This study analyzes the possibility of applying the acoustic emission method (AE) and the Kolmogorov-Sinai (K-S) metric entropy phenomenon in determining the structural changes that take place within the EN AW 7020 aluminum alloy. The experimental part comprised of a static tensile test carried out on aluminum alloy samples, and the simultaneous recording of the acoustic signal generated inside the material. This signal was further processed and diagrams of the effective electrical signal value (RMS) as a function of time were drawn up. The diagrams obtained were applied on tensile curves. A record of measurements carried out was used to analyze the properties of the material, applying a method based on Kolmogorov-Sinai (K-S) metric entropy. For this purpose, a diagram of metric entropy as a function of time was developed for each sample and applied on the corresponding course of stretching. The results of studies applying the AE and the K-S metric entropy method show that K-S metric entropy can be used as a method to determine the yield point of the material where there are no pronounced yield points.
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http://dx.doi.org/10.3390/ma13061386 | DOI Listing |
Front Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, R1173, Baltimore, MD, 21202, USA.
The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking.
View Article and Find Full Text PDFEntropy (Basel)
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School of Computer Science, Minnan Normal University, Zhangzhou 363000, China.
With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Optical Coherence Tomography (OCT) is a crucial imaging modality for diagnosing and monitoring retinal diseases. However, the accurate segmentation of fluid regions and lesions remains challenging due to noise, low contrast, and blurred edges in OCT images. Although feature modeling with wide or global receptive fields offers a feasible solution, it typically leads to significant computational overhead.
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