diff options
Diffstat (limited to 'Master/texmf-dist/doc/latex/hitszthesis/tex/reference.tex')
| -rw-r--r-- | Master/texmf-dist/doc/latex/hitszthesis/tex/reference.tex | 80 |
1 files changed, 80 insertions, 0 deletions
diff --git a/Master/texmf-dist/doc/latex/hitszthesis/tex/reference.tex b/Master/texmf-dist/doc/latex/hitszthesis/tex/reference.tex new file mode 100644 index 00000000000..2dded075769 --- /dev/null +++ b/Master/texmf-dist/doc/latex/hitszthesis/tex/reference.tex @@ -0,0 +1,80 @@ +% !TEX root = ../main.tex + +% 参考文献 +% 文献个数小于99 +\begin{thebibliography}{99} +\addcontentsline{toc}{chapter}{参考文献} + +% 每条参考文献均需用<\bibitem{}>引出， +% 花括号里的内容为此条参考文献的标签label， +% 可用<\upcite{}>和<\cite{}>引用。 + +\bibitem{bibc1} 焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社，1991. +\bibitem{bibc2} 何玉彬，李新忠.神经网络控制及其应用[M].北京:科学出版社，2000. +\bibitem{bibc3} McCulloch W S, Pitts W A. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5: 115-133. +\bibitem{bibc4} Hebb D O. The Organization of Behaviour [M]. New York, John Wiley\&Sons Inc., 1949. +\bibitem{bibc5} Rosenblatt. The perception: a probabilistic model for information storage and organization in the brain [J]. Psychology Review, 1958, 65: 386-408. +\bibitem{bibc6} Minsky M, Papert S. Perceptron [M]. Cambridge, MA: MIT Press, 1969. +\bibitem{bibc7} Hopf\/ield J J. Neural networks and physical systems with emergent collective computational abilities[C]. Proceeding of the National Academy of Science. USA (Biophysics), 1982, 79: 2554-2558. +\bibitem{bibc8} Hopf\/ield J J. Neurons with graded response have collective computational properties like those of two-state neurons[C]. Proceedins of the National Academy of Science, USA(Biophysics), 1984, 81:3088-3092. +\bibitem{bibc9} Widrow B, McCool J, Ball M. The complex LMS algorithm [C]. Proc. IEEE, 1975, 63(4):719-720 +\bibitem{bibc10} Hirose A. Complex-valued neural networks: theories and applications [M]. World Scientif\/ic Series on Innovation Intelligence, vol 5, Singapore: World Scientif\/ic Publishing Co. Pte. Ltd. 2003 + +\bibitem{bib1} 谢林江, 季桂树, 彭清, 等. 改进的卷积神经网络在行人检测中的应用[J]. 计算机科学与探索, 2018, 12(5):708-718. +\bibitem{bib2}王耀玮, 唐伦, 刘云龙, 等. 基于多任务卷积神经网络的车辆多属性识别[J]. 计算机工程与应用, 2018, 54(8):21-27. +\bibitem{bib3} Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps[J]. The International Journal of Robotics Research, 2015, 34(4-5):705-724. +\bibitem{bib4} Kumra S, Kanan C. Robotic grasp detection using deep convolutional neural networks[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 769-776. +\bibitem{bib5} Chu F J, Xu R, Vela P A. Real-world multiobject, multigrasp detection[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 3355-3362. +\bibitem{bib6} Robot Learning Lab: Learning to Grasp[EB/OL].(2009) [2019-03-12]. +\bibitem{bib7} Redmon J, Angelova A. Real-time grasp detection using convolutional neural networks[C]//2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015: 1316-1322. +\bibitem{bib8} Ni P, Zhang W, Bai W, et al. A New Approach Based on Two-stream CNNs for Novel Objects Grasping in Clutter[J]. Journal of Intelligent \& Robotic Systems, 2018(2):1-17. +\bibitem{bib9} Krizhevsky A, Sutskever I, Hinton G E. Imagenet classif\/ication with deep convolutional neural networks[C]// Advances in neural information processing systems. 