Identification of residual stress directionality using anisotropic indenter in instrumented indentation testing

Instrumented indentation testing can be used to quantitatively evaluate the local residual stress on the surface. Many studies have confirmed that indentation load-displacement curves obtained from Vickers indentation and Berkovich indentation are shifted depending on the residual stress state. Based on this, many researchers have proposed models for evaluating the residual stress by comparing indentation curves obtained from stressed and stress-free specimens of the same composition and microstructure. Though Vickers and Berkovich indenters can quantitatively evaluate the residual stresses, it is difficult to evaluate their directionality such as principal direction and principal stresses because the indenters are axisymmetric. In order to overcome these limitations, we have evaluated the residual stress directionality by using less axisymmetric indenters, such as the Knoop indenter and a modified Berkovich indenter (a conventional Berkovich indenter extended along one axis). [1] With these two sorts of indenters, the degree of shifting of the indentation curve depends on the direction of the long axis of the indenter in the non-equibiaxial stress state. We introduced a conversion factor, a proportional constant between indentation load difference and stress, and proposed a method for quantitatively evaluating the directionality of surface residual stress using this conversion factor. We applied a non-equibiaxial stress state to cruciform specimens and verified the accuracy of the proposed model using the conversion factor in Knoop and modified Berkovich indentation testing. Also, the experiments and finite-element analysis of Knoop and modified Berkovich indentations showed that the ratio of the length of the major axis and minor axis of the indenter is correlated to the conversion factor ratio; a generalized formula is proposed. REFERENCES [1] Jong-hyoungKim and Huiwen Xu, “Determination of directionality of non-equibiaxial residual stress by nanoindentation testing using a modified Berkovich indenter”, JMR 33. 3849-3856, 2018

A new approach to evaluate residual stress using instrumented indentation testing at nano scale

In structural integrity, residual stress is one of the major factors affecting structure failure. In particular, tensile residual stress accelerates crack growth and reduces integrity. Hence test methods have been devised that can quantitatively evaluate residual stresses, including X-ray diffraction, hole-drilling, and contour methods. Now a relatively new technique, instrumented indentation testing, can be used to quantitatively evaluate the surface residual stress of a structure semi-nondestructively with mechanical response causing small indents. Many studies have confirmed that indentation load-displacement curves are shifted depending on the residual stress state. For the same indentation depth, a larger indentation load is required for a compressive residual stress state, and a smaller indentation load is required for a tensile residual stress state, in contrast to the stress-free state. Thus, for the same indentation depth, there is a difference in indentation load between the stressed and stress-free states. Kwon and Lee have suggested and verified experimentally that, among the surface residual stress components, a deviatoric stress term parallel to the indentation axis induces a virtual force that affects the plastic deformation occurring during indentation, and consequently also affects the indentation load-displacement curve. [1] In this paper, principle and application for measuring residual stress by IIT at multi-scale will be included. References [1] Y.-H. Lee and D. Kwon, “Estimation of biaxial surface stress by instrumented indentation with sharp indenters”, Acta Materialia 52. 1555-1563, 2004

Evaluation of tensile properties using instrumented indentation technique for small scale testing

The Instrumented indentation technique (IIT) is a useful tool for estimating various mechanical properties such as tensile properties, fracture toughness, and residual stress by analyzing the load and depth curve. Unlike conventional test such as tensile test, CTOD, since IIT makes an indent with rigid indenter and measures load and depth continuously, it requires only a localized area and small area on the target material. IIT also has merits of simple specimen preparation and experimental procedure in terms of time and cost. Also, it can be applied to in-field structures nondestructively. In this study, we introduce a method for evaluating tensile properties, primary yield strength and tensile strength using representative stress-strain beneath the rigid spherical indenter through numerous investigations of instrumented indentation curves. Analytic models and procedures for estimating the mechanical characterization of materials using IIT are proposed. The representative stress-strain method directly correlates indentation stress and strain beneath indenter to true stress and strain of the tensile test by taking into account the plastic constraint effect. The experimental results from IIT were verified by comparing results from the uniaxial tensile test. In particular, the applications of IIT in small scale and localized area of materials are presented. Reference 1) D. Tabor: Hardness of metal, (first ed. Clarendon Press, New York, 1951) 2) W.C. Oliver and G.M. Pharr, J. Mater, Res, Vol. 7, (1992), p. 1564 3) S.-K. Kang, Y.-C. Kim, K.-H. Kim, J.-Y. Kim and D. Kwon, Int. J. Plast. 49, 1 (2013

