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	县域村镇建设资源环境承载能力评价及人口合理规模测算——以江西省永丰县为例期刊论文 OAI收割资源科学, 2020, 卷号: 42, 期号: 7, 页码: 1249 作者: 马定国; 戴雄祖; 羊金凤; 王传胜 \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2021/03/16 village and town development resource and environmental carrying capacity suitability classification development intensity optimal population size Yongfeng County 村镇建设资源环境承载能力适宜性分类开发强度人口合理规模永丰县
	How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China 期刊论文 OAI收割 SUSTAINABILITY, 2018, 卷号: 10, 期号: 11, 页码: 15 作者: Yang, Jun; Jin, Gui; Huang, Xianjin; Chen, Kun; Meng, Hao \| 收藏 \| 浏览/下载：40/0 \| 提交时间：2019/05/23 urban land factor substitution theory optimal intensity MDM multi-objective decision Wuhan urban agglomeration
	How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China 期刊论文 OAI收割 SUSTAINABILITY, 2018, 卷号: 10, 期号: 11, 页码: 15 作者: Yang, Jun; Jin, Gui; Huang, Xianjin; Chen, Kun; Meng, Hao \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2019/05/23 urban land factor substitution theory optimal intensity MDM multi-objective decision Wuhan urban agglomeration
	How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China 期刊论文 OAI收割 SUSTAINABILITY, 2018, 卷号: 10, 期号: 11, 页码: 15 作者: Yang, Jun; Jin, Gui; Huang, Xianjin; Chen, Kun; Meng, Hao \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2019/05/23 urban land factor substitution theory optimal intensity MDM multi-objective decision Wuhan urban agglomeration
	Analysis of product return rate and price competition in two supply chains 期刊论文 OAI收割 OPERATIONAL RESEARCH, 2018, 卷号: 18, 期号: 2, 页码: 469-496 作者: Zheng, Yanyan; Shu, Tong; Wang, Shouyang; Chen, Shou; Lai, Kin Keung \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2018/07/30 Supply chains Betrand competition Competition intensity Product return rate Competition structures Optimal strategy
	Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts? CNKI期刊论文 OAI收割 2014 作者: QIN Xiaohao; MU Mu \| 收藏 \| 浏览/下载：1/0 \| 提交时间：2024/12/18 adaptive observation,tropical cyclone,intensity forecast,conditional nonlinear optimal perturbation
	Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts? 期刊论文 OAI收割 ADVANCES IN ATMOSPHERIC SCIENCES, 2014, 卷号: 31, 期号: 2, 页码: 252-262 作者: Qin Xiaohao; Mu Mu 收藏 \| 浏览/下载：39/0 \| 提交时间：2015/06/11 adaptive observation tropical cyclone intensity forecast conditional nonlinear optimal perturbation
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.