A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

On-line quality inspection in laser blank welding using ART2 neural network

Laser blank welding is becoming more and more important in the automotive industry and the quality of the weld is critical for a successful application. A fully automated solution is required to inspect the quality of the blanks. This paper presents a vision inspection system with a CMOS camera which uses ART2 network to inspect the defects on-line to obtain the geometry and the quality of the weld seam. The neural network ART2 has the capability of self-learning fiom the environment. It can distinguish the defects that have been learned before and give new outputs for new defects. So ART2 network is suitable for weld quality inspection in laser blank welding. Additionally, a CO2 laser is used for the blank butt-welding.

Concrete Defects Inspection and 3D Mapping Using City Flyer Quadrotor Robot

The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.

Defect inspection technologies for additive manufacturing

Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity,internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.

Wafer bin map inspection based on DenseNet

Wafer bin map(WBM)inspection is a critical approach for evaluating the semiconductor manufacturing process.An excellent inspection algorithm can improve the production efficiency and yield.This paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model,the structure and training loss function are improved according to the characteristics of the WBM.In addition,a constrained mean filtering algorithm is proposed to filter the noise grains.In model prediction,an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model decision.The experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect patterns.Analyzing the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

Automatic Fabric Defects Inspection Machine

The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on eye inspection.Famously,human visual assessment is drawn-out,tiring,and an exhausting errand,including perception,consideration and experience to recognize the fault occurrence.The precision of human visual assessment declines with dull positions and vast schedules.Some of the time slow,costly,and sporadic review is the outcome.In this manner,the programmed automatic visual review safeguards both the fabric quality inspector and the quality.This examination has exhibited that Textile Defect Recognition System is fit for distinguishing fabrics’imperfections with endorsed exactness with viability.With some products 100%inspection is important to ensure the stipulated quality or standard.The classifications for the automated fabric inspection approaches are expanding as the work is vast and complex.According to the algorithm used,the texture analysis problem is classified into different approaches.They are Structural,spectral,model-based methods,Unfortunately,the optimal plan does not yet exist for these vast numbers of applied methods,as each of them has some advantages and disadvantages.

Rail fastener defect inspection method for multi railways based on machine vision

Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

Network traffic classification:Techniques,datasets,and challenges

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

Fabric Defect Detection Technique Based on Two-double Neural Network

This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.

Edge-Computing with Graph Computation:A Novel Mechanism to Handle Network Intrusion and Address Spoofing in SDN

Software Defined Networking(SDN)being an emerging network control model is widely recognized as a control and management platform.This model provides efficient techniques to control and manage the enterprise network.Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller.This data processing at the edge nodes reduces the latency and bandwidth requirements.In SDN,the controller is a single point of failure.Several security issues related to the traditional network can be solved by using SDN central management and control.Address Spoofing and Network Intrusion are the most common attacks.These attacks severely degrade performance and security.We propose an edge computing-based mechanism that automatically detects and mitigates those attacks.In this mechanism,an edge system gets the network topology from the controller and the Address Resolution Protocol(ARP)traffic is directed to it for further analysis.As such,the controller is saved from unnecessary processing related to addressing translation.We propose a graph computation based method to identify the location of an attacker or intruder by implementing a graph difference method.By using the correct location information,the exact attacker or intruder is blocked,while the legitimate users get access to the network resources.The proposed mechanism is evaluated in a Mininet simulator and a POX controller.The results show that it improves system performance in terms of attack mitigation time,attack detection time,and bandwidth requirements.

<i>Inmap-t</i>: Leveraging TTCN-3 to Test the Security Impact of Intra Network Elements

This paper rejuvenates the notion of conformance testing in order to assess the security of networks. It leverages the Testing and Test Control Notation Version 3 (TTCN-3) by applying it to a redefined notion of System under Test (SUT). Instead of testing, as it is classically done, a software/firmware/ hardware element, an intangible object, namely the network, is tested in order to infer some of its security properties. After a brief introduction of TTCN-3 and Titan, its compilation and execution environment, a couple of use cases are provided to illustrate the feasibility of the approach. The pros and cons of using TTCN-3 to implement a scalable and flexible network testing environment are discussed.

Defects Detection of TFT Lines of Flat Panel Displays Using an Evolutionary Optimized Recurrent Neural Network

This paper proposes an evolutionary optimized recurrent neural network for inspection of open/short defects on thin film transistor (TFT) lines of flat panel displays (FPD). The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor based non-contact sensor through scanning over TFT lines on the surface of mother glass of FPD. Irregular patterns on the waveform, sudden deep falls (open circuits) or sharp rises (short circuits), are classified and detected by employing the optimized recurrent neural network. The topology parameters of the recurrent neural network are optimized by a multiobjective evolutionary optimization process using a selected training data set. This method is an extension to our previous work, which utilized a feed-forward neural network, to address the drawbacks in it. Experimental results show that this method can detect defects on more realistic and noisy data than both of the previous method and the conventional threshold based method.

