scholarly journals Regional Opportunities for Carbon Dioxide Capture and Storage in China: A Comprehensive CO2 Storage Cost Curve and Analysis of the Potential for Large Scale Carbon Dioxide Capture and Storage in the People?s Republic of China

2009 ◽  
Author(s):  
Robert T. Dahowski ◽  
Xiaochun Li ◽  
Casie L. Davidson ◽  
Ning Wei ◽  
James J. Dooley
2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Hendra Amijaya

Carbon dioxide capture and storage (CSS) is alternative of reducing atmospheric emissions of CO2. The concepts of CO2 storage refer to the injection of carbon dioxide in dense form into aquifers, which basically must meet several conditions. Three types of geological formations that can be used for the geological storage of CO2 are oil and gas reservoirs, deep saline formations and unmineable coal beds. Indonesia has 60 Tertiary basins, however that great precautions must be taken for selecting particular sedimentary basin in Indonesia for carbon dioxide storage because of high possibility of leakage and the need to find deep formations as CO2 host since the geothermal gradient is high. One possibility to find proper basins is by selected “mature” basin as the detailed geological conditions are well known. Candidates are are North East Java or South Sumatra Basins. Keywords: Carbon dioxide capture, storage, emission, basin.


2009 ◽  
Vol 1 (1) ◽  
pp. 2849-2856 ◽  
Author(s):  
R.T. Dahowski ◽  
X. Li ◽  
C.L. Davidson ◽  
N. Wei ◽  
J.J. Dooley ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.


2012 ◽  
Vol 199 (12) ◽  
pp. 1642-1651 ◽  
Author(s):  
Suttichai Assabumrungrat ◽  
Janewit Phromprasit ◽  
Siriporn Boonkrue ◽  
Worapon Kiatkittipong ◽  
Wisitsree Wiyaratn ◽  
...  

10.1142/9209 ◽  
2021 ◽  
Author(s):  
Shinichi Nakao ◽  
Ziqiu Xue

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