인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
학술저널
DBpia Top 5%
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Drone-based wildfire surveillance has gained significant attention due to its ability for rapid deployment and real-time data collection in remote and hard-to-access areas. However, current surveillance activities are limited to specific locations and times, making it challenging to comprehensively monitor all wildfire-prone regions. This study addresses the optimization problem of enhancing the operational efficiency of drone-based wildfire surveillance. Specifically, it focuses on optimizing the hourly operational schedules of drones during the planning period, considering the fluctuating surveillance demands over time. Drones are deployed from drone stations to perform surveillance for a certain duration and must return to the station for battery recharging before resuming operations. Therefore, it is crucial to effectively plan the flight and charging schedules for multiple drones to maximize the satisfaction of surveillance demands. To this end, a mixed-integer programming (MIP) model was developed. A case study was conducted focusing on the Gangwon-do region, including Inje, Chuncheon, and Hongcheon. The results demonstrate that the proposed model can be effectively applied to real-world wildfire surveillance scenarios, significantly improving both surveillance efficiency and resource utilization. This study provides valuable insights into advancing drone-based wildfire surveillance systems and offers practical strategies for efficient resource management.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-25-02-092095950
