A rapid detection method for the surface defects of mosaic ceramic tiles

Due to its unique artistic value, mosaic ceramics are widely used in construction-related fields. To meet the artist's demand for high-quality mosaic ceramic to create artistic works, it is necessary to meet the needs for efficient screening of mosaic ceramic tiles. Different from the ordinary large-target ceramics, mosaic ceramics exhibit characteristics of small tile sizes, a variety of colors, large demand for quantities, and easy reflection on the surface. Common manual detection methods show problems of low efficiency or accuracy, easy to fatigue, and many others. To solve these problems, this paper proposes a new detection method to identify surface defects of mosaic ceramic tiles and designs a detection system platform to achieve rapid detection. The experiment proves that the detection system has a detection rate of 93.99% for small defects on the surface of mosaic ceramic tiles, and the detection time of a single mosaic ceramic tile is less than 0.06 s. The detection method can quickly and accurately screen out high-quality, defect-free mosaic ceramic tiles, which can effectively improve the quality and artistic value of mosaic ceramic art creation.     

无人机在输电线路巡检中的多种应用

无人机技术目前的快速发展使得我们国家输电线路自身的巡检能力得到了持续的提升,基于此本文在对无人机技术给予认识的情况下,在了解了输电线路巡检过程中的使用优势之后,还总结了无人机技术在当前我国线路巡检过程中的应用路径以及应用方法;之后使用案例分析的方式分析了无人机技术在当前输电线路巡检过程中的应用方法。     

A robust method for axis identification

The present paper proposes a new method for axis identification in discrete axially symmetrical geometric models. This method is based on-a-never-used-before property of the axially symmetrical surfaces for which the symmetry line of any section curve of the surface (or of a portion of it in the case of an incomplete axially symmetrical surface) always intersects the axis of symmetry of the surface. Thus the working principle of the method makes it very robust to local defectiveness, measurement noise and outliers.In order to compare it with the most cited methods presented in literature, several types of tests have been designed and performed. The robustness of those methods, on the one hand, has been evaluated by defining the Statistical Confidence Boundary at 1σ confidence level. The trueness of the method, on the other hand, has been evaluated on geometric models obtained by measuring real objects. The high robustness, which characterizes the proposed method, makes it particularly suitable for product geometric inspection where high accuracy is required.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

基于深度信念网络的列车故障音频诊断方法

铁路在交通运输行业有着举足轻重的地位，一旦列车发生故障将会导致严重的生命财产损失。由于列车发生故障的概率相对较低，因此难以捕获列车的故障样本。针对上述问题，提出了一种无监督学习的列车故障识别方法，通过检测列车音频信号来识别列车故障。该方法基于深度信念网络(DBN)，利用小波包分解提取检测信号的特征向量并将其作为DBN的输入，待网络充分训练后，由训练好的DBN识别当前列车的运行状况。现场监测实验结果表明，该方法能够在无监督的条件下有效识别列车故障，保障了列车的运行安全。     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

The World robot summit disaster robotics category – achievements of the 2018 preliminary competition

The World Robot Summit is a robot Olympics and aims to be held in a different country every four years from 2020. The concept of the Plant Disaster Prevention challenge is daily inspections, checks, and emergency response in industrial plants, and in this competition, robots must carry out these types of missions in a mock-up plant. The concept of the Tunnel Disaster Response and Recovery challenge is emergency response to tunnel disasters, and is a simulation competition whereby teams compete to show their ability to deal with disasters, by collecting information and removing debris. The Standard Disaster Robotics challenge assesses, in the form of a contest, the standard performance levels of a robot that are necessary for disaster prevention and emergency response. The World Robot Summit Preliminary Competition was held at Tokyo Big Sight in October 2018, and 36 teams participated in the Disaster Robotics Category. UGVs and UAVs contended the merits of new technology for solving complex problems, using core technologies such as mobility, sensing, recognition, performing operations, human interface, autonomous intelligence etc., as well as system integration and implementation of strategies for completing missions, gaining high-level results.     

考虑流固耦合的管道机器人冲击环焊缝过程动力学建模与分析

以管道机器人（Pipeline inspection gauge，PIG）为载体的内检测技术是保障油气管道安全运输的重要手段。针对管内高压流体作用下，管道机器人在冲击管内环焊缝过程中产生的动力学行为突变问题。建立了管道周向受限空间中基于Kelvin弹簧阻尼的管道机器人密封盘等效动力学模型，结合管道机器人本体建立了多体系管道机器人动力学模型；详细推导了管道机器人轴向振动微分方程，以及管内流体的流动方程；并使用Matlab/Simulink与Adams进行流固耦合仿真，作为重要的工艺参数之一，研究了管道机器人速度改变时，其在冲击环焊缝过程中的动力学响应情况。结果表明：所建立的密封盘及管道机器人动力学模型能够很好地表征密封盘在管道轴向、径向以及周向的力学特性；运行速度越快，管道机器人通过环焊缝引起的轴向振动越剧烈，冲击振动越明显；而垂向和俯仰振动现象随运动速度增大而显著减弱。     

基于灾度辨识的钢闸门巡视检查研究

巡视检查是对钢闸门要点部位开展定期、定点的信息收集,并对警兆信息做出快速辨识和评估的重要内容。应用灾度辨识的警兆信息评估方法,综合钢闸门巡视检查的各类要素信息,明确工程最大隶属的灾度等级(Ⅰ～Ⅴ级),可作为工程日常巡视检查决策的重要软技术手段。     

Smart optical measurement probe for autonomously detecting nano-defects on bare semiconductor wafer surface: Verification of proposed concept

We propose a new method of inspecting a surface for fine defects that combines the optical inspection method with observation of the physical behavior of a liquid. A liquid thin film on a substrate behaves as a near-field physical probe that autonomously captures nano-particulate defects. Optical observation of the interfacial behavior of the liquid thin film is used to detect minute defects. This method combines the characteristics of optical detection (i.e., detection from a remote field and simultaneous detectability on a plane) and the high sensitivity of a physical near-field probe. We examined the basic principles of the proposed method through numerical calculation and applied it in experiments to detect fine particulate defects on a silicon substrate for semiconductor manufacturing to demonstrate the validity of the basic concept of the proposed method.