Open Access

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Qian Dang¹, Huafeng Zhang¹, Bo Zhao², Yanwen He², Shiming He^3,*, Hye-Jin Kim⁴
Journal on Internet of Things, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jiot.2019.05804
Abstract As the development of smart grid and energy internet, this leads to a significant increase in the amount of data transmitted in real time. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation of electricity data which is generated by human’s actions and feelings, we build a low-rank electricity data matrix where the row is time and the column is user. Inspired by matrix decomposition, we divide the low-rank electricity data… More >

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Open Access

ARTICLE

Yong Xie¹, Zui Tao^2,*, Wen Shao³, John J. Qu⁴, Hai Huan³, Chuanyang Tian³
Journal on Internet of Things, Vol.1, No.1, pp. 9-16, 2019, DOI:10.32604/jiot.2019.05859
Abstract Radiation cross-calibration is an effective method to check and verify the accuracy and stability of sensor measurements. Satellites with high radiation accuracy are used to calibrate satellites with low radiation accuracy. In order to ensure the reliability of the radiation cross-calibration method, we propose to obtain the gain and offset of the GaoFen-1 satellite by linear regression after the radiation cross-calibration of the satellite with low precision and compare with the official coefficient. Finally, we get the relationship between the error in radiation cross-calibration results and side swing angle. The linear correction coefficients of each band are: 0.618, 0.625, 0.512… More >

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Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²
Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866
Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More >

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1570

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Haoran Zhu¹, Demin Gao^1,2,*, Shuo Zhang¹
Journal on Internet of Things, Vol.1, No.1, pp. 25-31, 2019, DOI:10.32604/jiot.2019.05897
Abstract Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index… More >

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ARTICLE

Qirun Wang^1,2, Fujian Zhu^3,4, Yan Leng^3,4, Yongjun Ren^3,4,*, Jinyue Xia⁵
Journal on Internet of Things, Vol.1, No.1, pp. 33-39, 2019, DOI:10.32604/jiot.2019.05898
Abstract With the rapid development of E-commerce and E-government, there are so many electronic records have been produced. The increasing number of electronic records brings about storage difficulties, the traditional electronic records center is difficult to cope with the current fast growth requirements of electronic records storage and management. Therefore, it is imperative to use cloud storage technology to build electronic record centers. However, electronic records also have weaknesses in the cloud storage environment, and one of them is that once electronic record owners or managers lose physical control of them, the electronic records are more likely to be tampered with… More >

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