Earticle

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

키워드

저자

• Gi-Seok Lee [ CEO, Uclab Inc, Gwangju, Korea ]
• Sang-Hyun Lee [ Associate Professor, Department of Computer Engineering, Honam University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700