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4篇 您的检索式：作者名="Songwen PEI"

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		题名	作者	年代	出处	被引量
	1	Novel Software Automated Testing System Based on J2EE显示文摘	Songwen Pei Baifeng Wu Kun Zhu Qiang Yu	2007	Tsinghua Science & Technology2007，，：	1
	2	Novel Software Automated Testing System Based on J2EE显示文摘	PEI Songwen WU Baifeng	2007	TSING HUA SCIENCE AND TECHNOLOGY2007，，：	1
	3	Novel Software Automated Testing System Based on J2EE显示文摘	PEI Songwen WU Baifeng ZHU Kun	2007	Tsinghua Science and Technology2007，12，1：	1
	4	Attention-based efficient robot grasp detection network显示文摘To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic information.Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a collision.This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers.We design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp configurations.AEGDN achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,respectively.The inference speed reaches 43.5 frames per second with only about 1.2×10^(6)parameters.The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well.Codes are available at http://gffzz188fe103f8f1460asukv0vpfqvqnw6ob0.ffgz.tsg.suse.edu.cn/robvincen/robot_gradet.	Xiaofei QIN Wenkai HU Chen XIAO Changxiang HE Songwen PEI Xuedian ZHANG	2023	Frontiers of Information Technology & Electronic Engineering2023，24，10：	0

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