[1] Compare M, Martini F, Zio E.Genetic Algorithms for Condition-based Maintenance Optimization under Uncertainty[J]. European Journal of Operational Research (S0377-2217), 2015, 244(2): 611-623. [2] Parikshit Mehta, Andrew Werner, Laine Mears.Condition based Maintenance Systems Integration and Intelligence Using Bayesian Classification and Sensor Fusion[J]. Journal of Intelligent Manufacture (S0956-5515), 2015, 26(2): 331-346. [3] 王兆强, 胡昌华, 王文彬, 等. 基于Wiener过程的钢厂风机剩余使用寿命实时预测[J]. 北京科技大学学报, 2014, 36(10): 1361-1368. Wang Zhaoqiang, Hu Changhua, Wang Wenbin, et al.Wiener process-based online prediction method of remaining useful life for draught fans in steel mills[J]. Journal of University of Science and Technology Beijing, 2014, 36(10): 1361-1368. [4] Si Xiaosheng, Wang Wenbin, Hu Changhua, et al.Remaining Useful Life Estimation Based on Nonlinear Diffusion Degradation Process[J]. IEEE Transactions on Reliability (S0018-9529), 2012, 61(1): 50-67. [5] 张会会, 张伟, 胡昌华. 基于参数递归更新的惯性器件寿命预测[J]. 系统仿真学报, 2013, 25(5): 1036-1040. Zhang Huihui, Zhang Wei, Hu Changhua.Inertia device lifetime prediction based on REM method[J]. Journal of System Simulation, 2013, 25(5): 1036-1040. [6] 石慧, 曾建潮. 考虑非完美维修的实时剩余寿命预测及维修决策模型[J]. 计算机集成制造系统, 2014, 20(9): 2259-2266. Shi Hui, Zeng Jianchao.Prediction of real-time remaining useful life and maintenance decision model considering imperfect preventive maintenance[J]. Computer Integrated Manufacturing Systems, 2014, 20(9): 2259-2266. [7] 张建勋. 基于随机退化建模的寿命预测方法及应用研究[D]. 西安: 第二炮兵工程大学, 2013: 2-8. Zhang Jianxun.Lifetime prediction method and its application based on stochastic degradation method[D]. Xi'an: Second Artillery Engineering University, 2013: 2-8. [8] 张仕新, 昝翔, 李浩, 等. 状态维修理论及剩余寿命预测的研究现状与展望[J]. 兵工自动化, 2014, 33(9): 15-20. Zhang Shixin, Zan Xiang, Li Hao, et al.Condition-Based Maintenance theory and research status and prospect about prediction of residual useful life[J]. Ordnance Industry Automation, 2014, 33(9): 15-20. [9] 张昭. 基于贝叶斯统计推断的陀螺仪剩余寿命预测方法研究[D]. 西安: 第二炮兵工程大学, 2013: 13-25 Zhang Zhao.Remaining useful life prediction of gyroscope based on Bayesian statistical inference[D]. Xi'an: Second Artillery Engineering University, 2013: 13-25. [10] 王飞跃. 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004, 19(5): 485-489. Wang Feiyue.Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004, 19(5): 485-489. [11] 周云. 面向实时作战决策支持的动态数据驱动仿真理论和方法研究[D]. 长沙:国防科学技术大学, 2010: 20-40. Zhou Yun.Research on the theory and methods of dynamic data driven simulation for real-time combat decision support[D]. Changsha: National University of Defense Technology, 2010: 20-40. [12] 王飞跃. 面向赛博空间的战争组织与行动:关于平行军事体系的讨论[J]. 军事运筹与系统工程, 2012, 26(3): 5-10. Wang Feiyue.War organizations and actions for Cyberspace: discussions on parallel military systems[J]. Military Operations Research and System Engineering, 2012, 26(3): 5-10. [13] Chen B.KD-ACP: A Software Framework for Social Computing in Emergency Management[J]. Mathematical Problems in Engineering (S1024-123X), 2014, 21(3): 35-38. [14] Wang F Y.Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications[J]. IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2010, 11(3): 630-638. [15] 张育林. 平行试验—武器装备体系试验的理论与方法[R]. 