基于表面波的钢管砼界面剥离检测模拟分析

钢管混凝土结构充分利用了钢管和混凝土的优点,具有优越的力学性能,被广泛应用于高层建筑及大型桥梁中。然而,钢管和混凝土间的界面剥离缺陷却会给结构力学性能带来负面影响,对隐蔽性的界面剥离缺陷进行有效检测很重要,基于同侧压电陶瓷片驱动与表面波测量进行检测易于实施。该文运用ANSYS建立了带界面剥离缺陷的钢管混凝土柱模型,通过在钢管混凝土柱表面同侧粘贴压电陶瓷(PZT)片进行驱动和传感,对具有不同长度缺陷的钢管混凝土柱的信号进行数值模拟分析,并与健康工况下的信号进行比较。数值模拟结果表明,界面剥离缺陷工况下的信号幅值较健康工况下大,且损伤程度与信号幅值改变呈正相关,损伤尺度越大,信号幅值越大。该文提出的基于表面波测量的界面剥离缺陷检测方法有效。     

基于多视角卡牌模型的需求缺陷检测

需求来源于不同利益相关方对现实系统的认识和期望。需求获取在整个软件产品的研发过程中至关重要,往往决定着软件产品的质量甚至成败。然而,由于各种复杂因素的影响,获取到的需求中往往存在不完整、不准确甚至冲突等缺陷。需求表达上的二义性、需求描述的不完整和不一致等是最常见的需求缺陷。文中提出一种基于多视角需求获取的卡牌模型和需求缺陷检测规则。在需求获取过程中,特别是在其初期,其能够发现来自各方需求信息中常见的不完整和不一致需求缺陷。最后,通过3组项目案例验证了方法的有效性。     

利用分支路径差异分析的故障定位研究

为了更加有效地定位软件故障可疑位置,考虑程序分支判断语句的行为状态及其之间的相互干扰,提出一种利用分支路径差异分析的故障定位方法。结合GCC编译器产生的中间文件做分析、转换得到程序抽象语法树,获取分支节点的执行信息,构造分支特征矩阵;提出聚类优化算法FCM-BP筛选合适路径,得到高度抗干扰的执行成功的路径代表和执行失败的路径代表;最后基于代表路径做差异分析生成故障可疑度排名报告。在Xerces等大型项目上进行实验分析并与Tarantula等经典实验对比后发现,利用分支路径差异的方法可以有效定位软件故障。     

基于改进遗传算法的浮法玻璃优化切割

浮法玻璃表面含有多种缺陷,这些缺陷的种类、数量和大小会不同程度影响玻璃质量,进而影响玻璃价格。因此在浮法玻璃优化切割过程中,应合理利用缺陷,优先规划质量较好的玻璃原片,以达到提高玻璃成品总价格的目的。针对浮法玻璃优化切割问题,以玻璃成品总价格最大为优化目标,建立该问题的数学模型,提出了一种符合浮法玻璃优化切割问题特性的遗传算法。以一个算例验证了上述模型的正确性及算法的有效性。     

基于标签技术和熵权法的缺陷推荐研究

针对电力系统,设备（资产）运维管理系统（PMS）与调度管理系统（OMS）之间的设备缺陷互联需要PMS运维人员进行主观判断及手动选择操作,导致人员工作量大幅增加且数据交互的不合理程度和不完备程度也逐渐增大,本文提出了基于标签技术和熵权法的缺陷推荐方法.该方法首先以基于正向最大匹配算法、编辑距离和规则库的标签技术对缺陷数据进行标签化标识,然后采用熵权法对其标签进行评价,进而实现向调控员进行智能化推荐缺陷的目的.实验结果表明,通过该缺陷推荐方法的实施,显著减少了运维人员的缺陷选择工作量,并提升了缺陷信息推荐的准确性.     

电压频控中抗强干扰软件关联缺陷检测

针对传统电压频控软件缺陷检测技术未考虑软件缺陷分类,存在检测精度低的问题,提出一种电压频控中抗强干扰软件关联缺陷检测技术。对软件关联缺陷检测原理进行分析,采用判别函数对待测软件样本进行识别,引入统计模式识别算法处理软件原始数据,依据关联缺陷概率分配,确定关联缺陷类别,计算缺陷特征值,利用贝叶斯分类器对关联缺陷进行划分,完成抗强干扰软件关联缺陷的分类,从而实现关联缺陷的高精度检测。实验结果表明,该检测技术对软件缺陷进行准确分类,在保证强抗干扰性的前提下,有效提高了检测精度。     

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent updates involving in many code commits to meet new requirements. Just-in-Time (JIT) defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers, which is more suitable to mobile apps. As the within-app defect prediction needs sufficient historical data to label the commit instances, which is inadequate in practice, one alternative method is to use the cross-project model. In this work, we propose a novel method, called KAL, for cross-project JIT defect prediction task in the context of Android mobile apps. More specifically, KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features. Then, the adversarial learning technique is used to extract the common feature embedding for the model building. We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation. The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.     

