Ultraviolet Light-Responsive Photorheological Fluid for Sensors and Actuators Realized by Phosphorescence Effects and LSTM RNN

A photo-rheological fluid (PRF) is a smart fluid which exhibits different viscosity under UV irradiation. A PRF is comprehensively presented in this work, with particular focus on its responses under UV off/on conditions. The isomeric conversion from SP to MC and vice versa under UV off and on, resp...

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Published in대한금속재료학회지 Vol. 59; no. 5; pp. 346 - 353
Main Authors Min Young Cho, Suman Timilsina, Jong Wook Roh, Walter Commerell, Ho Geun Shin, Yong-nam Kwon, Ji Sik Kim
Format Journal Article
LanguageKorean
Published 대한금속재료학회 05.04.2021
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Abstract A photo-rheological fluid (PRF) is a smart fluid which exhibits different viscosity under UV irradiation. A PRF is comprehensively presented in this work, with particular focus on its responses under UV off/on conditions. The isomeric conversion from SP to MC and vice versa under UV off and on, respectively, showed unequal rates of transformation. As a result, a complex non-linear hysteretic response was observed. To be used indifferent types of sensors and actuators which can exploit its rheological properties, it is essential the PRF have linearized hysteresis behavior. To minimize the asymmetric non-linear hysteresis characteristics under UV on and off conditions, the well-known long-lasting phosphor SAO (SrAl 2 O 4 :Eu 2+ , Dy 3+ ) was incorporated. The incorporation of SAO in the PRF improved the linearity of the PRF response, although the conversion rate was not identical under UV off/on conditions. The SAO particles were observed to settle over time due to phase splitting, undermining the usefulness of the SAO-PRF composite. Instead of improving the PRF response by further adjusting the PRF composite, a software approach based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNN) was employed to model and compensate the asymmetric non-linear hysteresis response, ensuring the realization of sensors and actuators that exploit PRF as hardware. (Received March 2, 2021; Accepted March 5, 2021)
AbstractList A photo-rheological fluid (PRF) is a smart fluid which exhibits different viscosity under UV irradiation. A PRF is comprehensively presented in this work, with particular focus on its responses under UV off/on conditions. The isomeric conversion from SP to MC and vice versa under UV off and on, respectively, showed unequal rates of transformation. As a result, a complex non-linear hysteretic response was observed. To be used indifferent types of sensors and actuators which can exploit its rheological properties, it is essential the PRF have linearized hysteresis behavior. To minimize the asymmetric non-linear hysteresis characteristics under UV on and off conditions, the well-known long-lasting phosphor SAO (SrAl 2 O 4 :Eu 2+ , Dy 3+ ) was incorporated. The incorporation of SAO in the PRF improved the linearity of the PRF response, although the conversion rate was not identical under UV off/on conditions. The SAO particles were observed to settle over time due to phase splitting, undermining the usefulness of the SAO-PRF composite. Instead of improving the PRF response by further adjusting the PRF composite, a software approach based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNN) was employed to model and compensate the asymmetric non-linear hysteresis response, ensuring the realization of sensors and actuators that exploit PRF as hardware. (Received March 2, 2021; Accepted March 5, 2021)
Author Walter Commerell
Ho Geun Shin
Suman Timilsina
Ji Sik Kim
Min Young Cho
Jong Wook Roh
Yong-nam Kwon
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  fullname: Yong-nam Kwon
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  fullname: Ji Sik Kim
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Snippet A photo-rheological fluid (PRF) is a smart fluid which exhibits different viscosity under UV irradiation. A PRF is comprehensively presented in this work, with...
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StartPage 346
SubjectTerms linearization
LSTM RNN
phosphorescence
photorheological fluid
smart fluid
Title Ultraviolet Light-Responsive Photorheological Fluid for Sensors and Actuators Realized by Phosphorescence Effects and LSTM RNN
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