A Comprehensive Study Of Nonlinear Effects Of Coupling Materials In Ultrasound Infrared Imaging

Nondestructive Evaluation (NDE) is a multidisciplinary field of research, which is focused on the development of analysis and measurement technologies for the quantitative characterization of materials, components and structures. It is a key process used in product evaluation, troubleshooting for the quality assurance in industry. Sonic Infrared (IR) Imaging technology is a hybrid sensing and imaging technique, in which cracks in an object are caused to become visible in the infrared imaging through frictional heating associated with the application of a short pulse of low-frequency ultrasound. The technique uses pulses of ultrasonic excitation applied to a sample for a fraction of a second. The heating at the crack is then captured by calibrated infrared cameras using real-time video/digital imaging. It\u27s been demonstrated that this technique can detect surface and subsurface cracks, delaminations, and disbonds in metallic and composite materials successfully. As a promising NDE technique, ultrasonic Infrared Imaging technique has gain more attention from researchers and technicians in NDE community, it has been used in detecting cracks/defects in the automotive and aerospace industry for several years. The purpose of the research work is to comprehensively study the non-linear effect of coupling materials used in the technique, where a coupling material is a thin layered material that separates transducer and sample. In this research, a series of coupling materials are investigated, and a comprehensive analysis, using different engagement force or different pulse frequency on two commonly used aluminum samples with different geometries and structures are studied. The combination of vibration waveforms and IR images/signals is used as an analysis method for the comprehensive study. Correlation analysis between the acoustic and thermal energy in the crack is discussed, as well. The finite element analysis is used to predict the thermal-mechanical behavior of the cracks in the samples under different boundary conditions by using different coupling media, different loading force and pulse frequency. FEA results are validated with the test results side by side. It is verified that coupling material can play an important role in crack detection

Quantification is not enough : the analysis of the complexity and uncertainty of urban form evolution

M.R.G.Conzen has implied that quantification, as a purely methodological approach, is not a methodology. It means quantitative approaches can be used as auxiliary means to study urban morphology, but it is not decisive. Thus, integrated method is more comprehensive and effective to analysis the complex influential factors of urban form. Therefore, the purpose of this paper is to prove this idea above through analyzing the complexity and uncertainty of urban form evolution, used by real examples of the burgage cycle. The evidences for choosing this outline are, firstly, the burgage cycle that M.R.G.Conzen proposed has demonstrated the complexity of the urban form evolution, and it still be used to research today. Second, these realistic cases can proved clearly that the determination and the value of the "climax" in the cycle is far from being decided and measured by purely quantitative methods, because many uncertain internal and external factors are able to influence it

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversarial examples can be improved by attacking diverse models simultaneously, model augmentation methods which simulate different models by using transformed images are proposed. However, existing transformations for spatial domain do not translate to significantly diverse augmented models. To tackle this issue, we propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models. Specifically, we apply a spectrum transformation to the input and thus perform the model augmentation in the frequency domain. We theoretically prove that the transformation derived from frequency domain leads to a diverse spectrum saliency map, an indicator we proposed to reflect the diversity of substitute models. Notably, our method can be generally combined with existing attacks. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method, \textit{e.g.}, attacking nine state-of-the-art defense models with an average success rate of \textbf{95.4\%}. Our code is available in \url{https://github.com/yuyang-long/SSA}.Comment: Accepted by ECCV 202

Engineered Corynebacterium glutamicum as an endotoxin-free platform strain for lactate-based polyester production

The first biosynthetic system for lactate (LA)-based polyesters was previously created in recombinant Escherichia coli (Taguchi et al. 2008). Here, we have begun efforts to upgrade the prototype polymer production system to a practical stage by using metabolically engineered Gram-positive bacterium Corynebacterium glutamicum as an endotoxin-free platform. We designed metabolic pathways in C. glutamicum to generate monomer substrates, lactyl-CoA (LA-CoA), and 3-hydroxybutyryl-CoA (3HB-CoA), for the copolymerization catalyzed by the LA-polymerizing enzyme (LPE). LA-CoA was synthesized by D-lactate dehydrogenase and propionyl-CoA transferase, while 3HB-CoA was supplied by β-ketothiolase (PhaA) and NADPH-dependent acetoacetyl-CoA reductase (PhaB). The functional expression of these enzymes led to a production of P(LA-co-3HB) with high LA fractions (96.8 mol%). The omission of PhaA and PhaB from this pathway led to a further increase in LA fraction up to 99.3 mol%. The newly engineered C. glutamicum potentially serves as a food-grade and biomedically applicable platform for the production of poly(lactic acid)-like polyester

Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Acknowledgments This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.Peer reviewedPostprin