ID 原文 译文
14035 最后,基于飞机模型进行了训练仿真测试分析,通过仿真可以看出,训练所得结果可以有效实现针对来袭导弹的规避决策,所设计的奖励函数和输入参数也可以起到相应正确的作用,并且结果具备一定的泛化能力。 Finally, training simulation tests and analysis were conducted based on the aircraft model. Through simulation, it can be seen that the training results can effectively realize the evasion decision of the incoming missile, and the designed reward function and input parameters can also play a correct role, and the results have certain generalization ability.
14036 为了实时采集飞行器在风洞试验时产生的数据,并控制试验流程,提出基于Windows 7+RTX64实时扩展平台设计和开发软件控制系统的方案。 In order to collect the data generated by the aerocraft during the wind tunnel test in real time and control the test flow,a scheme based on Windows 7+RTX64 is proposed to design and develop the software system.
14037 首先设计测试流程测试RTX64的实时性,并与在相同条件、Windows下测试的结果比较,验证了RTX64能够显著增强系统的实时性。 Firstly,a flow for testing the real-time performance of RTX64 is designed and the test result is compared with that of Windows under the same conditions, verifying that RTX64 can significantly enhance the real-time performance of the system.
14038 然后详细阐述了飞行器虚拟飞行软件控制系统进程间通信和试验流程的设计,通过共享内存和事件体实现进程间数据交互和同步的目的。 Secondly,the design of Inter-Process Communication(IPC) and test flow of the software system for aerocraft's virtual flight is described in detail, and inter-process data interaction and synchronization is realized by using the shared memory and the event.
14039 最后给出测试结果与分析。 Finally,the test results and analysis are given.
14040 结果表明,在4 ms定时周期内系统能够实时采集陀螺仪、编码器和天平等传感器数据,控制飞行器的虚拟飞行试验时序,验证了系统设计方案的可行性。 The results show that the sensor data from the gyroscope,the encoder and the balance can be collected in real time and the time sequence of the virtual flight test of the aerocraft can be controlled correctly during the 4 ms period, proving the feasibility of the design.
14041 工程设备与军事装备在运行过程中由于材料磨损、工况变化等多方面原因,系统性能逐渐退化甚至失效,造成经济损失与人员伤亡。 Due to the reasons as wear of materials and change of external environment during the operation of vital systems including engineering facilities and military equipment‚the performance of the system is gradually decreasing or even fails‚which will result in economic losses and loss of life.
14042 因此,为保障系统正常运行,剩余寿命(RUL)预测技术受到研究人员的重点关注。 Therefore‚in order to ensure the normal operation of the system‚the Remaining Useful Life(RUL) prediction technology has attracted the attention of researchers.
14043 大数据时代下所获得的监测数据具有高维度、强耦合性等特点,采用传统的剩余寿命预测方法难以建模,而深度学习方法能精确建立监测数据与退化状态或寿命标签间的映射关系。 In the era of big data‚the obtained monitoring data has the characteristics of high dimensions and strong coupling‚so it is difficult to model the data by using the traditional RUL prediction method. The deep learning method can accurately establish the mapping relationship between the monitoring data and the degradation states or the life tag.
14044 详细阐述了4种典型深度学习技术在剩余寿命预测领域的研究现状,总结各类方法的优缺点,最后探讨了基于深度学习的复杂退化系统剩余寿命预测方法的未来研究方向。 This paper elaborates the research status of four typical deep learning models in the field of RUL prediction in detail‚summarizes the advantages and disadvantages of these models‚and discusses the development direction of deep learning based RUL prediction of complex degradation systems.