A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors.
01 Jan 2018-pp 255-264
About: This article is published in International Conference on E-Business and Telecommunication Networks.The article was published on 2018-01-01 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Topology (electrical circuits).
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TL;DR: An encoder–decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain and outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman- modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-L STM, and Convolutional L STM (Conv-LSTM), with considerable gains.
Abstract: The development of Internet-of-Things (IoT) technology promotes the advances of grain condition detection and analysis systems. Temperature monitoring is a main element to maintain grain quality, and effective control of grain temperature is crucial to safe storage of grain. In this article, an encoder–decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain. Considering that the points on the gradient direction of the temperature surface have a great influence on the temperature of the target point, the Sobel operator is used to extract the local characteristics of the target point. In addition, considering the correlation structure in the sensory data, the attention mechanism is used to extract the global features of the target point. The extracted spatial features are fed into long short-term memory (LSTM) networks to obtain the long-term state information of spatial factors. LSTM unit and convolutional neural network are used to encode the spatial features of the target points. Taking meteorological factors as the external input of the decoder, temporal attention mechanism and LSTM unit are used to complete the decoding process and realize the prediction of grain temperature in the future. The results with real grain storage data show that the proposed model outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman-modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-LSTM, and convolutional LSTM (Conv-LSTM), with considerable gains.
9 citations
TL;DR: In this article , a space-time data prediction algorithm based on knowledge distillation (KD-ST) is proposed to compress teacher network to multi-student networks, and a weight transfer strategy is adopted for saving training time.
Abstract: In industrial Internet of Things (IIoT), the space–time data prediction algorithm is considered as one of the key technologies for supporting real-time monitoring and intelligent control. However, the complexity of existing algorithms is too high to be deployed on edge devices with limited computational capability. To solve this problem, a novel space–time data prediction algorithm based on knowledge distillation (KD-ST) is proposed to compress teacher network to multi-student networks. Specifically, generative adversarial network (GAN) discrimination and teacher outlier elimination (TOE) are developed to minimize the discrepancy between disparate networks and avoid training errors. Furthermore, a weight transfer strategy is adopted for saving training time. Experiment results demonstrate that compared with the state-of-the-art T-GCN, the proposed Transfer-LSTM improves the real-time response speed by 17.15 times, and the proposed Transfer-1DCNN further improves the real-time response speed by 30.20 times.