The compressive strength of the cement-silica fume blends with 5mass%, 10mass%, 20mass% and 30mass% of silica fume and water to binder ratio of 0.28, 0.32 and 0.36 from three days to ninety days were investigated. The...The compressive strength of the cement-silica fume blends with 5mass%, 10mass%, 20mass% and 30mass% of silica fume and water to binder ratio of 0.28, 0.32 and 0.36 from three days to ninety days were investigated. The reaction degree of silica fume was calculated from the Q4 silica tetrahedron, which was used as a probe obtained from 29 Si solid state nuclear magnetic resonance analysis. The fl at of compressive strength after 28 days disappeared for blended cement with inereasing reaction degree of silica fume. The compressive strength of the blended cement pastes approached that of P.I. cement pastes after 56 days and exceeded that after 90 days. The addition of silica fume and the w/b ratio of blends are both critical to the reaction degree of silica fume. The appropriate addition of silica fume, high silica fume reaction degree and low w/b ratio are benefi cial to the compressive strength of the cement-silica fume blends.展开更多
In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal...In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.展开更多
基金Funded by the National Basic Research Program of China(No.2009CB623100)
文摘The compressive strength of the cement-silica fume blends with 5mass%, 10mass%, 20mass% and 30mass% of silica fume and water to binder ratio of 0.28, 0.32 and 0.36 from three days to ninety days were investigated. The reaction degree of silica fume was calculated from the Q4 silica tetrahedron, which was used as a probe obtained from 29 Si solid state nuclear magnetic resonance analysis. The fl at of compressive strength after 28 days disappeared for blended cement with inereasing reaction degree of silica fume. The compressive strength of the blended cement pastes approached that of P.I. cement pastes after 56 days and exceeded that after 90 days. The addition of silica fume and the w/b ratio of blends are both critical to the reaction degree of silica fume. The appropriate addition of silica fume, high silica fume reaction degree and low w/b ratio are benefi cial to the compressive strength of the cement-silica fume blends.
基金co-supported by the National Natural Science Foundation of China (Nos.61005028,61175032,and 61101222)
文摘In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.
