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	Prescribed-Time Adaptive Fuzzy Control for Pneumatic Artificial Muscle-Actuated Parallel Robots With Input Constraints 期刊论文 OAI收割 IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2024, 卷号: 32, 期号: 4, 页码: 2039-2051 作者: Diao, Shuzhen; Liu, Gendi; Liu, Zhuoqing; Zhou, Lu; Sun, Wei \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2024/07/03 Parallel robots Actuators Target tracking Sun Muscles Transient analysis Safety Pneumatic artificial muscles (PAMs) multiple input constraints parallel robots prescribed-time control
	A New Composite Spiral Scanning Approach for Beaconless Spatial Acquisition and Experimental Investigation of Robust Tracking Control for Laser Communication System with Disturbance 期刊论文 OAI收割 IEEE PHOTONICS JOURNAL, 2020, 卷号: 12, 期号: 6, 页码: 13 作者: Zhang, Min; Li, Bo; Tong, Shoufeng \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2021/03/23 Spirals Laser beams Target tracking Uncertainty Heuristic algorithms Actuators Laser modes Optical communication beaconless spatial acquisition composite spiral scanning dynamic tracking assembly multiple-axis control
	A novel track initiation method for track splitting and merging 会议论文 OAI收割 OCEANS 2016 - Shanghai, Shanghai, China, April 10-13, 2016 作者: Li DD(李冬冬); Zhang Y(张瑶); Lin Y(林扬); Liu J(刘健) 收藏 \| 浏览/下载：29/0 \| 提交时间：2016/08/10 multiple target tracking track initiation splitting merging multiple hypotheses tracking modified logic-based method clutters
	基于视觉的靶精密定位与多光束引导技术研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014 作者: 张鹏程收藏 \| 浏览/下载：111/0 \| 提交时间：2015/09/02 显微视觉标定靶的跟踪与定位多光斑跟踪与引导视觉反馈控制束靶耦合 Microscopic Camera Calibration Target Tracking and Positioning Multiple Laser Beams Tracking and Guiding Visual Feedback Control Beam and Target Alignment
	多目标跟踪及其在航拍视频中的应用学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院大学, 2013 作者: 史信楚收藏 \| 浏览/下载：98/0 \| 提交时间：2015/09/02 多目标跟踪数据关联秩一张量近似场景和运动上下文关系航 multiple target tracking data association rank-1 tensor approximation scene and motion context aerial video analysis
	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：60/0 \| 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).
	Adaptive Image Segmentation based on Fast Thresholding and Image Merging (EI CONFERENCE) 会议论文 OAI收割 16th International Conference on Artificial Reality and Telexistence - Workshops, ICAT 2006, November 29, 2006 - December 1, 2006, Hangzhou, China 作者: Zhang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：14/0 \| 提交时间：2013/03/25 Image segmentation is the first essential and important step of low level vision. This paper proposes a novel algorithm for adaptive image segmentation it can be applied in many conditions based on thresholding technique and segments merging according to their characteristics combine with spatial position. Our earlier work of getting the entire information of the histogram could help choose the multiple thresholds. However including complex target segmented. We describe the algorithm in detail and perform simulation experiments. The computation based on pixels can fully parallel processing to save time. 2006 IEEE. not all the peaks of the histogram correspond to obvious structural unit in the image. Spatial information must be involved. This paper also suggests subjoining segments matching for video image tracking. They will give great help to image segmentation. The proposed algorithm can meet the real-time requirement and lead to higher segmentation accuracy some types of texture can also be segmented well