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.
피인용 0
초록·키워드
Current noise-evaluation practices for military firing ranges are based on weapon caliber: small firearms (less than 20 mm) apply A-weighting with a +12 dB impulse correction, whereas large firearms (20 mm or more) apply C-weighting with a +18 dB impulse correction. However, in combined firing ranges where both small and large firearms are used, this caliber-based approach does not adequately reflect the distinct acoustic characteristics of each weapon type. In particular, practice rounds from large firearms typically exhibit acoustic patterns similar to those of small firearms, thus rendering accurate classification challenging under the existing system. Hence, this study developed a machine learning-based classification model and verified its ability to classify noise more accurately by ammunition type. The Random Forest model achieved a high accuracy of 98.7 %, and when practice-round data were reclassified, 90.9 % were categorized as having small-firearm acoustic characteristics, thus confirming that large-firearm practice rounds resemble small firearms in terms of sound. These findings enhance the objectivity of noise analysis and noise-impact assessment for military firing ranges and are beneficial for improving noise-evaluation criteria in combined firing ranges in the future.
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) : I410-151-26-02-094660049
