DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gul, Osman | ko |
dc.contributor.author | Kim, Jeongnam | ko |
dc.contributor.author | Kim, Kyuyoung | ko |
dc.contributor.author | Kim, Hye Jin | ko |
dc.contributor.author | Park, Inkyu | ko |
dc.date.accessioned | 2024-06-18T15:00:11Z | - |
dc.date.available | 2024-06-18T15:00:11Z | - |
dc.date.created | 2024-06-18 | - |
dc.date.created | 2024-06-18 | - |
dc.date.created | 2024-06-18 | - |
dc.date.issued | 2024-06 | - |
dc.identifier.citation | ADVANCED MATERIALS TECHNOLOGIES, v.9, no.12 | - |
dc.identifier.issn | 2365-709X | - |
dc.identifier.uri | http://hdl.handle.net/10203/319854 | - |
dc.description.abstract | Electronic skin (e-skin) is an emerging technology with promising applications in various fields, including human-machine interfaces, prosthetics, and robotics. Soft and flexible sensors are vital components for the e-skin that can mimic human skin's sensing capabilities. Among soft sensors, liquid-metal-based sensors have gained attention owing to their unique properties, such as high electrical conductivity, stretchability, and elasticity. Herein, a novel approach is presented that enables multidirectional pressure sensing with a machine-learning approach from the transient response of the liquid-metal-based soft pressure sensor for the e-skins. In this study, a soft sensor is developed that utilizes liquid metal and has an array of microchannels on a dome-shaped structure to detect pressures from multiple directions. The transient response from six microchannels of the sensor is used as the input for a convolutional neural network (CNN) to predict the direction (classification accuracy of 99.1%) and magnitude (regression error of 20.13%) of the applied pressures in real time. Finally, a potential application of the developed liquid-metal-based soft sensor as a human-machine interface device is demonstrated by using it to control an RC model car through multidirectional predictions (pressure direction and magnitude) through machine learning in real time.,Liquid-metal-based microchannels are integrated into the dome-shaped structure to create a multidirectional soft pressure sensor. The proposed multidirectional pressure sensor employs machine learning to identify both the direction and magnitude of multidirectional pressures. Real-time machine learning-based detection of the direction and magnitude of multidirectional pressures from the proposed sensor is utilized as a human-machine interface device. image, | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | Liquid-Metal-Based Soft Pressure Sensor and Multidirectional Detection by Machine Learning | - |
dc.type | Article | - |
dc.identifier.wosid | 001202550400001 | - |
dc.identifier.scopusid | 2-s2.0-85190295522 | - |
dc.type.rims | ART | - |
dc.citation.volume | 9 | - |
dc.citation.issue | 12 | - |
dc.citation.publicationname | ADVANCED MATERIALS TECHNOLOGIES | - |
dc.identifier.doi | 10.1002/admt.202302134 | - |
dc.contributor.localauthor | Park, Inkyu | - |
dc.contributor.nonIdAuthor | Kim, Jeongnam | - |
dc.contributor.nonIdAuthor | Kim, Kyuyoung | - |
dc.contributor.nonIdAuthor | Kim, Hye Jin | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | 3D printing | - |
dc.subject.keywordAuthor | human-machine interaction | - |
dc.subject.keywordAuthor | liquid metal | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | soft sensor | - |
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