交通运输专业《自动控制原理》课程教学改革研究

《自动控制原理》是交通运输专业的一门重要课程,在教学过程中发现存在学生基础知识薄弱、教学内容多而学时少、传统教学方法效率不高等问题。针对这些问题,本文提出了相应的教学改革措施,包括教学内容的改革、教学方法与手段的改革、实践教学方法的改革等。采用将板书教学、多媒体课件及Matlab软件演示相结合的方式进行教学,以提高教学质量,适应应用型人才培养的要求。     

低温贮箱用复合材料的研究进展

系统地概括了低温贮箱用复合材料的研究背景、进展动态及研究现状,重点介绍了复合材料低温贮箱成型工艺、复合材料与低温推进剂之间的安全性及复合材料自身的低温力学性能.研究表明:自动铺丝成型工艺推动了无内衬的复合材料低温贮箱的发展;在冷热循环作用下界面等薄弱区域萌生大量微裂纹,机械载荷促进下形成低温推进剂泄漏路径;开发满足液氧...     

一种基于MATLAB快速开发跨平台算法软件的方法

利用MATLAB进行算法的研究、仿真和实现，已经成为科研工作者的重要手段之一。在MATLAB上开发算法便捷、高效，但无法直接应用在其他平台，此时需要软件人员编码转换进行二次开发。如果某个算法同时应用在Windows和Linux这两个系统中，软件人员需要开发两个不同的软件，它们功能相同只是代码不同。这不仅增加了软件开发的工作量，同时也不利于后期维护。针对上述问题，提出了一种基于MATLAB快速开发跨平台算法软件的方法。首先，利用MATLAB开发算法的便捷性进行算法的调试与验证，简化接口函数；其次，使用MATLAB Coder生成独立于MATLAB的C/C++代码；然后，将生成的代码在不同平台下进行封装，生成该目标平台的动态链接库；最后，不同平台通过调用该平台下的动态链接库，实现了同一算法的跨平台软件开发。所提出的算法软件跨平台开发方法，已成功应用于多个实际项目。通过本方法，不仅缩短了软件的开发周期，提高了软件编程效率，减少了软件开发中的人为错误，同时方便了软件的后期维护。     

液体火箭发动机推力自动校准系统仿真

针对液体火箭发动机原位校准高准确度的要求,介绍了一种以伺服电机为执行部件,基于液压传动力值自动加载装置,阐述了该系统的结构和工作原理。建立系统数学模型,通过Matlab和AMEsim搭建了系统的仿真模型,在分析影响模拟推力加载准确度和稳定性的基础上,设计了模糊PID控制器。仿真结果表明:所设计的力值加载系统能够完成力值精确加载,(0~6)kN力值加载时间小于30 s,加载力的示值最大误差为±0.05%,满足校准力值加载需求。证明了在力值加载系统中引入模糊PID控制算法,可以有效补偿系统中的不确定性扰动,增强系统的鲁棒性。     

Minimizing airfoil drag at low angles of attack with DBD-based turbulent drag reduction methods

Dielectric Barrier Discharge (DBD) based turbulent drag reduction methods are used to reduce the total drag on a NACA 0012 airfoil at low angels of attack. The interaction of DBD with turbulent boundary layer was investigated, based on which the drag reduction experiments were conducted. The results show that unidirectional steady discharge is more effective than oscillating discharge in terms of drag reduction, while steady impinging discharge fails to finish the mission (i.e. drag increase). In the best scenario, a maximum relative drag reduction as high as 64 % is achieved at the freestream velocity of 5 m/s, and a drag reduction of 13.7 % keeps existing at the freestream velocity of 20 m/s. For unidirectional discharge, the jet velocity ratio and the dimensionless actuator spacing are the two key parameters affecting the effectiveness. The drag reduction magnitude varies inversely with the dimensionless spacing, and a threshold value of the dimensionless actuator spacing of 540 (approximately five times of the low-speed streak spacing) exists, above which the drag increases. When the jet velocity ratio smaller than 0.05, marginal drag variation is observed. In contrast, when the jet velocity ratio larger than 0.05, the experimental data bifurcates, one into the drag increase zone and the other into the drag reduction zone, depending on the value of dimensionless actuator spacing. In both zones, the drag variation magnitude increases with the jet velocity ratio. The total drag reduction can be divided into the reduction in pressure drag and turbulent friction drag, as well as the increase in friction drag brought by transition promotion. The reduction in turbulent friction drag plays an important role in the total drag reduction.     

IPL：运载火箭测试数据自动判读语言

为确保运载火箭测试数据的完整性与正确性，克服传统人工判读模式判读效率低、易误判漏判以及通用编程语言自动判读模式判读适应性、扩展性、复用性差的不足，提出并设计运载火箭测试数据处理领域的自动判读语言IPL（Interpretation Language），以及配套的语言解析与执行器，实现了运载火箭测试判据的形式化定义与描述，减轻了业务人员的学习、判读成本，使得海量测试数据的判读在分钟级即可全部自动完成，目前已在多种新一代运载火箭工程实践中得到成功应用。     

基于TestCenter软件平台自动测试系统设计

随着通信技术的快速发展，目前市场上无线电测试类仪器保有量快速扩充，测试任务逐年增长。面对快速增长业务，传统的人工手动测试方式已不能满足市场需求。针对以上问题，采用TestCenter软件平台，结合已有计量标准设备，适当增加接口及智能开关设备，设计了一套通用自动测试系统。经验证，该自动测试系统操作简单，运行可靠，测试速度快。     

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

MC-LRF based pose measurement system for shipborne aircraft automatic landing

Due to the portability and anti-interference ability, vision-based shipborne aircraft automatic landing systems have attracted the attention of researchers. In this paper, a Monocular Camera and Laser Range Finder (MC-LRF)-based pose measurement system is designed for shipborne aircraft automatic landing. First, the system represents the target ship using a set of sparse landmarks, and a two-stage model is adopted to detect landmarks on the target ship. The rough 6D pose is measured by solving a Perspective-n-Point problem. Then, once the rough pose is measured, a region-based pose refinement is used to continuously track the 6D pose in the subsequent image sequences. To address the low accuracy of monocular pose measurement in the depth direction, the designed system adopts a laser range finder to obtain an accurate range value. The measured rough pose is iteratively optimized using the accurate range measurement. Experimental results on synthetic and real images show that the system achieves robust and precise pose measurement of the target ship during automatic landing. The measurement means error is within 0.4° in rotation, and 0.2% in translation, meeting the requirements for automatic fixed-wing aircraft landing.Received 5 July 2022; revised 19 August 2022; accepted 27 September 2022.