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		题名	作者	年代	出处	被引量
	1	Control of a 3-RRR Planar Parallel Robot Using Fractional Order PID Controller显示文摘3-RRR planar parallel robots are utilized for solving precise material-handling problems in industrial automation applications.Thus,robust and stable control is required to deliver high accuracy in comparison to the state of the art.The operation of the mechanism is achieved based on three revolute(3-RRR)joints which are geometrically designed using an open-loop spatial robotic platform.The inverse kinematic model of the system is derived and analyzed by using the geometric structure with three revolute joints.The main variables in our design are the platform base positions,the geometry of the joint angles,and links of the 3-RRR planar parallel robot.These variables are calcula ted based on Cayley-Menger determinants and bilateration to det ermine the final position of the platform when moving and placing objects.Additionally,a proposed fractional order proportional integral derivative(FOPID)is optimized using the bat optimization algorithm to control the path tracking of the center of the 3-RRR planar parallel robot.The design is compared with the state of the art and simulated using the Matlab environment to validate the effectiveness of the proposed controller.Furthermore,real-time implementation has been tested to prove that the design performance is practical.	Auday Al-Mayyahi Ammar A.Aidair Chris Chatwin	2020	International Journal of Automation and computing2020，17，6：	1
	2	On shadow elimination after moving region segmentation based on different threshold selection strategies显示文摘	Bhargav Kumar Mitra Rupert Young Chris Chatwin	2007	Optics and Lasers in Engineering2007，45，：	1
	3	Biometric iris recognition system using a fast and robust iris localization and alignment procedure显示文摘	Ganeshan Balaji Theckedath Dhananjay Young Rupert Chatwin Chris	2006	Optics and Lasers in Engineering2006，44，1：	1
	4	No-reference image quality assessment based on AdaBoost_BP neural network in wavelet domain显示文摘Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment(NR-IQA) method based on the Ada Boost BP neural network in the wavelet domain(WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics(NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the laboratory of image and video engineering(LIVE) database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence of the database and the relatively high operation efficiency of this method.	YAN Junhua BAI Xuehan ZHANG Wanyi XIAO Yongqi CHATWIN Chris YOUNG Rupert BIRCH Phil	2019	Journal of Systems Engineering and Electronics2019，30，2：	1

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