基于深度卷积神经网络算法和先验知识构建冠心病患者大鱼际望诊模型的思路与方法

基于全息理论的中医望诊可以辅助诊断西医疾病,但目前中医望诊主要依靠名老中医药专家的经验传承,存在望诊客观化、标准化程度不够,缺乏行业内认可度高的望诊转化技术的问题。而望诊融合人工智能信息化技术,可以提升中医望诊客观化、标准化的水平,可以有效地降低疾病的恶化率和病死率,促进中医望诊经验的转化。据此,结合前期开展的大鱼际特征与冠心病关系研究,得出大鱼际望诊可以用于冠心病早期预警筛查。以大鱼际望诊和冠心病之间的关系为例,将先验知识和深度卷积神经网络算法深度融合,将特征提取和分类合为一体,利用深度学习端对端的显著特点,输入观察到的原始大鱼际图像像素数据或信息,通过对大鱼际照片的大量深度学习,构建冠心病患者的关键特征要素,融合先验知识后,输出是否为冠心病的分类结果,中间为深层的网络结构。这一思路将提出一种中医望诊客观化、标准化的智能化算法,促进中医望诊经验的转化思路与方法,以提高基层群众的疾病预警筛查能力,服务“健康中国”战略。

遥感军事坦克轻量化检测的MSG-YOLOv7算法

针对遥感图像下军事坦克检测模型体积大、计算量大等问题,提出一种轻量化的遥感军事坦克目标检测算法MSG-YOLOv7。首先,MSG-YOLOv7采用MobileNetv3作为主干网络,利用倒残差结构和自适应缩放的方法对特征进行提取,以减小模型的体积大小与运算量;其次,设计SD-MP结构来提高细节特征表达能力,解决因下采样操作导致的小目标特征丢失问题;最后,基于GCNet和深度可分离卷积设计出GD-ELAN模块,通过全局上下文建模来增强模型对长距离关系的感知,在轻量化的同时更有效地捕捉全局信息,提高模型的性能。实验结果表明,MSG-YOLOv7在公开的Google Earth遥感军事坦克数据集上的平均检测精度(AP)达到了99.02%,体积较原模型下降了60%,计算量为18.59 GFlops,FPS达到41,证明该模型适用于要求高性能、高速度和较小模型体积的遥感军事坦克检测场景中。

基于改进YOLOv5算法的无人机巡检图像智能识别方法

提出一种基于改进YOLOv5算法的无人机巡检图像智能识别方法。该方法构建无人机巡检图像的相邻图像独立坐标系,并利用相对定向法确定图像中共同目标的位置关系。将巡检目标统一转换至同一坐标系下,采用先进的分割技术提取目标纹理特征向量,为后续的图像识别提供了有力支持。在改进YOLOv5算法的过程中,特别注重多尺度网络的选择与融合激活函数及损失函数的优化组合。采用大疆无人机获取建筑裂缝巡检图像进行实验。结果表明,该方法能够在高效率下实现不同类型建筑裂缝的高精度识别,展现出优异的稳定性能。这一研究成果为无人机巡检图像的智能识别提供了新的思路和方法,具有广泛的应用前景和实际价值。

“网络安全”概念的嬗变及其检视与重构

“网络安全”的核心内涵经历了网络系统安全时代、网络空间安全时代和网络信息安全时代的嬗变,实现了科学技术、互联网和网络信息三次视阈转换,相关研究也从信息科学技术领域延展到哲学及人文社科等领域。由此,在使用“网络安全”概念时,应重视稳定性与灵活性的统一,明确不同学科研究边界,根据不同范畴设定相应的法律原则、管理对象及调控方式。为实现“网络安全”目标,应按照总体国家安全观与法治要求,构建涵盖多主体行为的法律规范体系和网络安全保护制度:对于系统安全,强化技术防范与刑罚震慑;对于空间安全,设计科学有效的制度,重视公民基本权利保障,提高行政执法效率;对于信息安全,科学处理网络信息安全时代的民事、刑事和行政法律关系。

基于卷积神经网络的红外弱小车辆目标检测方法

传统方法无法获得理想的红外弱小车辆目标检测结果,导致检测误差大,无法满足实际应用要求,为了解决传统红外弱小车辆目标检测方法存在的局限性,及时检测红外图像中的弱小车辆,提高车辆检测精度,设计了基于卷积神经网络的红外弱小车辆目标检测方法。首先对弱小车辆目标检测需要的红外图像进行采集,并对红外图像噪声进行处理,消除噪声对弱小车辆目标检测的干扰,然后采用卷积神经网络建立弱小车辆目标检测模型,最后通过具体仿真实验测试弱小车辆目标检测方法的性能。结果表明,该方法的弱小车辆目标检测精度超过了90%,大幅度减少了弱小车辆目标的误检率,同时弱小车辆目标检测时间控制在5 s内,可以满足弱小车辆目标检测的实时性要求,具有较高的实际应用价值。