[1]王志良,杨 溢,杨 扬,等.一种周期时变马尔可夫室内位置预测模型[J].智能系统学报,2009,4(06):521-527.[doi:10.3969/j.issn.1673-4785.2009.06.009]
 WANG Zhi-liang,YANG Yi,YANG Yang,et al.A periodic time-varying Markov model for indoor location prediction[J].CAAI Transactions on Intelligent Systems,2009,4(06):521-527.[doi:10.3969/j.issn.1673-4785.2009.06.009]
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一种周期时变马尔可夫室内位置预测模型(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年06期
页码:
521-527
栏目:
出版日期:
2009-12-25

文章信息/Info

Title:
A periodic time-varying Markov model for indoor location prediction
文章编号:
1673-4785(2009)06-0521-07
作者:
王志良1杨   溢1杨   扬12张   琼1
1.北京科技大学 信息工程学院,北京100083; 2.北方工业大学 信息工程学院,北京 100144
Author(s):
WANG Zhi-liang1 YANG Yi1 YANG Yang12 ZHANG Qiong1
1. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. College of Information Engineering, North China University of Technology, Beijing 100144, China
关键词:
马尔可夫模型位置感知人工智能数字家庭
Keywords:
Markov model location awareness artificial intelligence smart homes
分类号:
TP31;TP391
DOI:
10.3969/j.issn.1673-4785.2009.06.009
文献标志码:
A
摘要:
根据在家庭环境中居住者的行为习惯具有周期性和时变性的特点,设计了一种智能数字家庭环境中的基于位置信息上下文的周期时变马尔可夫预测模型(PTVMM),用于预测居住者的下一个出现位置(房间).另外还构建了一个三维虚拟的智能数字家庭实验仿真环境(virtual smart home)用于模型的仿真对比研究.利用模拟行为数据的仿真结果表明,和其他的预测模型相比,周期时变马尔可夫位置预测模型具有较小的时间复杂度、较高的预测精度和较快的预测精度收敛速度,能够在智能数字家庭环境中进行实时、高精度的位置预测.
Abstract:
A location-based time-varying Markov model was designed to predict the next location (or room) of the inhabitants of a smart home environment. The model used periodic characteristics of inhabitant behavior in a three-dimensional simulation environment, or “virtual smart home”. This was established in order to simulate and compare different predictive models. The simulation results showed the proposed method decreased time complexity, increased predictive accuracy and improved convergences rates compared to other models. This method can be used to implement real-time and highly accurate predictions of location in a smart home environment.

参考文献/References:

[1]GOPALRATNAM K, COOK D J. Online sequential prediction via incremental parsing: the active LeZi algorithm[J]. IEEE Intelligent Systems,2007,22(1):52-58.
[2]ROY A, DAS S K, BASU K. A predictive framework for locationaware resource management in smart homes[J]. IEEE Transaction on Mobile Computing, 2007,6(11): 1270-1283.
[3]INTILLE S S.Designing a home of the future[J]. IEEE Pervasive Computing,2002,1(2):80-86.
[4]MOZER M. The neural network house: an environment that adapts to its inhabitants[C]//Proc AAAI Spring Symp on Intelligent Environments. Palo Alto, USA, 1998:110-114.
[5]HELAL S, MANN W, EIZZABADANI H. The gator tech smart house: a programmable pervasive space[J]. IEEE Computer, 2005,38(3): 50-60.
[6]ZIV J,LEMPEL A. Compression of individual sequences via variablerate coding[J]. IEEE Transaction on Information Theory, 1978, 24(5): 530-536.
[7]CHING W K, FUNG E S, NG M K. A higherorder Markov model for the Newsboy’s problem[J]. Journal of the Operational Research Society, 2003, 54(2): 291-298.
[8]CAWOOD S, MCGEE P. Microsoft XNA game studio creator’s guide[M]. New York, USA: McGraw-Hill Inc, 2007: 51-109.
[9]黄海平,王汝传,孙力娟,蒋   颢.基于Agent和无线传感器网络的普适计算情景感知模型[J].南京邮电大学学报:自然科学版,2008,28(2),74-79.
HUANG Haiping, WANG Ruchuan, SUN Lijuan, JIANG Hao.Pervasive computing scene apperceive model based on Agent & wireless sensor networks[J]. Journal of Nanjing University of Posts and Telecommunications:Natrual Science,2008,28(2),74-79.
[10]FEDER M, MERHAV N, GUTMAN M. Universal prediction of individual sequences[J]. IEEE Transaction on Information Theory,1992, 38(4):1265-1270.

备注/Memo

备注/Memo:
收稿日期:2009-08-15.
基金项目:国家“863”高技术发展计划资助项目(2007AA01Z160);北京市重点学科建设资助项目(XK100080537).
作者简介:
王志良,男,1956年生,教授,博士生导师,北京科技大学信息工程学院电子信息系主任,中国人工智能学会理事.主要研究方向为人工心理与情感计算、智能机器人、和谐人机交互,发表学术论文60余篇,其中被SCI、EI、ISTP检索30余篇,出版学术专著2部,合著2部.
杨    溢,男,1984年生,博士研究生,主要研究方向为数字家庭、人工智能与计算机网络.
杨    扬,男,1980年生,博士研究生,主要研究方向为人机交互技术,数字家庭 .
更新日期/Last Update: 2010-02-22