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	Potential risks of CO2 removal project based on carbonate pump to marine ecosystem 期刊论文 OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 卷号: 862, 页码: 14 作者: Duan, Liqin; Song, Jinming; Li, Xuegang; Yuan, Huamao; Zhuang, Wen \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2023/11/16 Carbon sequestration Carbonate pump Mineral addition Particle effect
	Improvement on fluidization and reduction of ultrafine CuO powders with the assistance of iron microspheres 期刊论文 OAI收割 POWDER TECHNOLOGY, 2022, 卷号: 411, 页码: 10 作者: Li, Da; Xue, Xue; Li, Jun; Li, Hongzhong; Zhu, Qingshan \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2023/02/24 Group C powders Fluidization Particle addition Interactions
	Effects of biochar addition on evaporation in the five typical Loess Plateau soils 期刊论文 OAI收割 CATENA, 2018, 卷号: 162, 页码: 29-39 作者: Wang, Tongtong; Stewart, Catherine E; Sun, Cengceng; Wang, Ying; Zheng, Jiyong \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2018/03/22 Biochar Soil Evaporation Biochar Particle Size Loess Plateau Soils Biochar Addition Amount
	Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文 OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者: Wang D.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 In feature-level fusion recognition system the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general there are two main missions. One is improving the recognition correct rate as soon as possible the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions this paper presents a more rational and accurate optimization Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.