Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.

Application of ultrasonic inspection in monitoring dynamic healing of mandibular fracture in rabbit model

Objective:To investigate the feasibility of ultrasonic diagnosis for monitoring fracture healing. Methods:Thirty rabbit models with fraction of mandible body were established by surgically removing partial lower jawbone.At the 1st,2nd,4th,6th,8th and 12th week after the operation, they were examined by X-ray and ultrasound,respectively.All detection results were scored according to a generally accepted standard.Spearman rank correlation analysis was conducted to explore the relationship between the results of the two inspection methods.Results:In each healing stage,the results of the ultrasonic inspection were basically consistent with those of the X-ray examination,as supported by a Spearman rank correlation coefficient of 0.892(P【0.001). Conclusions:Non-invasive ultrasonic inspection can be used instead of X-ray examination to monitor and diagnose fracture healing.

<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.

Sowing Seeds Of Food Security

China vigorously develops seed industry to ensure seed and food security Chinese President Xi Jinping reiterated the importance of the seed industry and stressed the crucial role of “Chinese seeds” in ensuring the country’s food security during an inspection of the Yazhou Bay Seed Laboratory in Sanya, south China’s Hainan Province, on April 10.

Application of molybdenum target X-ray photography in imaging analysis of caudal intervertebral disc degeneration in rats

BACKGROUND Conventional plain X-ray images of rats,the most common animals used as degeneration models,exhibit unclear vertebral structure and blurry intervertebral disc spaces due to their small size,slender vertebral bodies.AIM To apply molybdenum target X-ray photography in the evaluation of caudal intervertebral disc(IVD)degeneration in rat models.METHODS Two types of rat caudal IVD degeneration models(needle-punctured model and endplate-destructed model)were established,and their effectiveness was verified using nuclear magnetic resonance imaging.Molybdenum target inspection and routine plain X-ray were then performed on these models.Additionally,four observers were assigned to measure the intervertebral height of degenerated segments on molybdenum target plain X-ray images and routine plain X-ray images,respectively.The degeneration was evaluated and statistical analysis was subsequently conducted.RESULTS Nine rats in the needle-punctured model and 10 rats in the endplate-destructed model were effective.Compared with routine plain X-ray images,molybdenum target plain X-ray images showed higher clarity,stronger contrast,as well as clearer and more accurate structural development.The McNemar test confirmed that the difference was statistically significant(P=0.031).In the two models,the reliability of the intervertebral height measured by the four observers on routine plain X-ray images was poor(ICC<0.4),while the data obtained from the molybdenum target plain X-ray images were more reliable.CONCLUSIONMolybdenum target inspection can obtain clearer images and display fine calcification in the imaging evaluation of caudal IVD degeneration in rats,thus ensuring a more accurate evaluation of degeneration.

Reduction of the Beam Hardening Artifacts in the X-Ray Computer Tomography: Energy Discrimination with a Photon-Counting Detector

The material identification is a pressing requirement for the sensitive security applications. Dual-energy X-ray computer tomography (DXCT) has been investigated for material identification in the medical and security fields. It requires two tomographic images at sufficiently different energies. To discriminate dangerous materials of light elements such as plastic bombs in luggage, it is needed to measure accurately with several tens of kilo electron volts where such materials exhibit significant spectral differences. However, CT images in that energy region often include artifacts from beam hardening. To reduce these artifacts, a novel reconstruction method has been investigated. It is an extension of the Al-gebraic Reconstruction Technique and Total Variation (ART-TV) method that reduces the artifacts in a lower-energy CT image by referencing it to an image obtained at higher energy. The CT image of a titanium sample was recon-structed using this method in order to demonstrate the artifact reduction capability.

迈向智能监察:人工智能赋能国家监察的逻辑与进路

新一代人工智能的兴起为国家治理注入新动能,为国家监察工作的数字化、智能化指明方向。人工智能赋能国家监察具有以“数据全覆盖”实现“监察全覆盖”、从人力监督到智能监督等现实价值。受限于国家监察的初创性与人工智能的局限性,国家监察智能化呈现阶段性特征。国家监察的智能化发展围绕数据、算法、算力三大核心要素,实现监察“块数据”构建、面向国家监察的语言模型嵌入及算力资源的优化供给。针对人工智能存在的数据隐私、数据安全等技术性风险,可从人工智能政府采购的制度化与规范化、建构监察对象数据隐私与安全保护机制和人工智能监察应用的纠错与救济机制等方面予以防范和化解。

“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。

多模态情感视角:民航旅客异常行为预警研究

通过构建基于多模态情感计算的民航旅客异常行为预警模型,使用GEMEP语料库中音视频数据模拟旅客可能出现的异常行为,在对面部表情、肢体姿态和语音三模态数据识别后,采取赋予各模态情感识别模型权重和赋予各模态情感识别模型中不同情感权重两种决策级融合方法,感知旅客生气、厌恶、焦虑、害怕、悲伤等5类情感,并依据判定结果实现异常行为分级预警。研究发现,赋予各模态情感识别模型中不同情感权重的方法为最优情感计算模型,能够有效识别旅客存在潜在异常行为的相关情感,其整体识别准确率达到了82.76%。其中,生气、厌恶、焦虑、害怕、悲伤5类情感识别准确率分别为81.9%、78.5%、81.3%、83.2%、81.7%。

重大活动特种设备安全保障工作标准化研究

近年来,我国举办的重大活动越来越多,特种设备的安全保障工作逐步发展并形成常态化机制。为推进重大活动特种设备安全保障工作的规范化和制度化,本文总结历次特种设备安全保障经验,分析其现状和挑战,并从特种设备安全保障工作的流程和要求方面提出建议,为推进重大活动特种设备安全保障工作的标准化提供支撑。

基于多维感知与空天一体的输电线路智慧运维体系建设与实践

基于多维感知与空天一体技术,提出了一种输电线路的智慧运维体系。该体系通过卫星遥感、无人机巡检、智能安防以及多传感器集群等技术的协同应用,实现了输电线路的全方位实时监测与隐患排查。卫星遥感技术用于输电通道环境的宏观监控,精准评估自然灾害和外力破坏风险;无人机巡检技术则提供了高效的多场景应用,包括红外测温、风筝线隐患排查等,极大提升了巡检效率;智能安防系统通过多传感器联控感知,实现了对输电线路环境与设备的三维空间监控与智能预警。实践结果表明,该智慧运维体系大幅提升了输电线路的安全性、可靠性及巡检效率,推动了输电运维技术的革新。

安检图像小目标违禁品特征提取模块构建与应用

针对物流包裹安检图像中小目标违禁品易漏检问题,通过在感受野模块的多分支并行网络上引入卷积注意力模块,构建一种适用于小目标违禁品检测的特征提取模块。在此基础上,将构建的特征提取模块融入YOLOv5模型的主干部分,使得模型在违禁品检测的过程中聚焦于图像的重要特征。为了充分发挥所构建模块对于小目标物体的特征提取能力,采用空间深度转换模块替代原模型中的下采样模块,使得YOLOv5模型在特征提取的过程中能够尽可能地保留小目标物体的特征信息,提高对小目标违禁品的检测效果。

改进YOLOv7的X光图像危险品检测算法

针对X光安检图像在危险品检测时背景复杂、遮挡严重、尺度多变等问题,对YOLOv7算法进行了改进,在提高检测精度的同时使网络更加轻量化。首先构建PS-ELAN模块替换原主干网络中的ELAN模块,减少网络计算量和内存占用,同时提升网络的特征提取能力。其次将无参注意力机制SimAM与可变形卷积DCNv2融合至颈部网络的下采样阶段,提高网络对X光图像危险品关键特征的捕捉能力。最后引入Dynamic Head模块,增强检测头的尺度感知、空间感知和任务感知,提高网络的检测性能。实验结果表明,改进后的算法在自制数据集和CLCXray数据集上的平均精度均值(mean average precision,mAP)比原YOLOv7模型分别提高了4.7个百分点和1.2个百分点,参数量和计算量分别下降了16.2%和23.1%。改进后的算法提高了检测能力,同时更为轻量化,可在实际安检中起到很好的辅助作用。

辅助驾驶汽车信息安全检验关键技术研究及测试验证

随着辅助驾驶汽车技术的不断发展和普及,信息安全问题已经成了一个重要的研究方向。本文针对辅助驾驶汽车面临的信息安全隐患,研究提出信息安全检验关键技术,包括检验指标、检验方法和检验标准等,为辅助驾驶汽车安全性评估提供参考;提高辅助驾驶汽车安全性,推动行业的规范化发展,为辅助驾驶汽车的安全性和可靠性提供支持。

大型安检设备工业设计的造型难点及设计思路探究

工业设计在消费级产品中的应用蓬勃发展,但针对大型安检设备的工业设计造型研究仍然缺乏。为推进大型安检设备的工业设计造型发展,通过分析大型安检设备的工业设计造型价值,总结了大型安检设备在设计过程中的难点和痛点,探讨了大型安检设备工业设计造型的设计思路,希望为从业人员解决类似的设计难题提供参考。

基于信息化的高校实验室安全管理体系建设

实验室安全建设是高校争创“双一流”的重要保障。针对高校实验室安全管理中普遍存在的重点、难点、痛点问题,结合学校实际,探索数字化、信息化和智能化背景下实验室安全管理平台及管理体系建设的创新发展路径。通过绘制实验室安全图谱、实施多维检查、打造化学品一瓶一码全周期可追溯系统、搭建智慧楼宇3D可视化系统、层级化多元化长效化开展实验室安全教育培训、加强应急救援物资配备等举措,逐步形成了科学、规范、高效的主动防御体系,显著提升了实验室安全管理水平,为实现实验室零安全事故目标提供了坚实的机制保障。

结合改进YOLOv8n及SLAM的机器人自主巡检控制系统研究

本文提出了一种基于改进YOLOv8n及SLAM的室内安防机器人自主巡检控制系统方案。目标检测采用轻量化处理的YOLOv8n算法,使用GhostNet进行YOLOv8n的轻量级改进。达到降低计算量和内存消耗,同时提高目标检测速度的目的。改进后的YOLOv8n帧率相较于改进前速度提升50%,模型缩小为原来的63%,而识别的精确度仅下降5%左右。为提高机器人对未知环境的感知能力和自主性,使用了Cartographer算法。基于此算法,机器人可实现自主导航和地图构建,定位过程中估计值与实际值的横向偏差小于0.06 m;纵向偏差小于0.08 m;航向偏角小于16°。实验结果表明:该系统能够实现地图的精确构建以及目标火焰的快速检测,并实现实时预警。

基于随机巡视机制的电力物联网安全技术研究

电力物联网的建设促进了电力系统各个方面的数据集成,但对电网系统的网络安全提出了较高要求,因此在电力物联网安全技术的基础上,提出了一种基于随机巡视的电力物联网安全行为异常检测方法。通过对网络节点进行编号管理,为不同信誉值的节点设计了不同的随机巡视频率,汇聚节点检测异常行为。通过仿真分析了文中方法在安全性和恶意节点检测方面的性能。结果表明,文中提出的检测方法能够及时有效地检测异常行为和恶意节点,降低了网络能耗,满足电力物联网的安全需求。

基于数值模拟的安检大厅人员火灾疏散仿真

为了解火灾发生时某安检大厅人员疏散情况,建立了火灾模型及人员疏散模型,通过模拟分析了火灾产物及空间熟悉度对人员疏散产生的影响。火灾模拟结果表明,对于安检大厅这类建筑,能见度是影响人员疏散的主要因素,建议加强厅内的排烟设备等消防设施。人员疏散结果表明,空间熟悉度影响人员对于出口的选择,使得初次到达该安检大厅旅客无法通过距离最近出入口进行疏散,疏散时间增加;且厅内的建筑布局也阻碍了人员疏散进程。因此,结合火灾中烟气蔓延过程和人员疏散结果,对厅内的建筑布局及人员疏散方式进行优化,疏散优化后疏散时间缩短了10%。模拟结果可为安检大厅的消防措施、建筑设计以及应急管理提供参考和借鉴。

选煤厂控制系统综合智能化升级改造研究

针对贵州某选煤厂洗选工艺复杂、自动化程度低等问题,提出通过智能化升级改造提高选煤厂生产效率及产品品质。在分析选煤厂现有生产工艺和系统基础上,对智能化升级改造方案进行探讨并分析现场应用效果。结果表明,智能化系统在减人增效及提高选煤厂产品品质及生产效率方面效果显著,现场工作人员减少至94人、降低人力资源成本约576万元/年;同时精煤合格率、产率分别提升约1.2%、0.5%,直接增加经济效益约158万元/年。