Ibrahim, Kiagus Aufa and Sejati, Prima Asmara and Darma, Panji Nursetia and Nakane, Akira and Takei, Masahiro (2023) Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator. Sensors, 23 (19). ISSN 14248220
Full text not available from this repository. (Request a copy)Abstract
The minor copper (Cu) particles among major aluminum (Al) particles have been detected by means of an integration of a generative adversarial network and electrical impedance tomography (GAN-EIT) for a wet-type gravity vibration separator (WGS). This study solves the problem of blurred EIT reconstructed images by proposing a GAN-EIT integration system for Cu detection in WGS. GAN-EIT produces two types of images of various Cu positions among major Al particles, which are (1) the photo-based GAN-EIT images, where blurred EIT reconstructed images are enhanced by GAN based on a full set of photo images, and (2) the simulation-based GAN-EIT images. The proposed metal particle detection by GAN-EIT is applied in experiments under static conditions to investigate the performance of the metal detection method under single-layer conditions with the variation of the position of Cu particles. As a quantitative result, the images of detected Cu by GAN-EIT (Formula presented.) in different positions have higher accuracy as compared to (Formula presented.). In the region of interest (ROI) covered by the developed linear sensor, GAN-EIT successfully reduces the Cu detection error of conventional EIT by 40 while maintaining a minimum signal-to-noise ratio (SNR) of 60 dB. In conclusion, GAN-EIT is capable of improving the detailed features of the reconstructed images to visualize the detected Cu effectively. © 2023 by the authors.
Item Type: | Article |
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Additional Information: | Cited by: 3; All Open Access, Gold Open Access, Green Open Access |
Uncontrolled Keywords: | Electric impedance; Electric impedance measurement; Electric impedance tomography; Generative adversarial networks; Image reconstruction; Image segmentation; Integration; Signal to noise ratio; Aluminum particle; Copper cu; Cu detections; Electrical impe dance tomography (EIT); Integration systems; Metal particle; Metal particle detection; Network impedance; Particles detection; Reconstructed image; Image enhancement |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Vocational School |
Depositing User: | Sri JUNANDI |
Date Deposited: | 04 Nov 2024 07:30 |
Last Modified: | 04 Nov 2024 07:30 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10503 |