LSTM을 이용한 HILS 데이터 판정 자동화 모델 개발에 대한 연구
- Title
- LSTM을 이용한 HILS 데이터 판정 자동화 모델 개발에 대한 연구
- Other Titles
- A study on automation model for determining HILS data using LSTM
- Author
- 이형철
- Keywords
- 순환 신경망; 장기 단기 기억; 심층 학습; 데이터 분류 및 판정; RNN; LSTM; Deep Learning; HILS; Data Classification & Determination
- Issue Date
- 2020-11
- Publisher
- 한국자동차공학회
- Citation
- 2020년 한국자동차공학회 추계학술대회 및 전시회, page. 317-323
- Abstract
- Due to the advancement of autonomous driving technology and the paradigm shift toward electric vehicle, the functions of vehicle have become diverse and complex. For effective function development and verification, car manufacturers are developing functions using the HILS system, but the complexity and diversity of functions make repeated tests inevitable. As a result, human resources are consumed in analyzing numerous test data and determining results. To overcome this problem, This paper proposed an automation model for determining HILS data using LSTM network, one of artificial networks. For model training and verification, we used 2,175 multivariate time series HILS data. Trained model classify testcase number of input data and determine Pass/Fail of input data. The performance of the proposed model was validated by performance index(accuracy, precision, recall, F1-score).
- URI
- https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10519280https://repository.hanyang.ac.kr/handle/20.500.11754/172716
- ISSN
- 2713-7171
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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