Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (6): 11-24.doi: 10.6040/j.issn.1672-3961.0.2019.229

• Control Science & Engineering - Special Topic on Robot • Previous Articles     Next Articles

Research status and development trend of autonomous cognition and learning of robot manipulation skills

Wei WANG1(),Feng WU2,Fengyu ZHOU3,*()   

  1. 1. School of Computer Science and Technology, Qilu University of Technology, Jinan 250353, Shandong, China
    2. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, Anhui, China
    3. School of Control and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2019-06-13 Online:2019-12-20 Published:2019-12-17
  • Contact: Fengyu ZHOU E-mail:benwei85@sina.com;zhoufengyu@sdu.edu.cn
  • Supported by:
    国家重点研发计划项目(2017YFB1302400);国家自然科学基金资助项目(61773242);国家自然科学基金资助项目(61802213);山东省重大科技创新工程项目(2017CXGC0926);山东省重点研发计划(公益类专项)项目(2017GGX30133)

RichHTML

31

PDF (PC)

859

Abstract:

Autonomous cognition and learning of manipulation skills, being one of the most important skills for robots, has been one of the hot issues in the field of robotics research. Combining with the authors' work in the field of robotics, this paper's focus is placed on giving a comprehensive overview of the mainstream modes, methods, algorithms, as well as advantages and disadvantages of different methods in terms of robots' manipulation skill learning. It concludes the challenges faced by autonomous learning and the key issues that need to be addressed for the individual cloud robots learning manipulation skills in the knowledge sharing mode. At the end, a potential solution for the above issues is given, and that is to integrate individual learning mode and shared learning model for the purpose of enhancing autonomous cognition and learning ability for robots.

Key words: cloud robot, knowledge-sharing robot, manipulation skills, autonomous learning, autonomous cognition

CLC Number: 

  • TP242

Table 1

Some examples of the representation of robot manipulation skills"

代表项目 年代 国家 技能表示形式 学习方式 基本技能单元 技能单元性质 是否渐进式发展 跨机器人平台 跨工作种类 跨工作环境
Task Transfer 2007 美国 数值表示 发展式学习 数值 离散 是 否 是 否
Jean 2009 美国 符号表示 发展式学习 模板 离散 否 否 否 否
QLAP 2009 美国 定性表示 发展式学习 技能选项 离散 是 否 是 否
RoboEarth 2010 欧盟 符号表示 一次性获取 本体与语义描述 连续 否 是 是 否
CST 2013 美国 符号表示 从自身经验学习+发展式学习 技能抽象 连续 是 否 是 否

Table 2

Comparisons among representative projects on sharing manipulation skills and knowledge"

项目名称 年代 研究单位 国家(地区) 技能描述方法 技能自主发展
RoboEarth 2009 TUM 欧盟 本体、语义网络 否
RoboBrain 2013 加州伯克利分校 美国 常识维基百科 否
PR2叠毛巾 2015 康奈尔大学、斯坦福大学 美国 Twitter+深度神经网络 否
万物交流 2016 布朗大学 美国 ROS节点 否
Google Mind 2016 谷歌公司 美国 指令 否
C-LEARN 2017 麻省理工学院 美国 具有几何约束的规则数据库 否

Table 3

Comparisons of the main learning approaches for skill learning and development of robots"

技能学习方法 个体学习 社交学习 特点 主要挑战 主要技术
从演示中学习 支持 支持(非)面对面HRI、面对面的RRI 简单直接无需编程无需专家 缺少大量、高质量的演示数据 监督学习、强化学习、深度强化学习
发展式学习 支持 支持面对面HRI、RRI 模拟婴儿心智发育过程、渐进式技能增长 初始知识的数量和形式、可持续发展的主导程序设计、技能表示形式 强化学习、深度强化学习、DCO、DDCO
类脑学习 支持 不支持 模拟成人大脑、个案学习 视觉感知、与人沟通、大脑思考、自适应能力 基于CNN的深度学习、强化学习、基于生成模型的贝叶斯学习
共享知识学习 不支持 支持非面对面HRI、RRI 海量数据不受时空限制、一次性技能获取 数据的跨平台共享、共享数据的跨平台迁移学习 强化学习、深度强化学习、云计算

