录音机外语学习附加器的设计

本文介绍了录音机外语学习附加器的设计思想、工作原理以及独特的语音存储和各种重放功能。     

基于实测数据回放的ROV导航定位系统研究

针对ROV对国产导航系统的需求,研发了包含导航融合终端软件及光纤罗经在内的ROV导航定位系统工程样机。该系统由嵌入式运行模式、仿真运行模式和数据分析模块三部分组成,能够完整记录ROV工作环境数据信息并进行时标化存储,生成实测数据文件,进而通过精准复现ROV工作环境数据,为ROV导航算法的调试提供便捷、一致的试验数据和高效的数据分析手段。海试验证结果表明,系统有效复现了水下试验数据,通过数据回放仿真高效完成了导航算法参数调试,对USBL噪声实现80%抑制,并能够有效应对USBL输出异常情况,可有效辅助ROV水下调查及安装等工程作业。     

PVM并行调试器中检查点机制的实现方法

介绍了检查点在调试半行程序中常用的实现方法，提出了一种新的断点式一致性的检查点设置方案，并分析了它在ＰＶＭ环境中的实现策略，旨在提供一种快速，方便的调试ＰＶＭ并行程序的手段。     

焊接电弧波形和图像的同步采集与再现

进行了焊接电弧高速摄像图像和信号波形的同步采集和再现研究,给出了电弧图像和波形再现具体实现技术,并利用该技术成功地进行了电弧光谱信息检测熔滴过渡过程、二氧化碳短路过渡过程和变极性MIG焊电弧行为的研究。     

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

As an advanced combat weapon, Unmanned Aerial Vehicles (UAVs) have been widely used in military wars. In this paper, we formulated the Autonomous Navigation Control (ANC) problem of UAVs as a Markov Decision Process (MDP) and proposed a novel Deep Reinforcement Learning (DRL) method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments. To solve the problem of limited training experience, the proposed Imaginary Filtered Hindsight Experience Replay (IFHER) generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences. The well-designed goal, episode, and quality filtering strategies ensure that only high-quality augmented experiences can be stored, while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities. By training in a complex environment constructed based on the parameters of a real UAV, the proposed IFHER algorithm improves the convergence speed by 28.99% and the convergence result by 11.57% compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent. Moreover, the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.