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	A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information 期刊论文 OAI收割 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 卷号: 8, 期号: 3, 页码: 2077-2087 作者: Sun, Yao; Hu, Yunfeng; Zhang, Hui; Wang, Feiyue; Chen, Hong \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2023/11/17 Combined CO2 estimation model deterioration factor OBD-independent parallel supervision system vehicle CO2 emissions
	Application of coagulation/flocculation in oily wastewater treatment: A review 期刊论文 OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 卷号: 765, 页码: - 作者: Zhao, Chuanliang; Zhou, Junyuan; Yan, Yi; Yang, Liwei; Xing, Guohua \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2021/12/23 Coagulants/flocculants Coagulation mechanism Demulsification Cost estimation Combined technology
	Combined carrier phase and code phase passive radiation source localisation method 期刊论文 OAI收割 IET RADAR SONAR AND NAVIGATION, 2020, 卷号: 14, 期号: 1, 页码: 147-155 作者: Li, Youyang; Wang, Xue; Lu, Xiaochun \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2020/11/09 Kalman filters position control direction-of-arrival estimation Global Positioning System nonlinear filters time-of-arrival estimation code phase passive radiation source localisation method high-precision positioning method arrival methods carrier phase-based positioning process position tracking times higher accuracy TDOA method positioning accuracy traditional positioning method combined carrier phase time 4 0 s to 10 0 s
	Combined estimation for multi-measurements of branching ratio 期刊论文 OAI收割 CHINESE PHYSICS C, 2015, 卷号: 39, 期号: 10, 页码: 103001 作者: Xiao-Xia; L.; L. Xiao-Rui; Zhu YS(朱永生); Yong-Sheng 收藏 \| 浏览/下载：36/0 \| 提交时间：2016/04/18 branching ratio combined estimation likelihood function Bayesian method systematic error
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：63/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).