Fig.1

The combination of individual learning mode and knowledge-sharing learning mode(SRDL[20], CST's skill abstraction[27])"

Table 4

Four levels of the transfer learning of shared skills"

级别 工作 机器人平台 所需主要技术
第一级 已知 相同 自学迁移学习[53]
第二级 已知 不同 身体对应问题[4, 74]、自学迁移学习
第三级 未知 相同 多任务迁移学习[53]、身体对应问题
第四级 未知 不同 多任务迁移学习、身体对应问题、知识空白填补[75]

Fig.2

The architecture of Fog Robotics[80]"

1 WANG Wei. A social networking-enabled framework for autonomous robot skill development[D]. Sydney, Australia: University of Technology, Australia, 2016.
2 秦方博, 徐德. 机器人操作技能模型综述[J]. 自动化学报, 2019, 45 (8): 1401- 1418.
3 KUFFNER J. Cloud-enabled robots[C]//IEEE-RAS International Conference on Humanoid Robotics. Nashville, USA: ACM, 2010.
4 Breakthrough Technologies 2016. MIT technology review[EB/OL]. (2016-02-23)[2019-11-03]. https://www.technologyreview.com/lists/technologies/2016/.
5 ARGALL B D , CHERNOVA S , VELOSO M , et al. A survey of robot learning from demonstration[J]. Robotics & Autonomous Systems, 2009, 57 (5): 469- 483.
6 KONIDARIS G D , KUINDERSMA S R , GRUPEN R A , et al. Robot learning from demonstration by constructing skill trees[J]. International Journal of Robotics Research (IJRR), 2012, 31 (3): 360- 375.
doi: 10.1177/0278364911428653
7 CANGELOSI A , SCHLESINGER M . Developmental robotics: from babies to robots[M]. New York: USA:the MIT Press, 2015.
8 曾毅, 刘成林, 谭铁牛. 类脑智能研究的回顾与展望[J]. 计算机学报, 2016, 39 (1): 212- 222.
ZENG Yi , LIU Chenglin , TAN Tieniu . Review and outlook of brain-like intelligence research[J]. Chinese Journal of Computers, 2016, 39 (1): 212- 222.
9 CAKMAK M , DEPALMA N , ARRIAGA R I , et al. Exploiting social partners in robot learning[J]. Autonomous Robots, 2010, 29 (3/4): 309- 329.
10 YAHYA A, LI Adrian, KALAKRISHNAN M, et al. Collective robot reinforcement learning with distributed asynchronous guided policy search[C]//IROS. Vancouver, Canada: [s.n.], 2017: 79-86.
11 PIAGET J , COOK M T . The origins of intelligence in children[M]. New York: International Universities Press, 1952.
12 WENG Juyang , MCCELLAND A , PENTLAND O , et al. Autonomous mental development by robots and animals[J]. Journal of Science, 2001, 291, 599- 600.
13 FRANK G . Learning like a baby: a survey of artificial intelligence approaches[J]. The Knowledge Engineering Review, 2011, 26 (2): 209- 236.
doi: 10.1017/S0269888911000038
14 乔红, 尹沛劼, 李睿, 等. 机器人与神经科学交叉的意义:关于智能机器人未来发展的思考[J]. 中国科学院院刊, 2015, 30 (6): 762- 771.
QIAO Hong , YIN Peijie , LI Rui , et al. The significance of the crossover between robot and neuroscience: the thinking of the future development of intelligent robot[J]. Bulletin of Chinese Academy of Sciences, 2015, 30 (6): 762- 771.
15 ZENG Yi , ZHAO Yuxuan , BAI Jun . Towards robot self-consciousness (i): brain-inspired robot mirror neuron system model and its application in mirror self-recognition[J]. Lecture Notes in Computer Science, 2016, 10023, 11- 21.
16 ZENG Yi , ZHAO Yuxuan , BAI Jun , et al. Toward robot self-consciousness (ii): brain-inspired robot bodily self model for self-recognition[J]. Cognitive Computation, 2018, 10 (2): 307- 320.
doi: 10.1007/s12559-017-9505-1
17 QIAO Hong , LI Yinlin , LI Fengfu , et al. Biologically inspired model for visual cognition achieving unsupervised episodic and semantic feature learning[J]. IEEE Transactions on Cybernetics, 2015, 46 (10): 2335- 2347.
18 徐波, 刘成林, 曾毅. 类脑智能研究现状与发展思考[J]. 中国科学院院刊, 2016, 31 (7): 793- 802.
