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학술지 Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques
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박현, 박대헌, 김세한
Electronics, v.10 no.16, pp.1-22
21HU1500, 축산질병 예방 및 통제 관리를 위한 ICT 기반의 지능형 스마트 안전 축사 기술 개발(이월과제), 김세한
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.
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
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