원문정보
국제문화기술진흥원
International Journal of Advanced Culture Technology(IJACT)
Volume 11 Number 1
2023.03
pp.325-331
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.
목차
Abstract
1. INTRODUCTION
2. RESEARCH CONTENT
3. DESIGN OF 1-D CNN AND LSTM MIXED NEURAL NETWORK MODEL USING TIME SERIES DATA
3.1 Design of Mixed Neural Network Model of CNN and LSTM
3.2 Data Collection
3.3 Data Preprocessing
3.4 Implementation of the Model
4. CONCLUSION
REFERENCES
1. INTRODUCTION
2. RESEARCH CONTENT
3. DESIGN OF 1-D CNN AND LSTM MIXED NEURAL NETWORK MODEL USING TIME SERIES DATA
3.1 Design of Mixed Neural Network Model of CNN and LSTM
3.2 Data Collection
3.3 Data Preprocessing
3.4 Implementation of the Model
4. CONCLUSION
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보