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	Integral time real-time adjust method based on radiation calibration 期刊论文 OAI收割 JOURNAL OF INFRARED AND MILLIMETER WAVES, 2014, 卷号: 33, 期号: 3, 页码: 297-302 作者: Li Man-Liang; Wu Qin-Zhang; Xia Mo; Wu Shu-Wen 收藏 \| 浏览/下载：35/0 \| 提交时间：2015/07/10 integral time real-time adjust radiation calibration dynamic range radiation characteristics measurement system
	Trajectory tracking control for mobile robot based on the fuzzy sliding mode (EI CONFERENCE) 会议论文 OAI收割 10th World Congress on Intelligent Control and Automation, WCICA 2012, July 6, 2012 - July 8, 2012, Beijing, China Xie M.-J.; Li L.-T.; Wang Z.-Q. 收藏 \| 浏览/下载：30/0 \| 提交时间：2013/03/25 The trajectory tracking control problem of the uncertain mobile robot with nonholonomic constraints is analyzed. Sliding mode control is presented based on the kinematics models analysis. Switching function of sliding model control is designed according to back-stepping method. Trending law control is selected to improve the system dynamic performance. In order to solve the constant speed problem caused by conventional trending law control fuzzy control is used to adjust trending speed in the real time. The simulation results demonstrate that the fuzzy sliding mode controller improves the rapidity of trajectory tracking and reduces the tracking error and the chattering of the control output. 2012 IEEE.
	Application of improved UKF algorithm in initial alignment of SINS (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011, August 8, 2011 - August 10, 2011, Zhengzhou, China Su W. X. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 In order to improve the initial alignment accuracy and convergence rate of the SINS system proposed the improved UKF algorithm (AUKF) based on the Unscented Kalman Filter (UKF). Noise statistical characteristics are mostly unknown in real systems when it was effected by the initial value errors and dynamic model errors AUKF algorithm can real-time adjust the covariance of the state vector and observation vector and balance the right ratio of the state information and observation information in the filter results thereby improving the system performance. The experimental results show: The Improved UKF Algorithm enhances the convergence speed and alignment accuracy effectively. 2011 IEEE.
	Real-time motive vehicle detection with adaptive background updating model and HSV colour space (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China Rong-Hui Z.; Bai Y.; Hong-guang J.; Chen T. 收藏 \| 浏览/下载：72/0 \| 提交时间：2013/03/25 In the transportation monitor system we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately the arithmetic proposed in the paper can suppress shadow availably we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter detect motive vehicle accurately and satisfy real-time motive vehicle tracking. 2009 SPIE. and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering
	Real-time quality control on a smart camera (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Xiao C.; Zhou H.; Li G.; Hao Z. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 A smart camera is composed of a video sensing high-level video processing communication and other affiliations within a single device. Such cameras are very important devices in quality control systems. This paper presents a prototyping development of a smart camera for quality control. The smart camera is divided to four parts: a CMOS sensor a digital signal processor (DSP) a CPLD and a display device. In order to improving the processing speed low-level and high-level video processing algorithms are discussed to the embedded DSP-based platforms. The algorithms can quickly and automatic detect productions' quality defaults. All algorithms are tested under a Matlab-based prototyping implementation and migrated to the smart camera. The smart camera prototype automatic processes the video data and streams the results of the video data to the display devices and control devices. Control signals are send to produce-line to adjust the producing state within the required real-time constrains.