任务型教学法的任务设计

任务型教学法是新兴的一种强调“在做中学”(learning by doing)的语言教学方法，是我国外语课程教学改革的一个走向，吸收了以往多种教学法的优点而形成。任务是任务型教学法中一个重要概念。要使任务型教学法更好地发挥作用，任务的设计就显得尤为重要。在任务设计中，要注意任务与人本主义、任务与文化性、任务与教师角色之...     

MSG-3在民用航空器维修大纲制定中的应用

国内民用航空器的研发起步较晚,目前投入运行的国产航空器型号种类和数量都不多,航空器制造厂家在制定维修大纲方面的经验较少.本文对国际上通行的MSG-3维修任务分析方法进行研究,首先介绍MSG-3维修任务分析方法产生和发展的历史背景,以及其在国内民机型号中的应用;然后详细叙述MSG-3维修任务分析方法(包括航空器系统、动力装置维修任务的分析方法,结构检查和区域检查任务的分析方法,以及形成维修大纲的流程和维修任务的后期优化);最后通过对航空器型号的审查经验,总结我国民用航空器制造厂家在制定航空器维修大纲时存在的问题和改进方向.     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.     

弹道学在运载火箭总体设计中的实践与展望

简要介绍了运载火箭弹道学的主要研究内容,从弹道设计的基本方法出发,着重分析了弹道设计对于运载火箭总体设计的重要影响。在回顾我国运载火箭弹道学发展历史的同时,对我国运载火箭弹道学的特点进行了系统总结。最后结合后续我国运载火箭发展的技术需求,对弹道学的未来发展进行展望,提出了若干重点研究方向。     

对地观测DSS任务分配问题的CSP模型与算法

任务分配是对地观测分布式卫星系统(Distributed Satellites System, DSS)自主协作运行过程中的一个重要环节. 针对任务分配中的约束满足问题(Constraint Satisfaction Problem, CSP), 以DSS完成任务总耗能最少为原则, 构建了任务分配问题的CSP模型, 并引入MAS理论中的合同网协议, 给出模型的求解算法, 通过仿真算例对模型和算法进行了验证.      

民用飞机最小离地速度试飞实施方法研究

对民用飞机最小离地速度( VMU )试飞实施方法进行了研究,主要包含了条款的理解、试验准备、试验分工、模拟器工作准备、试飞技术和数据处理等内容,为民用飞机最小离地速度试飞提供指导。同时作为国内新兴的职业---试飞工程师( FTE),也对其在试飞过程中的分工和职责进行了阐述。     

无线传感器网络空中目标跟踪任务分配技术的研究

以无线传感器网络对空中飞行目标跟踪为背景，针对无线传感器网络协同技术中的任务分配问题，以降低传感器节点之间的通信能量消耗为目的，提出了一种基于弹性神经网络的任务分配算法。首先对多动态联盟多目标跟踪问题进行建模，然后依据最小能量准则，采用一种非全连接的环形结构的弹性神经网络模型，解决了多目标跟踪时的任务优化分配问题以及多个动态联盟对传感器资源竞争冲突时系统能耗增加的问题。仿真结果表明，该算法与传统的方法相比，大大降低了跟踪系统的能量消耗。     

An aggregate flow based scheduler in multi-task cooperated UAVs network

Unmanned Aerial Vehicles (UAVs) cooperative multi-task system has become the research focus in recent years. However, the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling, because they are not able to perceive the different features. In this paper, a novel cooperated UAVs network framework for multi-task scheduling is proposed. It is a three-layer network including a core layer, an aggregation layer and an execution layer, which enhances the efficiency of multi-task distribution, aggregation and transmission. Furthermore, an AggreGate Flow (AGFlow) based scheduler is dedicatedly designed to maximize the task completion rate, whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow. Simulation results demonstrate that, compared with that of state-of-the-art scheduler, the average task completion rate of AGFlow based scheduler is raised by 0.278.     

Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes

The task assignment problem of multiple heterogeneous unmanned aerial vehicles （UAVs）, concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources （e.g., weapons） onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromo- somes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.