2012: 1097-1105. +\bibitem{bib10} He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. + +\bibitem{bibb1} Dogar M, Hsiao K, Ciocarlie M, et al. Physics-based grasp planning through clutter[C]//Robotics: Science and Systems VIII. Cambridge, USA: MIT Press, 2012: 8pp. +\bibitem{bibb2} Goldfeder C, Ciocarlie M, Dang H, et al. The Columbia grasp database[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2009: 1710-1716. +\bibitem{bibb3} Weisz J, Allen P K. Pose error robust grasping from contact wrench space metrics[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2012: 557- 562. +\bibitem{bibb4} Jiang Y, Moseson S, Saxena A. Eff\/icient grasping from RGB-D images: Learning using a new rectangle representation[C]// IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE,2011: 3304-3311. +\bibitem{bibb5} Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. + +\bibitem{bib11} Qiang Z, Li Z, Li J, et al. Vehicle color recognition using +Multiple-Layer Feature Representations of lightweight convolutional neural network[J]. Signal Processing, 2018, 147: 146-153. + + \bibitem{bib:one} 马倩倩,李晓娟,施智平.轻量级卷积神经网络的机器人抓取检测研究[J/OL].计算机工程与应用:1-11[2019-04-09]. + + \bibitem{bib12} Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< +0.5 MB model size[J].2016. + + \bibitem{bib13} Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 4700-4708. + + \bibitem{bib14} Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]// Proceedings of the IEEE conference on computer vision and pat- tern recognition. 2016: 2818-2826. + \bibitem{bib15} Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708. + \bibitem{bib16} He K, Sun J. Convolutional neural networks at con-strained time cost[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5353-5360. + \bibitem{bib17} Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958. + +\bibitem{bibb6} 仲训杲，徐敏，仲训昱，等．基于多模特征深度学习的机器人抓取判别方法 [J]．自动化学报，2016，42(7)：1022- 1029. +\bibitem{bibb7} Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps[J]. International Journal of Robotics Research, 2015, 34(4/5): 705-724. +\bibitem{bibb8} 杜学丹，蔡莹皓，鲁涛，等．一种基于深度学习的机械臂抓取方法 [J]．机器人，2017，39(6)：820-828,837. +\bibitem{bibb9} Pinto L, Gupta A. Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2016: 3406-3413. +\bibitem{bibb10} Guo D, Sun F C, Liu H P, et al. A hybrid deep architecture for robotic grasp detection[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2017: 1609-1614. +\bibitem{bibb11} 刘义军．基于 FPGA 的线结构光视觉测量系统研究 [D]. 长 春：吉林大学，2017：23-49. + + \bibitem{bib:three} 夏晶,钱堃,马旭东,刘环.基于级联卷积神经网络的机器人平面抓取位姿快速检测[J].机器人,2018,40(06):794-802. + +\bibitem{bibb12} 邹媛媛，赵明扬，张雷．基于量块的线结构光视觉传感器直接标定方法 [J]. 中国激光，2014，41(11)：189-194. +\bibitem{bibb13} 卢津，孙惠斌，常智勇．新型正交消隐点的摄像机标定方法 [J]. 中国激光，2014，41(2)：294-302. + +\bibitem{bibb14} Huang J, Rathod V, Sun C, et al. Speed/accuracy trade-offs for modern convolutional object detectors[A/OL]. (2017-04-25) [2017-12-07]. +\bibitem{bibb15} Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2014: 580-587. +\bibitem{bibb16} Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision. Piscataway, USA: IEEE, 2015: 1440-1448. +\bibitem{bibb17} Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[M]// Advances in Neural Information Processing Systems 28. Cam- bridge, USA: MIT Press, 2015: 91-99. +\bibitem{bibb18} Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Cham, Switzerland: Springer, 2016: 21-37. +\bibitem{bibb19} Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2016: 779-788. +\bibitem{bibb20} Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning [A/OL]. (2016-08-23) [2017-12-07]. + +\bibitem{bib:two} 李传浩. 基于卷积神经网络的机器人自动抓取规划研究[D].哈尔滨工业大学,2018. + + \bibitem{bib:four} 胡琳,晁飞.基于双神经网络结构的发展型机器人3D抓取[J].电脑知识与技术,2012,8(12):2859-2862. +\bibitem{bib:five} 刘晓玉. 复杂环境下基于神经网络的工件识别与机器人智能抓取[D].武汉科技大学,2009. +\bibitem{bib:six} 游辉胜. 基于模糊神经网络的单目视觉伺服机器人智能抓取[D].武汉科技大学,2008. +\end{thebibliography}
\ No newline at end of file |