Testing web applications

Traditional testing techniques are not adequate for web-based applications, since they miss their additional features such as their multi-tier nature, hyperlink-based structure, and event-driven feature. Limited work has been done on testing web applications. In this paper, we propose new techniques for white box testing of web applications developed in the .NET environment with emphasis on their event-driven feature. We extend recent work on modeling of web applications by enhancing previous dependence graphs and proposing an event-based dependence graph model. We apply data flow testing techniques to these dependence graphs and propose an event flow testing technique. Also, we present a few coverage testing approaches for web applications. Further, we propose mutation testing operators for evaluating the adequacy of web application tests.     

Experiments and simulations of the mechanical performance of repaired panels

Firstly, the compress experiment is undertaken to investigate the efficiency of repaired panels in this paper, and then modeling of the mechanical behavior of the repaired composite panel under compressive static load is conducted by using of the finite element method. The effect of geometric non-linearity on the stress–strain response is considered in the numeric analysis. Fatherly, the user material subroutine (UMAT) is integrated with the ABAQUS package with the geometric non-linearity effect for studying the damage initiation and its progression in the composite structure, and quadrilateral, linear, thick shell elements (S8R) are adopted. Finally, the predicted strain distribution, damage evolution and strength of the laminate are compared with the test results.     

Fiber–fiber interactions in carbon mat thermoplastics

Carbon mat thermoplastics (CMT) consisting of 12.7 mm long, chopped carbon fibers in a polypropylene matrix were manufactured using the wetlay technique at fiber volume fractions (FVF) from 10% to 25%, and tests simulating the compression molding process were conducted. The packing stress of the CMT followed a power law relation with FVF. A single fiber pull-out fixture was used to measure the frictional and hydrodynamic lubrication coefficients at fiber–fiber touch points, and results were fit with an existing relation for glass mat thermoplastics. In isothermal squeeze flow the load–displacement behavior for the 10% FVF CMT was similar in shape to that for a fluid with a yield stress. However, for FVFs of 15–25%, the load–displacement curves showed a load spike at the beginning of the flow, then followed the curve for a fluid with a yield stress. The spike was attributed to fiber breakage that increased with increasing FVF of the sample.     

折线链节点标注技术

针对传统多边形位置关系计算比较烦琐,以及简单多边形的理论难以拓展到一般多边形的问题,提出标注节点状态的方法.通过定义11种位置来描述折线链上每个节点的状态,再采用"线段端点与线段"和"线段端点与邻折线"的标注方法来实现任意折线链的标注,同时利用两线段分割预处理使相交仅发生在端点处,从而使算法更高效;然后给出折线链基本位置关系的节点特征,并且探讨了三维顶点的标注方法.该方法的标注原理简单、方法实用,算法空间和时间复杂度分别为O(n)和O(n2).实验结果表明,该方法对任意形状的折线链都能实现稳定标注;通过搜索节点状态特征可以求解折线链间的相互关系,还可以实现一般折线链的碰撞检测、相交区域计算以及多边形简单化分解等.     

数字磁通门传感器的自动相位对准

三轴数字磁通门传感器信号处理电路的延时(相位延迟)及各轴延时不一致,影响数字滤波器的输出性能。提出了基于加权最小二乘法的互功率谱相位法计算延时的方法,针对该数字滤波器的特性,选择传感器的不同谐波信号对比分析相位延迟计算的精度,并设计了传感器的自动相位对准方法。实验结果表明:该计算方法精确有效,自动相位对准使滤波器输出性能提高25.89%。     

基于SOI的硅微谐振式压力传感器芯片制作

采用SOI硅片,基于MEMS技术,设计并加工了一种新型三明治结构的硅微谐振式压力传感器,根据传感器敏感单元的结构设计,制定了相应的制备工艺步骤,并且针对湿法深刻蚀过程中谐振子的刻蚀保护等问题,提出了一种基于氮化硅、氧化硅和氮化硅三层薄膜的保护工艺,实验表明,在采用三层薄膜保护工艺下进行湿法刻蚀10 h后,谐振子被完全释放,三层薄膜保护工艺对要求采用湿法刻蚀镂空释放可动结构具有较高的实用价值。最后对加工完成的谐振式压力传感器进行了初步的性能测试,结果表明,在标准大气压力下谐振子的固有频率为9.932 kHz,品质因数为34。     

新的e修正鲁棒自适应控制方法在航空发动机上的应用

针对航空发动机模型的结构和参数的不确定性,提出一种基于非李亚普诺夫方程(NQLF)的e修正鲁棒模型参考自适应控制法。该方法无需知道发动机精确数学模型,抗干扰能力强,在输出误差信号的作用下具有比普通方法更快的收敛性,振荡性能够得到很好的控制。从仿真结果可见控制器能使系统比较好地跟踪参考模型的输出,对发动机模型参数和结构的变化有一定的适应能力,鲁棒性较好。     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.     

Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing

Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies. Three-dimensional printing (3DP) reduces global energy consumption and CO₂ emissions related to industrial manufacturing. Various renewable energy harvesting mechanisms utilizing solar, wind, tidal and human potential have been fabricated through additive manufacturing. 3D printing aided the manufacturing companies in combating the deficiencies of medical healthcare devices for patients and professionals globally. In this regard, 3D printed medical face shields, respiratory masks, personal protective equipment, PLA-based recyclable air filtration masks, additively manufactured ideal tissue models and new information technology (IT) based rapid manufacturing are some significant contributions of 3DP. Furthermore, a bibliometric study of 3D printing research was conducted in CiteSpace. The most influential keywords and latest research frontiers were found and the 3DP knowledge was categorized into 10 diverse research themes. The potential challenges incurred by AM industry during the pandemic were categorized in terms of design, safety, manufacturing, certification and legal issues. Significantly, this study highlights the versatile role of 3DP in battle against COVID-19 pandemic and provides up-to-date research frontiers, leading the readers to focus on the current hurdles encountered by AM industry, henceforth conduct further investigations to enhance 3DP technology.     

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.