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您的检索式：作者名="Junmo Kwon"
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| 1 | Multi-directionally wrinkle-able textile OLEDs for clothing-type displays显示文摘A clothing-type wearable display can be utilized in fashion,bio-healthcare,and safety industries as well as smart textiles for the internet of things(IoTs)and wearable devices.In response to this trend,we demonstrate a textile display that can endure the active movements of a human body.It can be applied to any kind of textile,and is durable against conditions such as rain,sweat,and washing.As a key technology for realizing the multi-directional wrinkle-able textile display,we fabricated a stress-lowering textile platform with an ultrathin planarization layer replicated from the flat surface of glass.An elastomeric strain buffer for reducing mechanical stress is also inserted into the textile platform.Here,organic light-emitting diodes(OLEDs)with red,green and blue color,thin film transistors(TFTs)fabricated at a low temperature below 150℃,and a washable encapsulation layer blocking both gas and liquid were demonstrated on the textile platform. | Seungyeop Choi Woosung Jo Yongmin Jeon Seonil Kwon Jeong Hyun Kwon Young Hyun Son Junmo Kim Jun Hong Park Hyuncheol Kim Ho Seung Lee Minwoo Nam Eun Gyo Jeong Jeong Bin Shin Taek-Soo Kim Kyung Cheol Choi | 2020 | npj Flexible Electronics2020，4，1： | 3 |
| 2 | Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making显示文摘Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence(AI).If the AI understands the mental state behind a user’s decision,it can learn more appropriate decisions even in unclear situations.We introduce the Brain-AI Closed-Loop System(BACLoS),a wireless interaction platform that enables human brain wave analysis and transfers results to AI to verify and enhance AI decision-making.We developed a wireless earbud-like electroencephalography(EEG)measurement device,combined with tattoo-like electrodes and connectors,which enables continuous recording of high-quality EEG signals,especially the error-related potential(ErrP).The sensor measures the ErrP signals,which reflects the human cognitive consequences of an unpredicted machine response.The AI corrects or reinforces decisions depending on the presence or absence of the ErrP signals,which is determined by deep learning classification of the received EEG data.We demonstrate the BACLoS for AIbased machines,including autonomous driving vehicles,maze solvers,and assistant interfaces. | Joo Hwan Shin Junmo Kwon Jong Uk Kim Hyewon Ryu Jehyung Ok SJoon Kwon Hyunjin Park Tae-il Kim | 2022 | npj Flexible Electronics2022，6，1： | 0 |
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