交通流特性参数的识别与测量

提出一种用于交通流管理控制系统中交通流特性参数自动检测的新办法 ,介绍该方法的基本原理、识别模型及据此方法研制成功的在线实时智能检测系统的实验结果。这个系统较好地应用了模式识别技术 ,故不仅能自动区分机动车与非机动车 ,判别车型 ,准确地检测出机动车流特性参数 ,而且能自动准确地检测出非机动车 (自行车 )群体流量。此系统样机现场连续运行 3年多 ,已获满意结果     

一种星模式识别算法的稳健性研究

研究了一种基于奇异值分解的星模式识别算法的稳健性。首先简述了研究星模式识别算法的必要性和现有的星模式识别算法。从数学原理上证明了本文算法进行模式识别所用的矩阵的奇异值相对于坐标变换是不变的。从物理意义上给出了稳健的解释。仿真试验选取了四种有代表性的情况进行了研究。结果证实该算法所用的特征不变量是稳健的。结论部分分析了该算法的优缺点。最后给出了待研究的问题。     

基于“笔”输入模式的产品结构草图快速设计系统研究

为了减少设计人员从想法到设计实现之间的障碍,本文提出一种新的设计途径——"直接手绘"的方式来支撑产品结构草图设计过程,使得进行产品开发的功能结构、材料工艺、设计开发过程、制造模式等都发生了深远的变化。首先给出快速设计系统的总体方案,然后重点剖析阐述快速设计系统的软件设计,最后以算例形式验证了基于"笔"输入的三维航天产品结构草图快速设计方法及系统。     

外物损伤对不锈钢模拟叶片疲劳强度的影响研究

本文开展了1Cr15Ni4Mo3N不锈钢模拟真实前缘叶片的外物损伤(FOD)与高周疲劳(HCF)试验,旨在研究不锈钢叶片在承受外物损伤后的剩余疲劳强度。使用空气炮法对模拟叶片进行了FOD试验,结果表明损伤主要可分为半圆型、V型和撕裂型三类,且损伤深度随着钢珠直径、入射速度的增大而增大。基于步进法开展了FOD试样的HCF试验,试验结果表明FOD试样的振幅疲劳强度下降了70%以上,且随着损伤深度、入射速度的上升表现出明显的下降趋势。不同缺口类型之间存在差异,半圆型缺口疲劳强度较高,V型缺口稍低,撕裂型缺口最低。使用SEM观测了FOD缺口及断口微观特征,疲劳裂纹源区均位于缺口根部表面附近,说明高速冲击造成的材料丢失、剪切带与剪切韧窝等微观特征促使了疲劳裂纹的萌生。     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.     

GroupNet: Learning to group corner for object detection in remote sensing imagery

Due to the attractive potential in avoiding the elaborate definition of anchor attributes,anchor-free-based deep learning approaches are promising for object detection in remote sensing imagery. Corner Net is one of the most representative methods in anchor-free-based deep learning approaches. However, it can be observed distinctly from the visual inspection that the Corner Net is limited in grouping keypoints, which significantly impacts the detection performance. To address the above problem, ...     

在轨高效目标检测加速技术

针对深度卷积网络目标检测算法参数量大、计算量大以及受星上计算资源、存储资源及功耗的限制，难以实现在轨部署的问题，提出了一种在轨高效目标检测算法加速框架与实现方法。首先，设计了一种可以同时兼容三种卷积算子的计算引擎，有效提高了资源利用率;其次，从通道和卷积核两个维度将目标检测算法模型展开，实现了加速器的高度并行化和可扩展性;最后，在多种FPGA平台上实现了该加速器并对其性能进行了评估。实验结果表明：所提出的加速器计算性能可以达到1843.2 GFLOPs(每秒千兆次浮点运算)，推理时间为0.22 ms。与同类加速器方案相比，所提出的加速器框架在性能、功耗、能效比及推理时间方面具有很大优势，适合部署在资源受限环境中，具有良好的星上应用前景和价值。     

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.     

Target localization method of non-cooperative spacecraft on on-orbit service

With the explosion of the number of meteoroid/orbital debris in terrestrial space in recent years, the detection environment of spacecraft becomes more complex. This phenomenon causes most current detection methods based on machine learning intractable to break through the two difficulties of solving scale transformation problem of the targets in image and accelerating detection rate of high-resolution images. To overcome the two challenges, we propose a novel non-cooperative target detection method using the framework of deep convolutional neural network.Firstly, a specific spacecraft simulation dataset using over one thousand images to train and test our detection model is built. The deep separable convolution structure is applied and combined with the residual network module to improve the network’s backbone. To count the different shapes of the spacecrafts in the dataset, a particular prior-box generation method based on K-means cluster algorithm is designed for each detection head with different scales. Finally, a comprehensive loss function is presented considering category confidence, box parameters, as well as box confidence. The experimental results verify that the proposed method has strong robustness against varying degrees of luminance change, and can suppress the interference caused by Gaussian noise and background complexity. The mean accuracy precision of our proposed method reaches 93.28%, and the global loss value is 13.252. The comparative experiment results show that under the same epoch and batchsize, the speed of our method is compressed by about 20% in comparison of YOLOv3, the detection accuracy is increased by about 12%, and the size of the model is reduced by nearly 50%.     

SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network, which can achieve good performance in terms of general object detectors. However, in the field of remote sensing image object detection, as pretrained models are significantly different from remote sensing data, it is meaningful to explore a train-from-scratch technique for remote sensing images. This paper proposes an object detection framework trained from scratch, SRS-Net, and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution module. Then, two necessary improvement principles are proposed: studying the role of normalization in the network structure, and improving data augmentation methods for remote sensing images. To evaluate the proposed framework, we performed many ablation experiments on the DIOR, DOTA, and AS datasets. The results show that whether using the improved backbone network, the normalization method or training data enhancement strategy, the performance of the object detection network trained from scratch increased. These principles compensate for the lack of pretrained models. Furthermore, we found that SRS-Net could achieve similar to or slightly better performance than baseline methods, and surpassed most advanced general detectors.