Partial safety factors for laminated glass

Glass plies can be bonded together by polymeric interlayers to form laminated glass. Thanks to the capacity in finding alternative stress paths after partial breakage, it is commonly accepted that the more the layers are, the higher the safety level is. However, a negative aspect is that the tensile strength of glass carries a size effect in terms of surface area, which increases with lamination. Here we evaluate these competing factors by calibrating the partial safety factors to be used in semi-probabilistic (level-I) design, through comparison with probabilistic (level-III) methods in paradigmatic case-studies under wind, snow, and dead weight. Starting from a two-parameter Weibull distribution for glass strength, the “failure modes” approach determines the statistical distribution of strength for a multi-laminate as a function of the number of plies, which interferes with the statistics for actions. As a function of the target probability of failure for the assigned class of consequence, we introduce a new coefficient in the verification formula of level-I, to account for the effects of lamination with dependence upon the number of plies. We find that there is strong gain when passing from a monolith to a two-ply laminate, but the advantage fades by increasing the number of layers. Verification formulas of this type could avoid overconservative design.     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.     

基于混合润滑模型的往复密封动特性研究

为研究往复运动密封性能,采用MatLab数值方法建立一种混合润滑模型,该模型包含弹性力学、流体力学和接触力学分析。基于混合润滑模型,研究粗糙度和往复速度对动态往复密封摩擦力、泄漏量和油膜厚度等密封性能的影响规律,揭示液压往复密封机制。结果表明:往复运动密封圈处于混合润滑状态,接触区不仅有流体压力,还包含粗糙度接触压力;存在临界粗糙度σ_c和临界速度u_c,当粗糙度σσ_c时,随粗糙度的增大内行程的泄漏表现为越来越小的内泄漏,当σ≥σ_c时,随粗糙度的增大内行程的泄漏表现为越来越大的外泄漏;当速度uu_c时,净泄漏量随速度的增大表现为越来越小的外泄漏,当u≥u_c时,净泄漏量随速度的增大表现为越来越大的内泄漏;随着粗糙度的增加,膜厚与内行程的摩擦力增大,而外行程的摩擦力无明显变化;随着速度的增加,油膜厚度增加,内行程摩擦力减小,而外行程摩擦力变化很小。     

基于动态行为学习的空中目标识别方法

针对空中移动目标识别问题,提出了一种基于动态行为学习的识别方法。首先,从数据源中提取目标要素,经过标注形成目标数据特征集;其次,基于应用场景进行特征组合,形成特征模型要素;然后,基于深度学习算法进行模型训练,得到评估最优的模型参数;最后,利用训练后的模型进行目标识别处理。通过数值仿真验证,该方法能够有效地识别无业务规则的目标身份。     

Effect of α phase morphology on fatigue crack growth behavior of Ti−5Al−5Mo−5V−1Cr−1Fe alloy

Taking a Ti−5Al−5Mo−5V−1Cr−1Fe alloy as exemplary case, the fatigue crack growth sensitivity and fracture features with various tailored α phase morphologies were thoroughly investigated using fatigue crack growth rate (FCGR) test, optical microscopy (OM) and scanning electron microscopy (SEM). The tailored microstructures by heat treatments include the fine and coarse secondary α phase, as well as the widmanstatten and basket weave features. The sample with coarse secondary α phase exhibits better comprehensive properties of good crack propagation resistance (with long Paris regime ranging from 15 to 60 MPa·m^1/2), high yield strength (1113 MPa) and ultimate strength (1150 MPa), and good elongation (11.6%). The good crack propagation resistance can be attributed to crack deflection, long secondary crack, and tortuous crack path induced by coarse secondary α phase.     

A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.     

Perovskite catalysts for methane combustion: applications,design, effects for reactivity and partial oxidation

This review underlines the importance of the developments in perovskite catalysts for methane combustion from the past up to the present. In this review, after a general and brief introduction to perovskites, the mechanisms of catalytic combustion of methane have been included. Moreover, current studies on perovskites have been summarized including the effects of substitutions, doped perovskites, perovskite preparation methods, and the effect of sulfur presence on perovskite catalysts. Besides, recent studies on perovskite oxides and phenomenon of oxygen (O₂) deficiency, porous perovskite oxides, and nanostructured perovskites have been conducted. In addition, partial oxidation of methane (POM) has been reviewed. The loss of active component during the POM reaction can take place in the nickel catalyst, in particular. Since nickel has a lower melting point than noble metals and other active components, such as Co and Fe, in general, to deactivate nickel is easier. Compared with conventional structure, the porous structure with the unique morphology significantly enhances the catalytic activity through a much larger surface area (SA) and greater reactivity of the active sites. Furthermore, the monolithic nanoarrayed perovskite presents very good results in well‐defined faceted catalysts and takes part in porous channel hydrocarbon combustion. This review study is prepared as a guide to cover the profound knowledge of perovskite oxides catalysts, considering the methane combustion reaction mechanisms, and addresses prospective studies in this field for researchers.     

Feature-based ontological framework for semantic interoperability in product development

An essential requirement in integrating tasks in product development is to have a seamless exchange of product information through the entire product lifecycle. A key challenge in the integration is the exchange of shape semantics in terms of understandable labels and representations. A unified taxonomy is proposed to represent, classify, and extract shape features. This taxonomy is built using the Domain-Independent Form Feature (DIFF) model as the representation of features. All the shape features in a product model are classified under three main classes, namely, volumetric features, deformation features and free-form surface features. Shape feature ontology is developed using the unified taxonomy, which brings the shape features under a single reasoning framework. One-to-many reasoning framework is presented for mapping semantically equivalent information (label and representation) of the feature to be exchanged to target applications, and the reconstruction of the shape model automatically in that target application. An algorithm has been developed to extract the semantics of shape features and construct the model in the target application. The algorithm developed has been tested for shape models taken from literature and test cases are selected based on variations of topology and geometry. Results of exchanging product information are presented and discussed. Finally, the limitations of the proposed method for exchanging product information are explained.