Location–Aware RBAC Based on Spatial Feature Models and Realistic Positioning

2015 ◽  
pp. 131-147
Author(s):  
Philipp Marcus ◽  
Lorenz Schauer ◽  
Claudia Linnhoff–Popien
Keyword(s):  
Feature Models ◽  
Spatial Feature ◽  
Location Aware

DeSmoothGAN: Recovering Details of Smoothed Images via Spatial Feature-wise Transformation and Full Attention

2020 ◽  
Author(s):  
Yifei Huang ◽  
Chenhui Li ◽  
Xiaohu Guo ◽  
Jing Liao ◽  
Chenxu Zhang ◽  
...  
Keyword(s):  
Spatial Feature ◽  
Full Attention

ReliableBox: Secure and Verifiable Cloud Storage with Location-aware Backup

2021 ◽  
pp. 1-1
Author(s):  
Tao Jiang ◽  
Wenjuan Meng ◽  
Xu Yuan ◽  
Liangmin Wang ◽  
Jian-Hua Ge ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Location Aware

Multi-step real-time wind speed prediction based on convolution memory network

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Spatial Correlation ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Spatial Association ◽  
Spatial Feature ◽  
Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

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