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Sensory Data Prediction Using spatiotemporal correlation and LSTM Recurrent Neural Network

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Instrumentation 2019年第3期6卷 10-17页

作者： Tongxin SHU Department of Electrical and Computer Engineering The University of British ColumbiaVancouverCanada V6T 1Z4

The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring *** process of data gathering within the WSN is significant in terms of reporting the environmental ***,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected ***,the collected data may become inaccurate or *** on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor *** Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more *** is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.

关键词： spatiotemporal correlation LSTM Recurrent Neural Network time-series prediction

Differential Privacy Trajectory Protection Method Based on spatiotemporal correlation

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国际计算机前沿大会会议论文集 2020年第2期 151-166页

作者： Kangkang Dou Jian Liu Jiangsu Automation Research Institute Lianyungang 222061China Vivo Mobile Communications(Shenzhen)Co Ltd.Shenzhen 518000China

Location-based services provide service and convenience,while causing the leakage of track *** existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequence,the user’s original trajectory sequence,and the published trajectory *** they are susceptible to noise filtering attacks using filtering *** view of this problem,a differential privacy trajectory protection method based on spatiotemporal correlation is proposed in this *** this method,the concept of correlation function was introduced to establish the correlation constraint of release track sequence,and the least square method was used to fit the user’s original track and the overall direction of noise sequence to construct noise candidate *** ensured that the added noise sequence has spatiotemporal correlation with the user’s original track sequence and release track ***,it effectively resists attackers’denoising attacks,and reduces the risk of trajectory privacy ***,comparative experiments were carried out on the real data *** experimental results show that this method effectively improves the privacy protection effect and the data availability of the release track,and it also has better practicability.

关键词： Location-Based Services Trajectory privacy protection spatiotemporal correlation Cross-correlation constraints Noise candidate set

Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction

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Digital Communications and Networks 2022年第3期8卷 343-350页

作者： Jihua Ye Shengjun Xue Aiwen Jiang School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.

关键词： Multi-step traffic flow prediction Graph convolutional network External factors Attentional encoder network spatiotemporal correlation

Sound speed profiles in high spatiotemporal resolution using multigrid three-dimensional variational method:a coastal experiment off northern Shandong Peninsula

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Journal of Oceanology and Limnology 2023年第1期41卷 57-71页

作者： Guangchao HOU Jingsheng ZHAI Qi SHAO Yanling ZHAO Wei LI Guijun HAN Kangzhuang LIANG School of Marine Science and Technology Tianjin UniversityTianjin 300072China Tianjin Key Laboratory for Oceanic Meteorology Tianjin 300074China The PLA 31010 Units Beijing 100081China

It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic ***,conventional observation methods cannot obtain high-resolution ***,S SPs are complex and changeable in time and space,especially in coastal *** proposed a new space-time multigrid three-dimensional variational method with weak constraint term(referred to as STC-MG3DVar)to construct high-precision spatiotemporal resolution SSPs in coastal areas,in which sound velocity is defined as the analytical variable,and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the *** spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method,and the multi-scale information of sound velocity observations from long waves to short waves can be successively *** weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate *** verify the accuracy of the STC-MG3DVar,SSPs observations and CTD observations(temperature observations,salinity observations)are obtained from field experiments in the northern coastal area of the Shandong *** average root mean square error(RMSE)of the STC-MG3DVar-constructed SSPs is 0.132 m/s,and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term(ST-MG3DVar)by 10.14%and over the spatial multigrid 3DVar with weak constraint term(SC-MG3DVar)by 44.19%.With the advantage of the constraint term and the spatiotemporal correlation information,the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SCMG3DVar in constructing high-precision spatiotemporal re solution SSPs.

关键词： space-time multigrid 3DVar sound speed profiles temperature salinity spatiotemporal correlation multiscale

Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China

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Journal of Integrative Agriculture 2016年第11期15卷 2647-2657页

作者： HUANG Qing WANG Li-min CHEN Zhong-xin LIU Hang Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth

关键词： growth condition meteorological factors remote sensing spatiotemporal correlation winter wheat HuangHuai-Hai(HHH) Plain region China