Detection of Public Information Sign in Airport Terminal Based on Multi-scales Spatio-temporal Vision Information

2008 ◽  
Author(s):  
Gao Qingji ◽  
Yue Yue ◽  
Yang Guoqing
Keyword(s):  
Public Information ◽  
Airport Terminal ◽  
Temporal Vision ◽  
Spatio Temporal ◽  
Multi Scales

scholarly journals (Im-)material flow analysis framework implementation on repair and reuse initiatives

2020 ◽  
Vol 108 (5-6) ◽  
pp. 509 ◽  
Author(s):  
Julie Gobert ◽  
Romain Allais
Keyword(s):  
Material Flow ◽  
Flow Analysis ◽  
Material Flow Analysis ◽  
Spatiotemporal Analysis ◽  
Temporal Analysis ◽  
Local Initiatives ◽  
Spatio Temporal ◽  
Stakeholder Network ◽  
Multi Scales

This research aims at understanding better the nature of stakeholders’ resistance to and interest in repair and reuse. In fact, the authors assume that in the future waste management could be less centralized using a network of territorialized initiatives based on repair and reuse activities with high social and environmental values. Such system innovation requires tools and methods to support analysis and facilitate decision-making in multi-stakeholders, multi-scales systems. The framework for spatiotemporal analysis of territorial projects considers a project’s stakeholder network and the way they mobilize resources. These resources may be tangible or intangible, brought by individuals, organizations or even the territory. This communication focuses on the implementation of such an analysis in the community of communes Coeur de Savoie, to understand how local initiatives emerge and on which interactions and resources they are based. This paper proposes feedback on the implementation of the spatio-temporal analysis in one case study (Coeur de Savoie), and provides insights to build new networks promoting reuse and repair.

scholarly journals Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe

2020 ◽  
Author(s):  
Mohamed R. Ibrahim ◽  
James Haworth ◽  
Aldo Lipani ◽  
Nilufer Aslam ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Demographic Data ◽  
Public Information ◽  
International Travel ◽  
Contact Tracing ◽  
Information Campaigns ◽  
School Closing ◽  
Public Events ◽  
Short And Long Term ◽  
Spatio Temporal

AbstractModelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational LSTM-Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represent the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

scholarly journals Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246120
Author(s):  
Mohamed R. Ibrahim ◽  
James Haworth ◽  
Aldo Lipani ◽  
Nilufer Aslam ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Demographic Data ◽  
Public Information ◽  
International Travel ◽  
Contact Tracing ◽  
Information Campaigns ◽  
School Closing ◽  
Public Events ◽  
Short And Long Term ◽  
Spatio Temporal

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

scholarly journals A cortical locus for post adaptation facilitation in spatio-temporal vision

2010 ◽  
Vol 7 (9) ◽  
pp. 386-386 ◽  
Author(s):  
P. Bex ◽  
K. Langley ◽  
J. Cass
Keyword(s):  
Temporal Vision ◽  
Spatio Temporal
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