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원홍,김동하 한국고분자학회 2021 한국고분자학회 학술대회 연구논문 초록집 Vol.46 No.2
Surface-enhanced Raman scattering utilizing plasmonic metals combined with TMD materials has received much attention in bio-applications due to highly enhanced detection sensitivity. Plasmonic metal NPs have superior optical properties due to the resonant oscillation of stimulated electrons on the surface, i.e. LSPR. 2D MoS2 nanosheet has been regarded as a NIR absorbing agent because of the high absorption in the NIR region. In addition, it is reported to exhibit enhanced Raman signals due to the electromagnetic field enhancement by abundant free electrons. In this study, we report unconventional SERS-based sensors consisting of ultrathin 2D MoS₂ NS decorated Au nanoworms. Au NW was prepared by dopamine based self-assembly of Au NPs. To enhance the plasmon coupling effects, the gap between Au NW and MoS₂ was further controlled by regulating the dopamine spacer. The correlation between the configuration of the hybrid NWs and the SERS performance was comprehensively investigated.
피드백 진동의 심층학습 기반 원단 분류 기술의 시험 및 평가
원홍인,정승현,장진석,윤종필 대한임베디드공학회 2021 대한임베디드공학회논문지 Vol.16 No.2
This paper presents the test and evaluation results of the fabric classification technology using deep learning of feedback vibration occurring on fabric surfaces. Ten fabrics composed of different materials were selected for the classification test. To build a database for the design of an artificial intelligence model, feedback vibration measurement equipment with functions of fixed tension, contact load, and contact velocity control, was constructed and feedback vibration data on each fabric surface were collected under the same measurement conditions. Then, training and validation datasets were created with the collected feedback vibration data, and a deep learning architecture with convolutional neural networks was designed in the consideration of data characteristics. A deep learning model development program was established and the fabric classification model was derived with the training and validation dataset. In the end, a test system including an embedded system with the developed model was constructed in order to test and evaluate the performance of the fabric classification model. Results of the fabric classification test were summarized and analyzed by means of the confusion matrix. Finally, the performance of the integrated system was confirmed to have an accuracy of more than ninety percent in fabric classification with the developed model.