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.     

嵌入式实时操作系统内核混合代码的自动化验证框架

"如何构造高可信的软件系统"已成为学术界和工业界的研究热点.操作系统内核作为软件系统的基础组件，它的安全可靠是构造高可信软件系统的重要环节.为了确保操作系统内核的安全可靠，将形式化方法引入到操作系统内核验证中，提出了一个自动化验证操作系统内核的框架.该验证框架包括：（1）分别对C语言程序和混合语言程序（C语言和汇编语言）进行验证；（2）在混合语言程序验证中，为汇编程序建立抽象模型，并将C语言程序和抽象模型粘合形成基于C语言验证工具可接收的验证模型；（3）从规范中提取性质，基于该自动验证工具，对性质完成自动验证；（4）该框架不限于特定的硬件架构.成功地运用该验证框架对两种不同硬件平台的嵌入式实时操作系统内核μC/OS-II进行了验证.结果显示：利用该框架在对两个不同的硬件平台上内核验证时，框架的可重复利用率很高，高达到88%，虽然其抽象模型需要根据不同的硬件平台进行重构.在对基于这两种平台的操作系统内核验证中，分别发现了10~12处缺陷.其中，在ARM平台上两处与硬件相关的问题被发现.实验表明，该方法对不同硬件平台的同一个操作系统分析验证具有一定的通用性.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

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.     

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。     

离心泵并联运行工况点的数值求解

建立了求解多台不同型号离心泵并联运行工况点的数学模型,给出了用数值计算中的“二分法”求解该模型的方法,并附有用Exce l计算的实例。     

油气操作成本预测方法研究

油气操作成本在油气生产中占有较大的比重，油气操作成本的高低直接影响油田开发的经济效益。为了有效地控制油田油气操作成本，根据影响油气操作的因素，将油气操作成本按其影响因素进行分类，对基本运行费和油气处理费采用回归预测的方法，通过对相关数据的收集，建立对应的数学模型进行预测，对其他各项操作成本，采用单位费用和计划工作量进行预测，最后进行汇总，得到油气操作成本，其预测结果可作为油气操作成本计划制订的依据。该方法简单、操作性强，预测效果较好，在油田可以推广使用。