. To this end, by introducing interval-valued evidential reasoning (ER) approach into belief rule-based system (BRBS), this paper proposed an interval-valued BRB inference method for weapon system operational effectiveness evaluation Firstly, the operational effectiveness evaluation hierarchy is established based on the analysis of the weapon system. Then, the belief rule base (BRB) is constructed to capture prior knowledge of the weapon system. Next, different kinds of information are transformed into belief distribution, and the proposed interval-valued BRB inference method is applied to relay the input to the BRB and obtain the evaluation result. Finally, three numerical examples of missile system operational effectiveness evaluation with interval data, precise data, and missing information are conducted to illustrate the process of the proposed method and demonstrate its feasibility.

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.

Rule reduction is one of the focuses of numerous researches on belief-rule-based system, in some cases, too many redundant rules may be a concern to the rule-based system. Though rule reduction methods have been widely used in the belief-rule-based system, extended belief-rule-based system, which is an expansion of belief-rule-based system, still lacks methods to reduce and train rules in the extended belief rule base (EBRB). To this end, this paper proposes an EBRB reduction and training method. Based on the density-based spatial clustering applications with noise (DBSCAN) algorithm, a new EBRB reduction method is proposed, where all the rules in the EBRB will be visited and rules within the distance of the fusion threshold will be fused. Moreover, the EBRB training method using parameter learning, which uses a set of training data to train the parameters of EBRB, is also proposed to improve the accuracy of the EBRB system. Two case studies of regression and classification are used to illustrate the feasibility and efficiency of the proposed EBRB reduction and training method. Comparison results show that the proposed method can effectively downsize the EBRB and increase the accuracy of EBRB system.

The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter (CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method. The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the third-order term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF (FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions. Keywords: Algorithm time complexity, Cubature Kalman filter, Nonlinear filtering, Robustness, Strong tracking filter

The time cooperation technology has important influence on multiple unmanned combat aerial vehicles (multi-UCAVs) cooperative attacking, which improves the operational effectiveness of UCAVs conspicuously. In this paper, a novel time cooperation method based on hybrid non-uniform adjustment is proposed to solve the time collaboration problem of multiple UCAVs. The hybrid non-uniform cooperation method construct the time coordination mode combining maneuvering coordination and speed coordination, adopting the non-uniform cruising rate along the whole route. The simulation results verify the method proposed in this paper has a larger adjustment range and higher adjustment accuracy compared with the existing time collaboration methods.

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.

Clustering is one of the most important methods to discover the intrinsic grouping in a set of unlabeled data. As ways of getting data are more various and easier, the amount of data processed is increasing exponentially and the data is more likely to be located at different clients. Traditional clustering methods cannot process the large dataset one time due to the limit of memories. In this paper, an Image Scaling Density-based Clustering (ISDC) algorithm is proposed. ISDC can process data by a client alone as well as process in parallel by several clients to deal with data located at different clients. The ISDC algorithm does not need any parameters to be designated manually. The parameters are determined by the algorithm based on the statistical features of dataset. In Parallel ISDC or PISDC, each data block located at different client is clustered alone to form intermediate clusters. By border detection algorithm, representative clusters are formed by the points that are at the edge of intermediate clusters. Then, in global clustering, representative clusters from all clients are merged by the server. The border detection algorithm reduces the communication cost between clients and the server, as well as increases the efficiency of global clustering. At last, the server feeds back the clustering information to clients to complete clustering. Our experimental results verified the effectiveness and efficiency of PISDC and ISDC.

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.

Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.

A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of process models while only increasing the computational load slightly. It is well-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading factor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as well as STSCKF, while the increase in computational load is much lower than STSCKF.

Aiming at the problem that the traditional DempsterShafer (D-S) evidence theory cannot deal with conflicted evidences effectively and correctly, this paper points out that the key issue of this problem is to measure the degree of conflict between evidences correctly after analyzing various improved methods. The existing evidence conflict measure methods are analyzed, and a new evidence conflict measure method called evidence similarity measure based on the Tanimoto measure is proposed, while a new evidence combination method is proposed on the basis of evidence similarity measure. Firstly, the conflict degrees between evidences are obtained through the evidence similarity measure. Then the evidence sources are modified based on the credibility of different evidences and the weights of conflicted parts of evidences on different focal elements are determined. Finally, the fusion result is obtained by this method. Numerical examples show that the proposed method can effectively fuse evidences when evidences are consistent or highly conflicted, and it has a fast convergence speed, a high degree of accuracy and good adaptability.