Synergistic Effects of Cation and Anion in an Ionic Imidazolium Tetrafluoroborate Additive for Improving the Efficiency and Stability of Half-Mixed Pb-Sn Perovskite Solar Cells

Narrow-bandgap mixed Pb-Sn perovskite solar cells (PSCs) have great feasibility for constructing efficient all-perovskite tandem solar cells, in combination with wide-bandgap lead halide PSCs. However, the power conversion efficiency of mixed Pb-Sn PSCs still lags behind lead-based counterparts. Here, additive engineering using ionic imidazolium tetrafluoroborate (IMBF₄) is proposed, where the imidazolium (IM) cation and tetrafluoroborate (BF₄) anion efficiently passivate defects at grain boundaries and improve crystallinity, simultaneously relaxing lattice strain, respectively. Defect passivation is achieved by the chemical interaction between the IM cation and the positively charged under-coordinated Pb²⁺ or Sn²⁺ ions, and lattice strain relaxation is realized by lattice expansion with the intercalation of BF₄ anions into the perovskite lattice. As a result, the synergistic effects of the cation and anion in the IMBF₄ additive greatly enhance the optoelectronic performance of half-mixed Pb-Sn perovskites, leading to much longer carrier lifetimes. The best-performing half-mixed Pb-Sn PSC shows an efficiency above 19% with negligible hysteresis, while retaining over 90% of its initial efficiency after 1000 h in a nitrogen-filled glovebox and showing a lifetime to 80% degradation of 53.5 h under continuous illumination.     

Dual Additive for Simultaneous Improvement of Photovoltaic Performance and Stability of Perovskite Solar Cell

Additive engineering is one of the most efficient approaches to improve not only photovoltaic performance but also phase stability of formamidinium (FA)-based perovskite. Chlorine-based additives, such as methylammonium chloride (MACl), have been in general used to improve phase stability of FAPbI₃, which however often leads to loss of open-circuit voltage V_oc, accompanied by instability of the perovskite phase due to the volatile nature of the MA cation. A dual additive strategy for improving V_oc and thereby the overall efficiency are reported here. The mixing ratio of MACl to CsCl is varied from [MACl]/[CsCl] = 4 to 1, where V_oc increases with decreasing the ratio and best performance is achieved from [MACl]/[CsCl] = 2. As compared to the single source of MACl, the addition of CsCl reduces trap density and increases resistance against charge recombination, which is responsible for the increased V_oc. Moreover, defect passivation achieved by dual additive enables better stability than the single additive MACl as confirmed by long-term stability tests with unencapsulated devices for 50 days under relative humidity of about 40% at room temperature. The best power conversion efficiency of 23.22% is achieved by dual additive, which is higher than that for single additive of MACl or CsCl.     

Disclosing the Role of Defect-Engineered Metal–Organic Frameworks in Mixed Matrix Membranes for Efficient CO2 Separation: A Joint Experimental-Computational Exploration

Incorporation of defects in metal–organic frameworks (MOFs) offers new opportunities for manipulating their microporosity and functionalities. The so-called “defect engineering” has great potential to tailor the mass transport properties in MOF/polymer mixed matrix membranes (MMMs) for challenging separation applications, for example, CO₂ capture. This study first investigates the impact of MOF defects on the membrane properties of the resultant MOF/polymer MMMs for CO₂ separation. Highly porous defect-engineered UiO-66 nanoparticles are successfully synthesized and incorporated into a CO₂-philic crosslinked poly(ethylene glycol) diacrylate (PEGDA) matrix. A thorough joint experimental/simulation characterization reveals that defect-engineered UiO-66/PEGDA MMMs exhibit nearly identical filler–matrix interfacial properties regardless of the defect concentrations of their parental UiO-66 filler. In addition, non-equilibrium molecular dynamics simulations in tandem with gas transport studies disclose that the defects in MOFs provide the MMMs with ultrafast transport pathways mainly governed by diffusivity selectivity. Ultimately, MMMs containing the most defective UiO-66 show the most enhanced CO₂/N₂ separation performance—CO₂ permeability = 470 Barrer (four times higher than pure PEGDA) and maintains CO₂/N₂ selectivity = 41—which overcomes the trade-off limitation in pure polymers. The results emphasize that defect engineering in MOFs would mark a new milestone for the future development of optimized MMMs.