ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques
Cited 2 time in scopus Download 15 time Share share facebook twitter linkedin kakaostory
저자
박현, 박대헌, 김세한
발행일
202108
출처
Electronics, v.10 no.16, pp.1-22
ISSN
2079-9292
출판사
MDPI
DOI
https://dx.doi.org/10.3390/electronics10161958
협약과제
21HU1500, 축산질병 예방 및 통제 관리를 위한 ICT 기반의 지능형 스마트 안전 축사 기술 개발(이월과제), 김세한
초록
In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.
키워드
Anomaly detection, Environmental monitoring, OneM2M, RNN, Smart farming
KSP 제안 키워드
Environmental factor(E-factor), Environmental monitoring, Environmental sensor, Growth conditions, Livestock farms, Pig house, Predictive model, Real-Time, Recurrent Neural Network(RNN), Smart farming, analysis of data
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
저작자 표시 (CC BY)