Fatigue Behavior of Stainless Steel Sheet Specimens at Extremely High Temperatures

Avery, Katherine; Pan, Jwo; Engler-Pinto, Carlos; Wei, Zhigang; Yang, Fulun; Lin, Shengbin; Luo, Limin; Konson, Dmitri

doi:10.4271/2014-01-0975

Cited by 22 publications

(6 citation statements)

References 7 publications

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“…In this regard, deep learning, also known as self-learning artificial intelligence approaches, are paving new avenues for data analysis and computer vision 9 . In the field of remote sensing, so-called Convolutional Neural Networks (CNN) are currently revolutionizing possibilities for object detection and pattern recognition 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Kattenborn

Eichel

Fassnacht

2019

Sci Rep

191

110

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Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Kattenborn

Eichel

Fassnacht

2019

Sci Rep

191

110

View full text Add to dashboard Cite

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“…Its chemical composition is given in Table 1. Specimens with the geometry in Figure 1a are machined from the as-received steel plates according to the standard [20]. Before the experiment, the surface was polished with 2000 grit emery papers and cleaned by distilled water and ethanol.…”

Section: Methodsmentioning

confidence: 99%

Thermal Fatigue Characteristics of Type 309 Austenitic Stainless Steel for Automotive Manifolds

Zhan

Huang³

et al. 2019

Metals

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The thermal fatigue behavior of type 309 austenitic stainless steel was investigated by cyclic tests ranged from 100 • C to the maximum temperatures 800 and 900 • C. The microstructures of the specimens were characterized by optical microscope, scanning electron microscope and X-ray diffraction. With changing the maximum temperature from 800 to 900 • C, the stainless steel exhibits much lower strength, higher elongation and a decrease of fatigue life about 56.6%. After the thermal fatigue failure, the specimens show micro-void coalescence fractures caused by the creep during the holding period at the maximum temperatures, and the quasi-cleavage feature also appears in the case of the maximum temperature 800 • C. During the thermal fatigue processes, the cavities usually form at the grain and twin boundaries, facilitating the initiation and growth of cracks. Furthermore, the high-temperature oxidation produces oxides on the specimen surfaces and in the cracks, deteriorating thermal fatigue properties. With an increase in the maximum temperature, the enhanced synergetic effect of strength, grain size, creep and oxidation is responsible for the accelerated fatigue failure of 309 stainless steel during the thermal cycles.

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“…Moreover, it also outperforms Inception-V3 incredibly on a more extensive image classification dataset, including 350 million pictures and 17,000 classes. Since Xception's structure has the same parameters as Inception-V3, the performance gains are not due to increased capacity but to more efficient usage of the model's hyperparameters [16].…”

Section: Xceptionmentioning

confidence: 99%

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Ibrahim,

Ali,

Jawad

et al. 2023

Applied Sciences

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Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence.

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Fatigue Behavior of Stainless Steel Sheet Specimens at Extremely High Temperatures

Cited by 22 publications

References 7 publications

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Thermal Fatigue Characteristics of Type 309 Austenitic Stainless Steel for Automotive Manifolds

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

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