Sorry, you need to enable JavaScript to visit this website.

Other applications of machine learning (MLR-APPL)

Social Media Analytics for Crisis Response


Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response.

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Document Year:
Cite

Document Files

Shamanth_thesis.pdf

(529)

Subscribe

[1] , "Social Media Analytics for Crisis Response", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/541. Accessed: Apr. 22, 2019.
@article{541-15,
url = {http://sigport.org/541},
author = { },
publisher = {IEEE SigPort},
title = {Social Media Analytics for Crisis Response},
year = {2015} }
TY - EJOUR
T1 - Social Media Analytics for Crisis Response
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/541
ER -
. (2015). Social Media Analytics for Crisis Response. IEEE SigPort. http://sigport.org/541
, 2015. Social Media Analytics for Crisis Response. Available at: http://sigport.org/541.
. (2015). "Social Media Analytics for Crisis Response." Web.
1. . Social Media Analytics for Crisis Response [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/541

Social Media Analytics for Crisis Response


Crises and situations of mass emergency such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally this process has been restricted to the information collected by first responders on the ground in the affected region or official agencies such as local governments involved in the response.

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Document Year:
Cite

Document Files

Shamanth_Kumar_Thesis_slides_2015.pptx

(453)

Subscribe

[1] , "Social Media Analytics for Crisis Response", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/539. Accessed: Apr. 22, 2019.
@article{539-15,
url = {http://sigport.org/539},
author = { },
publisher = {IEEE SigPort},
title = {Social Media Analytics for Crisis Response},
year = {2015} }
TY - EJOUR
T1 - Social Media Analytics for Crisis Response
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/539
ER -
. (2015). Social Media Analytics for Crisis Response. IEEE SigPort. http://sigport.org/539
, 2015. Social Media Analytics for Crisis Response. Available at: http://sigport.org/539.
. (2015). "Social Media Analytics for Crisis Response." Web.
1. . Social Media Analytics for Crisis Response [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/539

Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations


This work proposes a learning-based energy management policy that takes into consideration the trade-off between the depth-of-discharge (DoD) and the lifetime of batteries. The impact of DoD on the energy management policy is often neglected in the past due to the inability to model its effect on the marginal cost per battery usage. In this work, a novel battery cost evaluation method that takes into consideration the DoD of each battery usage is proposed, and is utilized to devise the day-ahead energy management policy using reinforcement learning and linear value-function approximations.

Paper Details

Authors:
Ting-Hsing Wang
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP2015v2.pdf

(447)

Subscribe

[1] Ting-Hsing Wang, "Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/454. Accessed: Apr. 22, 2019.
@article{454-15,
url = {http://sigport.org/454},
author = {Ting-Hsing Wang },
publisher = {IEEE SigPort},
title = {Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations},
year = {2015} }
TY - EJOUR
T1 - Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations
AU - Ting-Hsing Wang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/454
ER -
Ting-Hsing Wang. (2015). Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations. IEEE SigPort. http://sigport.org/454
Ting-Hsing Wang, 2015. Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations. Available at: http://sigport.org/454.
Ting-Hsing Wang. (2015). "Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations." Web.
1. Ting-Hsing Wang. Learning-Based Energy Management Policy with Battery Depth-of-Discharge Considerations [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/454

Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images

Paper Details

Authors:
Alan Bovik
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSipPresentation.pdf

(445)

Subscribe

[1] Alan Bovik, "Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/396. Accessed: Apr. 22, 2019.
@article{396-15,
url = {http://sigport.org/396},
author = {Alan Bovik },
publisher = {IEEE SigPort},
title = {Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images},
year = {2015} }
TY - EJOUR
T1 - Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images
AU - Alan Bovik
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/396
ER -
Alan Bovik. (2015). Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images. IEEE SigPort. http://sigport.org/396
Alan Bovik, 2015. Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images. Available at: http://sigport.org/396.
Alan Bovik. (2015). "Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images." Web.
1. Alan Bovik. Generalizing a Closed-Form Correlation Model of Oriented Bandpass Natural Images [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/396

Pages