XU Bo , LIU Chenglin , ZENG Yi . Thoughts on the status and development of brain-inspired intelligence research[J]. Bulletin of Chinese Academy of Sciences, 2016, 31 (7): 793- 802.
19 TENORTH M , BEETZ M . KnowRob:a knowledge processing infrastructure for cognition-enabled robots[J]. International Journal of Robotics Research, 2013, 32 (5): 566- 590.
doi: 10.1177/0278364913481635
20 TENORTH M, PERZYLO A, LAFRENZ R, et al. The roboearth language: representing and exchanging knowledge about action, objects and environments[C]//IEEE International Conference on Robotics and Automation (ICRA). St. Paul, MN, USA: IEEE, 2012: 1284-1289.
21 RUSSELL S J, NORVIG P.人工智能:一种现代方法[M].殷建平,祝恩,刘越,等译. 3版.北京:清华大学出版社, 2017.
22 SUTTON R S , PRECUP D , SINGH S . Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning[J]. Artificial Intelligence, 1999, 112 (1/2): 181- 211.
23 ZHANG Yilu , WENG Juyang . Task transfer by a developmental robot[J]. IEEE Transactions on Evolutionary Computation, 2007, 11 (2): 226- 248.
doi: 10.1109/TEVC.2006.890269
24 KUIPERS B . Qualitative reasoning[M]. Massachusetts, USA: MIT, 1994.
25 COHEN P R, CHANG Yuhan, MORRISON C T. Learning and transferring action schemas[C]//The 20th International Joint Conference on Artificial Intelligence (IJCAI). Hyderabad, India: IEEE, 2007: 720-725.
26 MUGAN J , KUIPERS B . Autonomous learning of high-level states and actions in continuous environments[J]. IEEE Transactions on Autonomous Mental Development, 2012, 4 (1): 70- 86.
doi: 10.1109/TAMD.2011.2160943
27 KONIDARIS G, KUINDERSMA S, GRUPEN R A, et al. Autonomous skill acquisition on a mobile manipulator[C]//Association for the Advancement on Artificial Intelligence (AAAI). San Francisco, USA: AAAI Press, 2011.
28 PRESTES E , CARBONERA J L , FIORINI S R , et al. Towards a core ontology for robotics and automation[J]. Robotics and Autonomous Systems, 2013, 61 (11): 1193- 1204.
doi: 10.1016/j.robot.2013.04.005
29 TENORTH M , PERZYLO A C , LAFRENZ R , et al. Representation and exchange of knowledge about actions, objects, and environments in the RoboEarth framework[J]. IEEE Transactions on Automation Science and Engineering, 2013, 10 (3): 643- 651.
doi: 10.1109/TASE.2013.2244883
30 NIEKUM S , OSENTOSK S , KONIDARIS G D , et al. Learning grounded finite-state representations from unstructured demonstrations[J]. International Journal of Robotics Research, 2014, 34 (2): 131- 157.
31 SILVER D , HUANG Aja , MADDISON C J , et al. Mastering the game of go with deep neural networks and tree search[J]. Nature, 2016, 529 (7587): 484- 489.
doi: 10.1038/nature16961
32 LENZ I , LEE H , SAXENA A . Deep learning for detecting robotic grasps[J]. International Journal of Robotics Research (IJRR), 2014, 34 (4/5): 705- 724.
33 MNIH V , KAVUKCUOGLU K , SILVER D , et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518 (7540): 529- 533.
doi: 10.1038/nature14236
34 VOOSEN P . The AI detectives[J]. Science, 2017, 357 (6346): 22- 27.
doi: 10.1126/science.357.6346.22
35 DOSILOVVIC F, BRCIC M, HLUPIC N. Explainable artificial intelligence: a survey[C]//The 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2018). Opatija, Croatia: ACM, 2018: 210-215.
36 刘乃军, 鲁涛, 蔡莹皓, 等. 机器人操作技能学习方法综述[J]. 自动化学报, 2019, 45 (3): 458- 470.
LIU Naijun , LU Tao , CAI Yinghao , et al. A survey of learning methods on robot manipulation skills[J]. Acta Automatica Sinica, 2019, 45 (3): 458- 470.
37 SILVER D , SCHRITTWIESER J , SIMONYAN K , et al. Mastering the game of go without human knowledge[J]. Nature, 2017, 550 (7676): 354- 359.