第428次香山科学会议主题报告, 北京, 2012. Zhang Yulin.Parallel experiment-a discussion concepts and methods of weapon equipment system-of-systems experiment[R]. The 428th Xiangshan Science Conference Thematic Report, Beijing, 2012. [16] 王飞跃, 李晓晨, 毛文吉, 等. 社会计算的基本方法与应用[M]. 杭州: 浙江大学出版社, 2013: 7-10. Wang Feiyue, Li Xiaochen, Mao Wenji, et al.Social computing methods and applications[M]. Hangzhou: Zhejiang University Press, 2013: 7-10. [17] Darema Frederica. Dynamic Data Driven Application Systems, NSF Workshop Report[R/OL]. (2006-02) [2016-07]. http://www.cise.nsf.gov/dddas. [18] Surdu J R, Kittka K.The Deep Green Concept[C]// Spring Multiconference 2008, Military Modeling and Simulation Symposium (MMS). Ottawa, Canada: DARPA, 2008: 103-107. [19] Surdu J R, Kittka K.Deep Green: Commander's Tool for COA's Concept[C]// Computing, Communications and Control Technologies, Orlando, Florida, USA: CCCT, 2008: 56-62. [20] Darema Frederica, Douglas Craig, Patra Abani. InfoSymbiotics /DDDAS: The Power of Dynamic Data Driven Applications Systems, NSF and AFOSR Workshop Report[R/OL].(2010-08) [2016-07]. http:// www.cise.nsf.gov/dddas. [21] Fujimoto Richard, Lunceford Dell, Page Ernest, et al. Grand Challenges for Modeling and Simulation [R/OL].(2002-08) [2016-07]. http://www.dangstuhl.de/ Reports/02/02351.pdf. [22] Aydt Heiko, Turner Stephen John, Cai Wentong, et al.Research Issues in Symbiotic Simulation[C]// Proceedings of the 2009 Winter Simulation Conference, Austin, Texas. USA: IEEE, 2009: 1213-1222. [23] 胡晓峰. 大数据时代对建模仿真的挑战与思考[J]. 军事运筹与系统工程, 2013, 27(4): 5-12. Hu Xiaofeng.Challenges and thoughts of modeling & simulation in big data era[J]. Military Operations Research and System Engineering, 2013, 27(4): 5-12. [24] 毕长剑. 建模与仿真技术的三个前沿领域[R]. 第十届中国系统建模与仿真技术高层论坛, 北京, 2015. Bi Changjian.Three frontiers of modeling and simulation technology[R]. The 10th China System Modeling and Simulation Technology Forum, Beijing, 2015. [25] 王飞跃. 指控5.0: 平行时代的智能指挥与控制体系[J]. 指挥与控制学报, 2015, 1(1): 107-120. Wang Feiyue.CC 5.0: intelligent command and control systems in the parallel age[J]. Journal of Command and Control, 2015, 1(1): 107-120. [26] Scholkopf B, Smola A, Muller K R.Nonlinear Component Analysis as a Kernel Eigenvalue Problem[J]. Neural Computation (S0899-7667), 1998, 10(1): 1299-1319. [27] Reichle R H, Walker J P, Koster R D, et al.Extended Versus Ensemble Kalman Filtering for Land Data Assimilation[J]. Journal of Hydrometeorology (S1525- 755X), 2002, 3(6): 728-740. [28] Moradkhani H, Hsu K L, Gupta H, et al.Uncertainty Assessment of Hydrologic Model States and Parameters: Sequential Data Assimilation Using the Particle Filter[J]. Water Resources Research (S0043-1397), 2005, 41(5): 1-17. [29] Vrugt J A, ter Braak C J F, Disks C G H, et al. Hydrologic Data Assimilation Using Particle Markov Chain Monte Carlo Simulation: Theory, Concepts and Application[J]. Advances in Water Resources (S0309-1708), 2013, 51: 457-478. [30] 司小胜, 胡昌华, 李娟, 等. Bayesian更新与EM算法协作下退化数据驱动的剩余寿命估计方法[J]. 模式识别与人工智能, 2013, 26(4): 357-365. Si Xiaosheng, Hu Changhua, Li Juan, et al.Degradation data-driven remaining useful life estimation approach under collaboration between Bayesian updating and EM algorithm[J]. Pattern Recognition and Artificial Intelligence, 2013, 26(4): 357-365. |