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
This study designs and implements a machine learning-based temperature prediction and safety diagnosis system for internal combustion engine vehicles. The system features a web-based interface for enhanced user accessibility and employs various algorithms, including Random Forest, Linear Regression, and Support Vector Regression, to improve prediction accuracy. Long Short-Term Memory (LSTM) was also utilized for comparative performance analysis. In the first phase, temperature data from Gwangju Metropolian City, provided by the Korea Meteorological Administration from 2020 to 2024, was extracted for initial experiments, serving as training and validation data for the prediction model. The second phase involved measuring the engine heating temperature of a Hyundai YF Sonata gasoline vehicle over 30 minutes, with the collected data used to evaluate the machine learning model's performance. Additionally, an IoT sensor-integrated web monitoring environment was developed, enabling real-time data collection and providing users with an intuitive interface to enhance temperature prediction. This system offers real-time monitoring capabilities, effectively improving vehicle body safety. The research lays foundational data for exploring the applicability of various vehicle models and is expected to advance body safety management systems.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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