doi: 10.1038/nature24270
38 VAN HASSELT H, GUEZ A, SILVER D. Deep reinforcement learning with double Q-learning[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Arizona, USA: AAAI Press, 2016: 2094-2100.
39 WANG Ziyu, SCHAUL T, HESSEL M, et al. Dueling network architectures for deep reinforcement learning[C]//The 33rd International Conference on Machine Learning. New York, USA: JMLR, 2016: 1995-2003.
40 HAUSKNECHT M, STONE P. Deep recurrent Q-learning for partially observable MDPs[C]//The 29th AAAI Conference on Artificial Intelligence. Texas, USA: AAAI Press, 2015.
41 GU Shixiang, LILLICRAP T, SUTSKEVER I, et al. Continuous deep Q-learning with model-based acceleration[C]//Proceedings of the 33rd International Conference on Machine Learning (ICML). New York, USA: JMLR, 2016: 2829-2838.
42 SILVER D, LEVER G, HEESS N, et al. Deterministic policy gradient algorithms[C]//The 31st International Conference on International Conference on Machine Learning (ICML). Beijing, China: JMLR, 2014: 387-395.
43 LILLICRAP T , HUNT J J , PRITZEL A , et al. Continuous control with deep reinforcement learning[J]. Computer Science, 2015, 8 (6): A187.
44 SCHULMAN J, LEVINE S, MORITZ P, et al. Trust region policy optimization[C]//The 32nd International Conference on Machine Learning (ICML). Lille, France: JMLR, 2015: 1889-1897.
45 HEESS N, DHRUVA T B, SRIRAM S, et al. Emergence of locomotion behaviours in rich environments.[EB/OL]. (2017-07-01)[2019-11-03]. https://www.researchgate.net/publication/318316001_Emergence_of_Locomotion_Behaviours_in_Rich_Environmentsar
46 LEVINE S, KOLTUN V. Guided policy search[C]//The 30th International Conference on Machine Learning (ICML). Atlanta, USA: JMLR, 2013: 1-9.
47 LEVINE S , FINN C , DARRELLL T , et al. End-to-end training of deep visuomotor policies[J]. Journal of Machine Learning Research, 2016, 17 (1): 1334- 1373.
48 KULKARNI T D, NARASIMHAN K R, SAEEDI A, et al. Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation[C]//Neural and Evolutionary Computing Statistics-Machine Learning. New York, USA: IEEE, 2016.
49 KRISHNAN S, FOX R, STOICA I, et al. DDCO: Discovery of deep continuous options for robot learning from demonstrations[C]//Proceedings of Machine Learning Research. Boston, USA: ACM, 2017.
50 LEMKE C , BUDKA M , GABRYS B . Metalearning: a survey of trends and technologies[J]. Artificial Intelligence Review, 2015, 44 (1): 117- 130.
doi: 10.1007/s10462-013-9406-y
51 FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning(PMLR). Sydney, Australia: ACM, 2017.
52 FINN C, YU T, ZHANG T, et al. One-shot visual imitation learning via meta-learning[C]// The 1st Conference on Robot Learning(CoRL). Osaka, Japan: IEEE, 2017: 357-368.
53 PAN Jialin , YANG Qiang . A survey on transfer learning[J]. IEEE Transactions on Knowledge & Data Engineering, 2010, 22 (10): 1345- 1359.
54 RUSU A A, RABINOWITZ N C, DESJARDINS G, et al. Progressive neural networks[EB/OL]. (2019-03-15)[2019-11-03]. https://blog.acolyer.org/2016/10/11/progressive-neural-networks/?utm_source=tuicool&utm_medium=referral.
55 FERNANDO C, BANARSE D, BLUNDELL C, et al. Pathnet: evolution channels gradient descent in super neural networks[J/OL]. Neural and Evolutionary Computing, 2017. https://www.researchgate.net/publication/313096253_PathNet_Evolution_Channels_Gradient_Descent_in_Super_Neural_Networks.
