An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques

Sarrionandia, Xabier; Nieves, Javier; Bravo, Beñat; Pastor-López, Iker; Bringas, Pablo García

doi:10.3390/jmmp7010017

Cited by 5 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical data augmentation refers to the process of synthesizing additional training samples from existing data to increase the size of the dataset. This technique is widely used in medical imaging to overcome the limitations of small, unbalanced, and annotated datasets [29]. One popular method of data augmentation in medical imaging is image transformations.…”

Section: Deep Methods For Asd Diagnosis By Facial Imagementioning

confidence: 99%

Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI

Alam,

Rashid,

Faizabadi

et al. 2023

Technologies

View full text Add to dashboard Cite

The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model.

show abstract

Section: Deep Methods For Asd Diagnosis By Facial Imagementioning

confidence: 99%

Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI

Alam,

Rashid,

Faizabadi

et al. 2023

Technologies

View full text Add to dashboard Cite

show abstract

New Architecture for Real-Time Image Computing Using Parallel Processing Based on DSP/FPGA

Oukaira,

Karime,

Roberge

et al. 2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

View full text Add to dashboard Cite

Influence of Nb Reinforcement on the Wear Behavior of Spheroidal Graphite Cast Iron

Yakut

2024

Lubricants

View full text Add to dashboard Cite

Spheroidal graphite cast iron, which is commonly used in a variety of applications, is subject to sliding wear and tear during operation. This damage can be prevented by increasing its strength value. In this study, 0%, 0.191%, and 0.304% niobium-reinforced spheroidal graphite cast irons were produced. Specimens for hardness, compression, and abrasion tests were produced in accordance with the standards for the tests of the sand mold cast specimens. In order to compare the results of the tests, test specimens reinforced with 0.191% and 0.304% niobium, as well as 0% (unreinforced), were also produced. The hardness and compression strength of the niobium-reinforced and unreinforced specimens were tested in accordance with the standards. In addition, wear tests were carried out at 5 N, 10 N, and 15 N loads for 0%, 0.191%, and 0.304% niobium reinforcements, respectively. In the hardness tests, the highest measured value was observed in the sample reinforced with 0.304% niobium, with an average of 272 HB. Since Nb is an element with high hardness, the hardness values of the samples increased with the increase in Nb reinforcement. With an average value of 1411 MPa, the niobium-reinforced sample with 0.191% niobium reinforcement exhibited the highest compression strength. When analyzing the results of the compression tests, an increasing trend was observed with increasing reinforcement ratio, as was the case with the results of the hardness tests. In the wear tests, the changes in wear volume, wear rate, and friction coefficients were examined at loads of 5 N, 10 N, and 15 N. When the test specimens with the same reinforcement ratio were examined, it was found that the wear volume loss values increased with the increase in load. As the wear resistance increases with increasing hardness value, it becomes more difficult to detach the particles from the wear surfaces. The wear volume of the samples, therefore, decreases as the hardness value increases. At the end of the experimental study, a microstructural analysis of the surfaces subjected to sliding wear was carried out. It was observed that wear marks and pits had formed on the surfaces subjected to sliding wear.

show abstract

An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques

Cited by 5 publications

References 45 publications

Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI

Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI

New Architecture for Real-Time Image Computing Using Parallel Processing Based on DSP/FPGA

Influence of Nb Reinforcement on the Wear Behavior of Spheroidal Graphite Cast Iron

Contact Info

Product

Resources

About