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.
초록·키워드
ABSTRACT Biological function often depends on complex mechanisms of a dynamic, time‐variant nature. An example is certain bat species (horseshoe bats—Rhinolophidae) that use intricate pinna musculatures to execute a variety of pinna deformations. While prior work has indicated the potential significance of these motions for sensory information encoding, it remains unclear how the complex time‐variant pinna geometries could be controlled to enhance sensory performance. To address this issue, this work has investigated deep neural network models as digital twins for biomimetic pinnae. The networks were trained to predict the acoustic impacts of the deformed pinna geometries. A total of three network architectures have been evaluated for this purpose using physical numerical simulations (boundary element method) as ground truth. The networks predicted the acoustic beampattern function from pinna shape or even directly from the states of actuators that were used to deform the pinna shapes in simulation. Inserting prior knowledge in the form of beam‐shaped basis functions did not improve network performance. The ability of the networks to produce beampattern predictions with low computational effort (in about three milliseconds each) should lend itself readily to supporting learning methods such as deep reinforcement learning that require many such functional evaluations.
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.
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