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Trends in Disaster Environment Multimodal Sensing Platforms

재난환경 멀티모달 센싱 플랫폼 기술 동향

  • S.M. Park ;
  • P.J. Park ;
  • K.H. Park ;
  • B.T. Koo
  • 박성모 (지능형센싱반도체연구실) ;
  • 박필재 (지능형센싱반도체연구실) ;
  • 박경환 (지능형센싱반도체연구실) ;
  • 구본태 (지능형반도체연구본부 )
  • Published : 2024.10.01

Abstract

For a quick and accurate response at a disaster site, technological solutions are essential to overcome limited visual information, secure environmental information, and identify victim locations. Research on artificial-intelligence-based semiconductors is being actively conducted to address existing challenges. In fact, new technologies combining various sensor signals are required to provide accurate and timely information at disaster sites. We examine existing disaster environment multimodal sensing technologies and discuss the status of disaster risk detection and monitoring technologies. Additionally, we present current problems and future directions of development.

Keywords

Acknowledgement

이 논문은 2022년도 행정안전부 재난 위험감지 및 모니터링 기술개발 사업의 지원을 받아 수행된 연구임(20018247)[No. 2022-20018247, 실내 요구조자 유무 판단을 위한 융합형 재실자 감지 장치 개발].

References

  1. 박성모 외, "스파이킹 신경망 기반 뉴로모픽 기술 동향," TTA, 188호, 2020. 3, pp. 28-33. 
  2. 박성모 외, "저전력 인공지능 반도체 기술 동향," IITP, 2007호, 2021. 7, pp. 6-10. 
  3. 박성모 외, "Processing-in-Memory 반도체 기술 동향," IITP, 2075호, 2022. 12, pp. 2-14. 
  4. 박성모 외, "프로세싱 인 메모리 기반 뉴로모픽 기술 동향," KIST 융합연구리뷰, 제8권, 2022. 8, pp. 29-39. 
  5. K. Ando et al., "BRein memory: A single-chip binary/ternary reconfigurable in-memory deep neural network accelerator achieving 1.4 TOPS at 0.6 W," IEEE J. Solid-State Circuits, vol. 53, no. 4, 2018, pp. 983-994. 
  6. M. Kang, S. K. Gonugondla, A. Patil, and N. R. Shanbhag, "A multi-functional in-memory inference processor using a standard 6T SRAM array," IEEE J. Solid-State Circuits, vol. 53, no. 2, 2018, pp. 642-655. 
  7. A. Biswas and A. P. Chandrakasan, "CONV-SRAM: An energy-efficient SRAM with in-memory dot-product computation for low-power convolutional neural networks," IEEE J. Solid-State Circuits, vol. 54, no. 1, Jan. 2019, pp. 217-230. 
  8. 노치원 외, "농연 환경 내 가시거리 확장을 위한 센서모듈 시제품 개발," ICROS, proceeding, 2017, pp. 185-186. 
  9. 임한신 외, "RGB와 IR 영상의 압축률에 따른 객체 탐지 신경망 성능 분석," 방송공학회논문지, vol. 26, no. 2, 2021, pp. 155-166. 
  10. P. Park et al., "An impulse radio (IR) radar SoC for through the-wall human-detection applications," ETRI J. vol. 42, no. 4, 2020, pp. 480-490. 
  11. Z. Ahmad et al., "Multi-modality helps in crisis management: An attention-based deep learning approach of leveraging text for image classification," Expert Syst. Applicat., Vol. 195, June 2022, pp. 1-11. 
  12. Q. Chen et al., "Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media," in IEEE Int. Conf. Smart Comput. Proceed., (Bologna, Italy), 2020, pp. 232-237. 
  13. P. Cheng et al., "A Deep Learning-Enhanced Multi-Modal Sensing Platform for Robust Human Object Detection and Tracking in Challenging Environments," vol. 12, 3423, 2023, pp. 1-19. 
  14. 장지호 외, "멀티모달 센서 기반 실외 경비로봇 기술 개발 현황," 전자통신동향분석, 제37권 제1호, 2022, 1-9. 
  15. O.G. Ajayi et al., "Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images," Smart Agricultural Technol., vol. 5, 100231, 2023, pp. 1-17. 
  16. 박필재 외, "실내 요구조자 유무 판단을 위한 융합형 재실자 감지 장치 개발," 연차보고서, 2022. 11.