基于關聯關系的仿真模型實時智能推薦方法

范國超; 許承東; 胡春生; 宋丹

doi:10.13374/j.issn2095-9389.2017.04.019

基于關聯關系的仿真模型實時智能推薦方法

doi: 10.13374/j.issn2095-9389.2017.04.019

1) 北京理工大學宇航學院, 北京 100081
2) 寧夏大學機械工程學院, 銀川 750021

基金項目:

國家自然科學基金資助項目(61502257，41304031)

詳細信息

中圖分類號: TP391.9
計量
- 文章訪問數: 609
- HTML全文瀏覽量: 200
- PDF下載量: 12
- 被引次數: 0
出版歷程
- 收稿日期: 2016-06-23

Real-time intelligent recommendation method of a simulation model based on incidence relation

1) School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
2) School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China

摘要

摘要: 當全球導航衛星系統(global navigation satellite system,GNSS)分布式仿真環境中共享的模型數量非常多時,檢索模型和配置仿真任務將成為一個比較復雜的工程.為提高仿真模型選取和仿真任務配置的效率,設計了一套針對GNSS分布式仿真環境中仿真模型的實時智能推薦方法,方法中首先定義了模型關聯關系和接口形狀的概念,然后提出了一種條件約束下的頻繁模式樹(FP-tree)結構,并從理論上分析了該結構在檢索任務量方面的減少程度,設計并推導了模型關聯關系度的計算方法,以及整套智能推薦方法的運行流程.推薦方法在GNSS分布式仿真環境中進行了仿真驗證,仿真結果與傳統智能推薦方法做對比分析,分析結果表明,該方法針對仿真模型推薦時運行時間短,推薦結果準確度高,能夠實時為用戶推薦合適的模型.
- 全球導航衛星系統 /
- 分布式仿真 /
- 智能推薦 /
- 關聯關系
Abstract: With the availability of a large number of sharing models, model search and task design would be an extremely complex project in the global navigation satellite system (GNSS) -distributed simulation environment (GDSE). For improving the efficiency of model search and task design, a real-time intelligent recommendation method was designed for GDSE. Based on the characteristics of the simulation model, the incidence relation and interface shape of the model were defined in the method and a conditional frequent pattern tree (FP-tree) structure was designed to further improve the retrieval efficiency. The effect of the conditional FP-tree structure was proved theoretically. Then, the calculation method of the model incidence relation degree was proposed and derived based on the Bayesian statistical method. The entire processing of the intelligent recommendation method was designed for implementing it in GDSE. Hence, to check the effect of the real-time intelligent recommendation method, it was implemented in GDSE. Compared with the simulation result of the traditional recommendation method, the model intelligent recommendation method is proved to have a shorter running time and a high accuracy on simulation model recommendation. The computing capability and real-time performance are proved through the simulation. It is demonstrated that the intelligent recommendation method is efficient and flexible for GDSE.
- global navigation satellite system /
- distributed simulation /
- intelligent recommendation /
- incidence relation

HTML全文

參考文獻(17)

[1]	Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. Commun ACM, 2010, 53(4):50
[2]	Mell P, Grance T. The NIST Definition of Cloud Computing. NIST Special Publication 800-145, 2011
[3]	Baun C, Kunze M, Nimis J, et al. Cloud Computing Web-based Dynamic it Services. Berlin:Springer-Verlag, 2011
[4]	Miller M. Cloud Computing:Web-Based Applications that Change the Way You Work and Collaborate Online. Indianapolis:QUE Publishing Company, 2008
[6]	Buyya R, Ranjan R, Calheiros R N. Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit:challenges and opportunities//International Conference on High Performance Computing&Simulation. IEEE, Leipzig, 2009:1
[7]	Fang B L, Yin X, Tan Y, et al. The contributions of cloud technologies to smart grid. Renewable Sustainable Energy Rev, 2016, 59:1326
[8]	Vrba P, Marik V, Siano P, et al. A review of agent and service-oriented concepts applied to intelligent energy systems. IEEE Trans Ind Inf, 2014, 10(3):1890
[9]	Tsai W T, Bai X Y, Huang Y. Software-as-a-service (SaaS):perspectives and challenges. Sci China Inf Sci, 2014, 57(5):1
[10]	Wu D Z, Greer M J, Rosen D W, et al. Cloud manufacturing:strategic vision and state-of-the-art. J Manuf Syst, 2013, 32(4):564
[13]	Armbrust M, Fox A, Griffith R, et al. Above the Clouds:A Berkely View of Cloud Computing. University of California Berkeley, 2009
[15]	Hu C S, Xu C D, Fan G C, et al. A simulation model design method for cloud-based simulation environment. Adv Mech Eng, 2013(8):1
[17]	Park D H, Kim H K, Choi I Y, et al. A literature review and classification of recommender systems research. Expert Syst Appl, 2012, 39(11):10059
[18]	Borràs J, Moreno A, Valls A. Intelligent tourism recommender systems:a survey. Expert Syst Appl, 2014, 41(16):7370
[19]	Chen S N, Qian H Y, Gu J Y. A recommender system for mobile commerce based on relational learning//9th International Workshop Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015. Fuzhou, 2015, 9426:415
[20]	Han J S, Kim G J. A method of intelligent recommendation using task ontology. Cluster Comput, 2014, 17(3):827
[21]	Dai C Y, Chen L. An algorithm for mining frequent closed itemsets in data stream. Phys Procedia, 2012, 24:1722
[22]	Tan P N, Steinbach M, Kumar V. Introduction to Data Mining. Beijing:China Machine Press, 2010:363