DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Gun-Hee | ko |
dc.contributor.author | Park, Jin-Kwan | ko |
dc.contributor.author | Byun, Junyoung | ko |
dc.contributor.author | Yang, Jun Chang | ko |
dc.contributor.author | Kwon, Se Young | ko |
dc.contributor.author | Kim, Chobi | ko |
dc.contributor.author | Jang, Chorom | ko |
dc.contributor.author | Sim, Joo Yong | ko |
dc.contributor.author | Yook, Jong-Gwan | ko |
dc.contributor.author | Park, Steve | ko |
dc.date.accessioned | 2020-05-14T08:20:16Z | - |
dc.date.available | 2020-05-14T08:20:16Z | - |
dc.date.created | 2019-12-30 | - |
dc.date.created | 2019-12-30 | - |
dc.date.created | 2019-12-30 | - |
dc.date.created | 2019-12-30 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.citation | ADVANCED MATERIALS, v.32, no.8 | - |
dc.identifier.issn | 0935-9648 | - |
dc.identifier.uri | http://hdl.handle.net/10203/274197 | - |
dc.description.abstract | Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S-11) spectrum, and the applied pressure changes the magnitude of the S-11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S-11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability. | - |
dc.language | English | - |
dc.publisher | WILEY-V C H VERLAG GMBH | - |
dc.title | Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System | - |
dc.type | Article | - |
dc.identifier.wosid | 000502609400001 | - |
dc.identifier.scopusid | 2-s2.0-85076780231 | - |
dc.type.rims | ART | - |
dc.citation.volume | 32 | - |
dc.citation.issue | 8 | - |
dc.citation.publicationname | ADVANCED MATERIALS | - |
dc.identifier.doi | 10.1002/adma.201906269 | - |
dc.contributor.localauthor | Park, Steve | - |
dc.contributor.nonIdAuthor | Park, Jin-Kwan | - |
dc.contributor.nonIdAuthor | Kim, Chobi | - |
dc.contributor.nonIdAuthor | Jang, Chorom | - |
dc.contributor.nonIdAuthor | Sim, Joo Yong | - |
dc.contributor.nonIdAuthor | Yook, Jong-Gwan | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | electronic skin | - |
dc.subject.keywordAuthor | LC passive resonators | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | parallel signal processing | - |
dc.subject.keywordAuthor | pressure sensors | - |
dc.subject.keywordPlus | PLASTICITY | - |
dc.subject.keywordPlus | MECHANISMS | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | SKIN | - |
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