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심층 신경망을 이용한 승차감 평가 모델 개발 및 검증 A Study on the Development and Verification of a Ride Comfort Evaluation Model Using Deep Neural Networks

이동필, 김병삼
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  • URLhttp://db.koreascholar.com/Article/Detail/444598
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한국기계기술학회지 (韓國機械技術學會誌)
제27권 제2호 (2025.04)
pp.332-336
한국기계기술학회 (Korean Society of Mechanical Technology)
초록

Ride comfort is a key factor in vehicle performance, yet traditional evaluations often rely on subjective methods, leading to inconsistencies. This study presents a deep neural network (DNN)-based model trained on real-world driving data to objectively assess ride comfort. The model’s accuracy is validated using RMS, VDV, and Crest Factor based on ISO 2631. Results show that the DNN effectively captures nonlinear vibration characteristics and offers reliable predictions. This highlights the potential of AI in improving ride comfort assessment.

키워드
승차감 평가심층 신경망진동분석인공지능 기반 모델차량주행실험 Ride Comfort EvaluationDeep Neural NetworkVibration AnalysisAI-Bssed ModelVehicle Driving Test
목차
Abstract
1. 서 론
2. 연구 방법
    2.1 데이터 수집 및 전처리
    2.2 심층 신경망 모 델 설계
    2.3 진동평가
3. 실험 결과 및 분석
    3.1 승차감 평가
    3.2 성능 평가 및 비교
4. 결 론
References
저자
  • 이동필(Department of Mechanical Engineering, Wonkwang University) | Dong-Pil Lee
  • 김병삼(Department of Mechanical Engineering, Wonkwang University) | Byoung-Sam Kim Corresponding author