56 KEHOE B, MATSUKAWA A, CANDIDO S, et al. Cloud-based robot grasping with the google object recognition engine[C]//IEEE International Conference on Robotics & Automation (ICRA). Karlsruhe, Germany: the IEEE Press, 2013: 4263-4270.
57 STEELS L . Evolving grounded communication for robots[J]. Trends in Cognitive Sciences, 2003, 7 (7): 308- 312.
doi: 10.1016/S1364-6613(03)00129-3
58 STEELS L , BELPAEME T . Coordinating perceptually grounded categories through language: a case study for color[J]. Behavioral and Brain Sciences, 2005, 28 (4): 489- 529.
doi: 10.1017/S0140525X05220083
59 WAIBEL M , BEETZ M , CIVERA J , et al. RoboEarth: a world wide web for robots[J]. IEEE Robotics and Automation Magazine, 2011, 18 (2): 69- 82.
doi: 10.1109/MRA.2011.941632
60 ASHUTOSH S, JAIN A, SENER O, et al. RoboBrain: large-scale knowledge engine for robots[EB/OL]. (2014-12-01)[2019-11-03]. https://arxiv.org/abs/1412.0691v1.
61 GU Shixiang, HOLLY E, LILLICRAP T, et al. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). Singapore: IEEE, 2017: 3389-3396.
62 CLAUDIA P D, SHAH J A. C-LEARN: learning geometric constraints from demonstrations for multi-step manipulation in shared autonomy[C]//IEEE International Conference on Robotics & Automation (ICRA). Singapore: IEEE, 2017: 4058-4065.
63 TENORTH M , KLANK U , PANGERCIC D , et al. Web-enabled robots[J]. IEEE Robotics and Automation Magazine, 2011, 18 (2): 58- 68.
doi: 10.1109/MRA.2011.940993
64 WANG Wei , JOHNSTON B , WILLIAMS M-A . Social networking for robots to share knowledge, skills and know-how[J]. Lecture Notes in Computer Science, 2012, 7621, 418- 427.
65 MyRobots.com[EB/OL]. (2010-12-19)[2019-11-03]. http://creader.com/news/20011219/200112.
66 FREEMAN K. Social network for robots lets you talk to your roomba[EB/OL]. (2011-12-29)[2019-11-03]. http://mashable.com/2011/12/28/social-network-for-robots.
67 NIEKUM S, OSENTOSKI S, KONIDARIS G, et al. Learning and generalization of complex tasks from unstructured demonstrations[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vilamoura-Algarve, Portugal: IEEE, 2012.
68 KEHOE B , PATIL S , ABBEEL P , et al. A survey of research on cloud robotics and automation[J]. IEEE Transactions on Automation Science and Engineering, 2015, 12 (2): 398- 409.
doi: 10.1109/TASE.2014.2376492
69 张钹.走向真正的人工智能[R].深圳: [s.n.], 2018.
70 KONIDARIS G , KAELBLING L P , LOZANO-PEREZ T . From skills to symbols: learning symbolic representations for abstract high-level planning[J]. Journal of Artificial Intelligence Research, 2018, 61, 215- 289.
doi: 10.1613/jair.5575
71 QUIGLEY M, GERKEY B, CONLEY K, et al. ROS: an open-source robot operating system[C]//ICRA Workshop on Open Source Software. Kobe, Japan: IEEE, 2009, 3(3).
72 陈贤, 武延军. 基于ROS的云机器人服务框架[J]. 计算机系统应用, 2016, 25 (10): 73- 80.
CHEN Xian , WU Xiangjun . A framework for ROS-based cloud robot service[J]. Computer Systems and Applications, 2016, 25 (10): 73- 80.
73 张继鑫, 武延军. 基于ROS的服务机器人云端协同计算框架[J]. 计算机系统应用, 2016, 25 (9): 85- 91.
ZHANF Jixin , WU Yanjun . A cloud-based collaborative computing framework for ROS-based service robot[J]. Computer Systems and Applications, 2016, 25 (9): 85- 91.
74 ALISSANDRAKIS A , NEHANIV C L , DAUTENHAHN K . Correspondence mapping induced state and action metrics for robotic imitation[J]. IEEE Transactions on Systems(Man and Cybernetics-Part B): Cybernetics, 2007, 37 (2): 299- 307.
doi: 10.1109/TSMCB.2006.886947
75 JI Jianmin , CHEN Xiaoping . A weighted causal theory for acquiring and utilizing open knowledge[J]. International Journal of Approximate Reasoning, Elsevier Science Inc, 2014, 55 (9): 2071- 2082.
