Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

一种自适应运动目标检测算法及其应用

针对ViBe算法在动态背景下存在鬼影消除时间长、算法适应性差、前景检测噪声多的问题,本文提出一种基于ViBe算法框架的改进算法.该算法采用鬼影检测法标记第1帧中的鬼影区域,并向位于鬼影区域的背景模型中强制引入背景样本,从而快速抑制鬼影;在像素分类过程中,引入自适应分类阈值,解决全局阈值易受动态噪声干扰的问题;在背景模型更新中,根据像素分类的匹配值来动态决定更新因子,提高算法适应场景变化的能力.定性与定量的对比实验结果表明,本文算法相较于ViBe算法能够有效地检测动态背景下的运动目标,应用于河流漂浮物检测场景中也有较好的效果.     

Windows Bootkit实现及其检测系统设计

作为新型的劫持系统内核技术,Windows Bootkit具有较强的隐蔽性和免杀能力,引发了严峻的计算机安全问题。通过分析研究Windows Bootkit的实现方式,并结合可信计算检测原理,设计了一种基于可信计算技术的Windows Bootkit检测系统,应用结果表明,该系统能检测出各种形式的Windows Bootkit,可有效增强Windows操作系统下计算机的安全性。     

一种基于网络安全风险评估的入侵检测方法

针对传统的入侵检测系统存在报警数量大、误报率高等缺陷,提出了一种基于网络安全风险评估的入侵检测方法,该方法基于入侵检测结果,引入抗体浓度随入侵强度动态变化这一人工免疫理论的最新研究成果进行网络安全风险的计算,然后根据当前网络面临的实时安全风险动态设置报警策略。实验结果表明,该方法能够实时、定量地计算主机和网络所面临的风险,并极大地降低报警数量和误报率。     

一种基于有向二分图模型和贝叶斯网络的入侵检测方法

针对入侵检测中存在的非确定性推理问题,文章提出一种基于二分图模型和贝叶斯网络的入侵检测方法,该方法利用二分有向图模型表示入侵和相关特征属性之间的因果拓扑关系,利用训练数据中获取模型的概率参数,最后使用最大可能解释对转化后的推理问题进行推理,并通过限定入侵同时发生的数目来提高检测效率。实验表明,该方法具有较高的检测率和很好的鲁棒性。     

入侵检测主流技术及发展动态

入侵检测技术是网络安全的主要技术和网络研究的热点,入侵检测方法包括基于数据挖掘、粗糙集、模式识别、支持向量机和人工免疫等主要技术,详细分析了各种检测方法在入侵检测应用中的优缺点。通过回顾研究人员近期的研究成果,提出了该技术的主要发展方向,为进一步研究提供参考。     

Snort入侵检测系统中数据包捕获模块的分析与设计

随着网络的高速发展,网络带宽得到了极大的提升。高速网络环境下对入侵检测系统提出了更高的要求,其中入侵检测系统的数据包捕获能力成为其发展的瓶颈。目前大多数系统使用传统的Libpcap库来实现数据包捕获功能,文章对一个基于Snort入侵检测系统中数据包捕获模块进行了分析设计,给出了设计架构,详细说明了工作流程,并对系统的性能进行了对比分析。     

有源电力滤波器谐波检测方法研究

针对常用的ip-iq谐波电流检测方法因采用数字低通滤波器而存在滤波效果和实时性不能同时兼顾的问题,提出了一种采用数学形态滤波器代替数字低通滤波器的改进方法。通过选取适当的结构元素,结合形态学的基本运算,设计了低通形态滤波器。仿真结果表明,在保证检测精度的情况下,改进方法的实时性要明显好于常用的谐波检测方法,具有一定的参考、实用价值。     

航天器用实时操作系统设计

针对航天器在应用实时操作系统时的特殊需要,设计了航天器用实时操作系统的总体设计方案及内核;总体设计中,将操作系统分为板卡级驱动层、系统内核层、接口层三个层次,并确定了满足航天器任务的最简需求与各层的关系;在内核设计中,设计了双向链表管理器、多任务调度管理器、时钟管理器、消息队列管理器、中断管理器,并在其中体现了最简设计的思想;最后,对航天器用实时操作系统的可靠性设计思路进行了描述;此操作系统目前已应用多个航天器中。