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- Das, Yadunandan B., et al.
(författare)
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The influence of temperature on deformation-induced martensitic transformation in 301 stainless steel
- 2018
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Ingår i: Materials Science and Technology. - : Taylor & Francis Group. - 0267-0836 .- 1743-2847. ; 34:17, s. 2114-2125
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Tidskriftsartikel (refereegranskat)abstract
- Deformation-induced martensitic transformations are increasingly being used to create desirable mechanical properties in steels. Here, the kinetics of the deformation-induced martensitic transformation is investigated at 300, 263, 223, 173 and 100 K using in situ neutron diffraction during tensile loading. The results from these experiments show a distinct change in the transformation behaviour between 300 K and the tests conducted at 263 K and below, causing a difference in martensite structure. The difference in transformation kinetics is correlated to the suppression of slip at low temperatures, as evidenced using diffraction peak intensity analysis for different grain families and corroborated using transmission electron microscopy. A direct correlation between the deformation-induced martensite fraction and the work-hardening rate is shown.
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| 2. |
- Sarp, Salih, et al.
(författare)
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An XAI approach for COVID-19 detection using transfer learning with X-ray images
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use and importance of Artificial Intelligence (AI) dramatically increasing. This paper proposes a model using an Explainable Artificial Intelligence (XAI) technique to detect and interpret COVID-19 positive CXR images. We further analyze the impact of COVID-19 positive CXR images using heatmaps. The proposed model leverages transfer learning and data augmentation techniques for faster and more adequate model training. Lung segmentation is applied to enhance the model performance further. We conducted a pre-trained network comparison with the highest classification performance (F1-Score: 98%) using the ResNet model. © 2023 The Author(s)
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