doi: 10.1016/j.ijar.2014.03.002
76 FITZGERALD T, THOMAZ A L. Skill demonstration transfer for learning from demonstration[C]//The 10th ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (HRI'15 Extended abstracts). New York, USA: ACM, 2015: 187-188.
77 KONIDARIS G D, KAELBLING L, LOZANOPEREZ T. Symbol acquisition for task-level planning[C]//AAAI Conference on Learning Rich Representations from Low-Level Sensors. Bellevue, Washington, USA: AAAI Press, 2013.
78 YANG Fangkai, LYU Daoming, LIU Bo, et al. PEORL: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making[C]//International Joint Conference on Artificial Intelligence(IJCAI). Stockholm, Sweden: IEEE, 2018.
79 GUDI S L K C, OJHA S, JOHNSTON B, et al. Fog robotics: an introduction[C]//International Conference on Intelligent Systems (IROS). Vancouver, Canada: ACM, 2017.
80 GUDI S L K C, OJHA S, JOHNSTON B, et al. Fog robotics for efficient, fluent and robust human-robot interaction[C]//IEEE the 17th International Symposium on Network Computing and Applications (NCA). Massachusetts, USA: Computer Society Press, 2018: 1-5.
[1] TIAN Guohui, XU Yaxiong. Cloud robotics: concept, architectures and key technologies [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 47-54.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] LUO Yun-hu,XING Li-dong,WANG Qin,LIU Hai-chun,WENG Xiao-guang . Coordination of bidding strategies for two kinds of interruptible load reserve markets on demand side[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(3): 77 -80 .
[2] LUO Yun-hu,WU Xu-wen,PAN Shuang-lai,DONG Er-ling,SUN Xiu-juan,WANG Chuan-jiang,WU Na . Coordination of two kinds of interruptible loads of demand side and reserve capacity of generation side[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(6): 66 -70 .
[3] HAN Xue. Example analysis for landslide hazard remote monitoring at  the Pingzhuang west open-pit mine[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(4): 116 -120 .
[4] . H2 white noise estimation for linear continuous-time systems with delayed measurements[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(3): 56 -61 .
[5] JIANG Peng-fei,WANG Zhen . Game analysis of manufacturer and different suppliers[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 117 -119 .
[6] GAO Ming, SHI Yue-Thao, WANG Ni-Ni, SUN Feng-Zhong, PING Ya-Ming. Circumferential inflow air distributing rules in a natural draft  wet-cooling tower under crosswind conditions[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(3): 154 -158 .
[7] WANG En-dong, . Strength characteristic analysis of box girder bridge pavement[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 71 -76 .
[8] MENG Xiang-xing1, YU Da-yang2, HAN Xue-shan2, ZHAO Jian-guo3. The  influence of  correlation  between  solar  irradiation  and  the  load  variation  on  grid-connected  photovoltaic  power  generation[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(2): 126 -129 .
[9] LIU Hai-chun,XIE Shao-jun,WANG Guo-feng . A novel hybrid current control method applied in threephase four leg APF[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(6): 10 -14 .
[10] YAO Zhan-yong,LI Yun-heng,SHANG Qing-sen,LI Shi-hua,ZHANG Hong-bo , . Research on some problems about a classified highway in a water net plain district[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(1): 70 -73 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd