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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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In this paper, we present an entry-level COVID-19 stand-alone digitial signage player (CoSiP) which performs not only conventional digital signage functionalities but also fever detection, face mask wearing detection, and KI-pass QR code checking. The overall design of CoSiP is proposed, and implementation of a temperature checking algorithm using a low cost thermal sensor is elaborately presented. Through experiments over datasets and against a developed CoSiP device, it is shown that the fever detection, face mask wearing detection, KI-pass QR code checking as well as signage functionalities of the proposed CoSiP work properly and reliably.

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기사명 저자명 페이지 원문 목차
영상 생성적 데이터 증강을 이용한 딥러닝 기반 SAR 영상 선박 탐지 = Deep-learning based SAR ship detection with generative data augmentation 권형준, 정소미, 김성태, 이재석, 손광훈 p. 1-9
발열 감지, 안면 마스크 착용 검출, 전자출입명부 QR 코드 체킹을 지원하는 보급형 COVID-19 디지털 사이니지 플레이어 설계 및 구현 = Design and implementation of entry-level COVID-19 digital signage player supporting fever detection, face mask wearing detection and KI-pass QR code checking 쩐꾸억바오후이, 박상군, 정선태 p. 10-28
잡음제거 합성곱 신경망을 이용한 이미지 복원 방법 = Image restoration method using denoising CNN 김선재, 이정호, 이석환, 전동산 p. 29-38
앙상블 멀티태스킹 딥러닝 기반 경량 성별 분류 및 나이별 추정 = Light-weight gender classification and age estimation based on ensemble multi-tasking deep learning 쩐꾸억바오후이, 박종현, 정선태 p. 39-51
Intra-class local descriptor-based prototypical network for few-shot learning Xi-Lang Huang, Seon Han Choi p. 52-60
유사성 모델 기반의 수중 다중매체 통신 라우팅 프로토콜스택 선택방법 = A method for the selection of underwater multimedia routing protocol stack based on the similarity model 신동현, 김창화 p. 61-71
객체 추적을 위한 보틀넥 기반 Siam-CNN 알고리즘 = Bottleneck-based Siam-CNN algorithm for object tracking 임수창, 김종찬 p. 72-81
SARIMA 모델을 이용한 태양광 발전량 예측 연구 = A research of prediction of photovoltaic power using SARIMA model 정하영, 홍석훈, 전재성, 임수창, 김종찬, 박형욱, 박철영 p. 82-91
마약류 범죄의 사례 연구 및 문화콘텐츠를 활용한 예방과 인식 개선 방안 = Cases for narcotic crimes and solutions to prevent and improve the awareness with cultural contents 이연우 p. 92-102
소셜미디어 사용자가 만드는 동영상 광고효과에 관한 연구 : A study on the effectiveness of video advertisements generated by social media users : centered on video content type and information framework / 동영상 콘텐츠 유형 및 정보 프레임워크 중심으로 장녕, 김치용 p. 103-113
하이퍼리얼리즘형 웹드라마 <좋좋소>의 콘텐츠 특성 연구 = A study on content characteristics of hyperrealism web drama <Jot-Jot-So> 이준석, 정원식 p. 114-123
VR 콘텐츠가 재한 중국인 유학생 아증후군적 우울 상태에 미치는 영향 연구 : A study on the effect of VR content on sub-syndromatic depression of Chinese students in Korea : based on attention restoration theory (ART) / 주의력회복이론을 기반으로 정선요, 이연우, 김치용 p. 124-134
민화 DB를 위한 분류체계 설계 = Designing a classification system for Minhwa DB 최은진, 이영숙 p. 135-143

참고문헌 (42건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 D. Perpetuini, C. Filippini, D. Cardone, and A. Merla, “An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies,” International Journal of Environmental Research and Public Health, Vol. 18, Issue 6, 3286, 2021 미소장
2 J. Foster, A. B. Lloyd, and G. Havenith, “Non-Contact Infrared Assessment of Human Body Temperature,” The Journal Temperature Toolbox, Temperature, Vol. 18, Issue 4, pp. 306-319, 2021. 미소장
3 A Guide to KI-PASS, Korea Disease Control and Prevention Agency, 2021. 미소장
4 Digital signage, https://en.wikipedia.org/wiki/Digital_signage (accessed December 13, 2021). 미소장
5 Human body temperature, http://www.khna. or.kr/homecare_new/01_first/adult01.php (accessed December 13, 2021). 미소장
6 Thermography, https://en.wikipedia.org/wiki/Thermography (accessed December 13, 2021). 미소장
7 H.Y. Kim, “Technology Trend of MEMS Infrared Sensor,” Electrical & Electronic Materials, Vol. 24, Issue 1, pp. 23-33, 2011. 미소장
8 J. Hee, H. S. Cho, S. H. Park, and J. H. Lee, “A Study on Skin Temperature Distribution of the Human Body as Fundamental Data for Developing Heat Energy Harvesting Clothing,”Journal of Korea Sciety for Emotion and Sensibility, Vol. 14, Issue 3], pp. 435-444, 2011. 미소장
9 D.D. Pascoe and G. Fisher, “Comparison of Measuring Sites for the Assessment of Body Temperature,” Thermology International, Vol. 19, Issue 1, pp. 35-42, 2009. 미소장
10 IEC 80601-2-59:2017/DAMD 1 Medical Electrical Equipment —Part 2-59: Particular Requirements for the Basic Safety and Essential Performance of Screening Thermo- graphs for Human Febrile Temperature Screening—Amendment 1, https://www.iso.org/standard/83469. html (accessed December 13, 2021). 미소장
11 Do I need a Blackbody for Skin Temperature Screening?, https://www.flir.com/discover/public-safety/do-i-need-a-blackbody-forskin-temperature-screening/ (accessed December 13, 2021). 미소장
12 P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceeding 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1. pp. 1-8, 2001. 미소장
13 N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,”IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1. pp. 886-893, 2005. 미소장
14 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012. 미소장
15 S. Minaee, P. Luo, Z. Lin, and K. Bowyer, “Going Deeper Into Face Detection: A Survey,”arXiv Preprint, arXiv:2103.14983, 2021. 미소장
16 Google MLKit, https://developers.google.com/ml-kit (accessed December 13, 2021). 미소장
17 Google ML Kit Face Detection API, https://developers.google.com/ml-kit/vision/facedetection (accessed December 13, 2021). 미소장
18 V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, “BlazeFace:Sub-Millisecond Neural Face Detection on Mobile GPUs,” arXiv Preprint, arXiv:1907. 05047, 2019. 미소장
19 W. Liu, et al., “SSD: Single Shot MultiBox Detector,” European Conference on computer vision, pp 21-37, 2016. 미소장
20 A. Howard, et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv Preprint, arXiv: 1704.04861, 2017. 미소장
21 K.R. Singh, S.D. Kamble, S. M. Kalbande, and P. Fulzele, “A Review on COVID-19 Face Mask Detection Using CNN,” Journal of Pharmaceutical Research International, Vol. 33(35B), pp 39-45, 2021. 미소장
22 S. Sethi, M. Kathuria, and T. Kaushik, “Face Mask Detection Using Deep Learning: An Approach to Reduce Risk of Coronavirus Spread,” Journal of Biomedical Informatics, Vol. 120, pp. 1-12, 2021. 미소장
23 K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,”arXiv Preprint, arXiv:1512.03385v1, 2015. 미소장
24 S. Teboulbi, S. Messaoud, M.A. Hajjaji, and A. Mtibaa, “Real-Time Implementation of AIBased Face Mask Detection and Social Distancing Measuring System for COVID-19Prevention,” Scientific Programming, Vol. 2021, pp. 1-21, 2021. 미소장
25 M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 4510-4520, 2018. 미소장
26 K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Preprint, arXiv:1409.1556v6, 2014. 미소장
27 H. Lim, S. Ryoo, and H. Jung, “Face-Mask Detection with Micro processor,” Journal of Korea Institute of Information and Communication Engineering, Vol. 25, No. 3, pp. 490-493, 2021. 미소장
28 W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, and A.C. Berg, “Ann-Arbor-SSD: Single Shot MultiBox Detector,”arXiv Preprint, arXiv:1512.02325, 2018. 미소장
29 Rockchip RK3399 Datasheet, https://www. rockchip.fr/RK3399%20datasheet%20V1.8.pdf (accessed December 13, 2021). 미소장
30 Liming Hu, Facemaskdetector, https://github. com/liminghu/facemaskdetector (accessed December 13, 2021). 미소장
31 T. Ruffin, J. Steele, Y. Acquaah, N. Sharma, E. Sarku, R. Tesiero and B. Gokaraju, “Noninvasive Low Cost Fever Detection Systems,”SoutheastCon, 2021. 미소장
32 G. Jocher, YOLOv5, https://github.com/ultralytics/yolov5 (accessed December 13, 2021). 미소장
33 A. Somboonkaew, et al., “Mobile-Platform for Automatic Fever Screening System Based on Infrared Forehead Temperature,” Opto-Electronics and Communications Conference, 2017. 미소장
34 J. Ahlberg, N. Markus, and A. Berg, “Multi-Person Fever Screening Using a Thermal And A Visual Camera,” Svenska Sällskapet för Automatiserad Bildanalys, 2015. 미소장
35 M. F. Azwan, Mushahar, and N. Zaini, “Human Body Temperature Detection based on Thermal Imaging and Screening using YOLO Person Detection,” IEEE International Conference on Control System, Computing and Engineering, pp. 27-28, 2021. 미소장
36 OpenCV, https://opencv.org/ (accessed December 13, 2021). 미소장
37 M.J. Lee, Y.M. Kim, and Y.M. Lim. “Masked Face Temperature Measurement System Using Deep Learning,” Journal of Korea Multimedia Society, Vol. 24, No. 2, pp. 208-214, 2021. 미소장
38 Bar Code Scanning, https://developers.google. com/ml-kit/vision/barcode-scanning (accessed December 13, 2021). 미소장
39 TeraRanger Evo Thermal 33, https://www. terabee.com/shop/thermal-cameras/teraranger-evo-thermal-33/ (accessed December 13, 2021). 미소장
40 Application Note on Fever Detection with Evo Thermal 33, https://terabee.b-cdn.net/wpcontent/uploads/2020/11/Application-noteon-fever-detection-with-Evo-Thermal-33. pdf (accessed December 13, 2021). 미소장
41 Z. Wang, et al., “Masked Face Recognition Dataset and Application,” arXiv Preprint, arXiv:2003.09093v2, 2020. 미소장
42 Real-World-Masked-Face-Dataset, https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset (accessed December 13, 2021). 미소장

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