Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator
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 reconstru...
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8062 |
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| author | Kiagus Aufa Ibrahim Prima Asmara Sejati Panji Nursetia Darma Akira Nakane Masahiro Takei |
| author_facet | Kiagus Aufa Ibrahim Prima Asmara Sejati Panji Nursetia Darma Akira Nakane Masahiro Takei |
| author_sort | Kiagus Aufa Ibrahim |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ψ</mi></mrow><mo>̿</mo></mover></mrow><mrow><mi mathvariant="normal">G</mi><mi mathvariant="normal">A</mi><mi mathvariant="normal">N</mi></mrow></msup></mrow></semantics></math></inline-formula> in different positions have higher accuracy as compared to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mfenced open="⟨" close="⟩" separators="|"><mrow><msup><mrow><mi mathvariant="bold-sans-serif">σ</mi></mrow><mrow><mo>*</mo></mrow></msup></mrow></mfenced></mrow><mrow><mi mathvariant="normal">E</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">T</mi></mrow></msup></mrow></semantics></math></inline-formula>. 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. |
| first_indexed | 2024-03-10T21:36:20Z |
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| language | English |
| last_indexed | 2024-03-10T21:36:20Z |
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| spelling | doaj.art-f405782008c347aa979999465faf72b12023-11-19T15:02:10ZengMDPI AGSensors1424-82202023-09-012319806210.3390/s23198062Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration SeparatorKiagus Aufa Ibrahim0Prima Asmara Sejati1Panji Nursetia Darma2Akira Nakane3Masahiro Takei4Department of Mechanical Engineering, Division of Fundamental Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, JapanDepartment of Mechanical Engineering, Division of Fundamental Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, JapanDepartment of Mechanical Engineering, Division of Fundamental Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, JapanSanritsu Machine Industry Co., Ltd., Chiba 263-0002, JapanDepartment of Mechanical Engineering, Division of Fundamental Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, JapanThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mover accent="true"><mrow><mi>ψ</mi></mrow><mo>̿</mo></mover></mrow><mrow><mi mathvariant="normal">G</mi><mi mathvariant="normal">A</mi><mi mathvariant="normal">N</mi></mrow></msup></mrow></semantics></math></inline-formula> in different positions have higher accuracy as compared to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mfenced open="⟨" close="⟩" separators="|"><mrow><msup><mrow><mi mathvariant="bold-sans-serif">σ</mi></mrow><mrow><mo>*</mo></mrow></msup></mrow></mfenced></mrow><mrow><mi mathvariant="normal">E</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">T</mi></mrow></msup></mrow></semantics></math></inline-formula>. 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.https://www.mdpi.com/1424-8220/23/19/8062metal particle detectionelectrical impedance tomographygenerative adversarial network |
| spellingShingle | Kiagus Aufa Ibrahim Prima Asmara Sejati Panji Nursetia Darma Akira Nakane Masahiro Takei Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator Sensors metal particle detection electrical impedance tomography generative adversarial network |
| title | Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator |
| title_full | Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator |
| title_fullStr | Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator |
| title_full_unstemmed | Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator |
| title_short | Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator |
| title_sort | metal particle detection by integration of a generative adversarial network and electrical impedance tomography gan eit for a wet type gravity vibration separator |
| topic | metal particle detection electrical impedance tomography generative adversarial network |
| url | https://www.mdpi.com/1424-8220/23/19/8062 |
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