YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Disturbance Observer-based Adaptive Fault-tolerant Dynamic Surface Control of Nonlinear System with Asymmetric Input Saturation

International Journal of Control, Automation and Systems - In this paper, a composite fault-tolerant control problem is studied for a class of uncertain nonlinear system with asymmetric input...     

Adaptive compensation of persistent actuator failures of nonlinear systems

In this article, adaptive compensation designs are developed for nonlinear systems with uncertainties from the system functions and persistent actuator failures of characterizations that (i) some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants and (ii) the failure pattern always switches from one to another and the switching does not stop. Such a controlled plant is described by an uncertain time-varying nonlinear system, and some robust adaptive feedback linearization based failure compensation results are studied for closed-loop system stabilization and bounded output tracking for some specific conditions. To improve the tracking performance in the presence of persistent actuator failures, a new adaptive control scheme is developed, using the failure indicator function which contains the failure pattern and failure time in the formulation. Detailed stability and tracking performance are shown. Simulation results are shown to verify the effectiveness of the proposed adaptive actuator failure compensation method.     

基于Vega Prime的飞行复现仿真系统设计

将视景仿真技术应用于飞机的飞行复现仿真分析,为飞行员提供了飞行训练时的真实世界景象和场景的渲染效果,对于提高飞行训练效果具有重要的作用。文章基于Vega Prime开展飞行训练视景仿真系统研究,对视景仿真系统中的关键技术开展了研究,实现了飞行过程的复现、作战轰炸过程的再现,并对对地攻击结果给出评估。     

一种能量有效的WSN目标跟踪动态协同自组织算法

针对无线传感器网络目标跟踪应用中跟踪精度与网络能耗的权衡问题,提出一种能量有效的动态协同自组织算法(E-DCS)。根据目标预测位置和节点的位置、能量信息,建立了信息效用、通信开销和节点剩余能量的综合性能指标,并利用层次分析法确定了性能指标中各要素的权值系数。通过自适应动态成簇策略,分别设定簇首切换精度阈值和节点选择精度阈值判断是否切换簇首和选择任务节点。簇首节点根据簇内节点提供的测量信息采用序贯EKF进行状态估计。仿真结果表明,与信息驱动传感器查询(IDSQ)和自适应动态协同自组织算法(A-DCS)相比,该算法在保证跟踪精度的基础上,降低了网络能耗,有效延长了网络的生命周期。     

基于几何解析的双雷达地面目标定位算法

传统的双雷达地面目标定位算法因测量数据本身固有的误差和量测噪声,使得基于非线性估计定位算法的定位精度不高。本文研究了一种新的双雷达地面目标定位算法,该算法将已知雷达探测数据以几何方式加以表示,通过几何解析对地面目标进行定位。此外,还详细分析了各种因素在不同情况下对定位精度的影响。所研究的算法具有计算量小、远距离定位精度高等优点。最后,通过仿真验证了该算法能够提高远距离定位的定位精度。     

复杂系统维护策略最新研究进展: 从视情维护到预测性维护

对于复杂、可修复的工程系统, 设备维护是确保系统安全性、可靠性、可用性的重要手段之一. 系统维护策略已经历修复性维护、定时维护、视情维护等多种维护策略. 其中, 视情维护是目前最受关注的维护策略, 它通过收集和评估系统的实时状态信息进行维护决策, 具有全寿命周期内系统可靠性高、运营维护成本低等优点. 近年来, 随着物联网技术、信息技术和人工智能的快速发展, 一种更新颖的视情维护策略——预测性维护逐渐成为领域研究热点. 本文首先简要回顾了系统维护策略的发展历程; 然后, 重点介绍了视情维护的研究进展, 根据决策支持技术的不同, 将视情维护划分为基于随机退化模型的视情维护和基于数据驱动的预测性维护, 对每类技术的发展分支与研究现状进行了疏理、分析和总结; 最后, 探讨了当前复杂系统维护策略面临的挑战性问题和可能的未来研究方向.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

Accurate positioning of a drilling and riveting cell for aircraft assembly

Modern aircraft assembly demands assembly cells or machines with higher machining efficiency and accuracy. Thus, a dual-machine drilling and riveting cell is developed in this paper. We firstly discuss its physical design, as well as the automatic drilling and riveting process. With the automatic drilling and riveting cell, drilling and riveting production line of aircraft panels can be expected. The frame chain of the drilling and riveting cell is constructed to link the assembly cell to its task space, which is the kinematics base. System calibrations, including task space calibration, the sensor calibration of an orientation alignment unit, the floating calibration of the implicit hand-eye relationship, are explored. For high positioning accuracy, a multi-sensor servoing method is proposed for cell positioning. An orientation-based laser servoing strategy, which uses the feedback of the orientation errors measured by laser displacement sensors, is used to align drilling direction and camera shooting direction. Besides, A single-camera-based visual servoing is applied to align the tool center point (TCP) to reference holes, to obtain their coordinates for drilling position modification. Experiments of multi-sensor servoing for cell positioning are performed on an automatic drilling and riveting machine developed for the panel assembly of an aircraft in China. With the cell positioning method, the automatic drilling and riveting cell can approximately achieve an accuracy of 0.05 mm, which can adequately fulfill the requirement for the assembly of the aircraft.