Do expressive writing interventions have positive effects on Koreans?: a meta-analysis

IntroductionExpressive Writing (EW) is an intervention that focuses on individuals’ writing down their thoughts and feelings about trauma or stressful events. Meta-analyses on EW studies have confirmed that EW has a positive effect. However, the heterogeneity of studies is high, so many studies have investigated boundary conditions and moderators. One of these moderators is the cultural difference in emotional suppression. Since EW focuses on the expression of suppressed thoughts and emotions, its effect might be slightly different for people in Asian cultures who show a high tendency to suppress their emotions. This study attempted to confirm the effect size of the EW interventions in Korea and examine whether these studies have different effect size from those based on Western cultures.MethodA total of 29 studies published in Korea until 2021 were analyzed. The effect size was calculated using the “dmetar,” “meta,” and “metafor” packages of the statistical program R 4.0.4.ResultsThe results were as follows. First, the effect size of EW intervention was 0.16, and we found that studies in the Korean context showed no significant difference from studies based on western meta-analysis. Second, the moderating variables that influenced the EW intervention were the writing type, the number of sessions, the time per session, and the measurement time.DiscussionThe results of this study suggest that EW interventions benefit Koreans. And it is at least harmless and has a positive effect considering the efficiency and conciseness of interventions. Furthermore, the finding shows that EW interventions can be helpful even in the general population without apparent psychological problems. By considering moderators, we could structure more effective form of EW interventions for Koreans

Fully Quantized Always-on Face Detector Considering Mobile Image Sensors

Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications.Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks (LBQNN), Ora

Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors

As the physical size of recent CMOS image sensors (CIS) gets smaller, the latest mobile cameras are adopting unique non-Bayer color filter array (CFA) patterns (e.g., Quad, Nona, QxQ), which consist of homogeneous color units with adjacent pixels. These non-Bayer sensors are superior to conventional Bayer CFA thanks to their changeable pixel-bin sizes for different light conditions but may introduce visual artifacts during demosaicing due to their inherent pixel pattern structures and sensor hardware characteristics. Previous demosaicing methods have primarily focused on Bayer CFA, necessitating distinct reconstruction methods for non-Bayer patterned CIS with various CFA modes under different lighting conditions. In this work, we propose an efficient unified demosaicing method that can be applied to both conventional Bayer RAW and various non-Bayer CFAs' RAW data in different operation modes. Our Knowledge Learning-based demosaicing model for Adaptive Patterns, namely KLAP, utilizes CFA-adaptive filters for only 1% key filters in the network for each CFA, but still manages to effectively demosaic all the CFAs, yielding comparable performance to the large-scale models. Furthermore, by employing meta-learning during inference (KLAP-M), our model is able to eliminate unknown sensor-generic artifacts in real RAW data, effectively bridging the gap between synthetic images and real sensor RAW. Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs

Depression Symptoms Mediate Mismatch Between Perceived Severity of the COVID-19 Pandemic and Preventive Motives

The present study monitored changes in beliefs about the coronavirus disease 2019 (COVID-19) pandemic, depressive symptoms, and preventive motives between the first and second waves in South Korea using an online survey administered to 1,144 individuals nationally representative for age, gender, and areas of residence. While participants correctly updated their beliefs about the worsening pandemic situations, the perceived importance of social distancing did not change, and their motives to follow prevention measures shifted toward compulsory rather than voluntary motives. This inconsistency appeared to be mediated by depressive symptoms, such that negative belief changes followed by increased depressive symptoms were associated with the decreased perceived importance of social distancing and decreased voluntary motives. Our data highlights the importance of psychological responses to the dynamically evolving pandemic situations in promoting preventive behaviors

Harnessing the power of diffusion models for plant disease image augmentation

IntroductionThe challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions.MethodsIn this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications.ResultsQuantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61).DiscussionThis study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field