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Research Papers

Dynamic Computation of Time-Varying Spatial Contexts

[+] Author and Article Information
Imre Horváth

Fellow ASME
Industrial Design Engineering,
Delft University of Technology,
Landbergstraat 15,
Delft 2628CE, The Netherlands
e-mail: i.horvath@tudelft.nl

Yongzhe Li

Industrial Design Engineering,
Delft University of Technology,
Landbergstraat 15,
Delft 2628CE, The Netherlands
e-mail: y.li-8@tudelft.nl

Zoltán Rusák

Industrial Design Engineering,
Delft University of Technology,
Landbergstraat 15,
Delft 2628CE, The Netherlands
e-mail: z.rusak@tudelft.nl

Wilhelm Frederik van der Vegte

Industrial Design Engineering,
Delft University of Technology,
Landbergstraat 15,
Delft 2628CE, The Netherlands
e-mail: w.f.vandervegte@tudelft.nl

Guangjun Zhang

State Key Laboratory of
Advanced Welding and Joining,
Harbin Institute of Technology,
92 West Dazhi Street,
Harbin 150001, China
e-mail: zhanggj@hit.edu.cn

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 12, 2016; final manuscript received June 22, 2016; published online November 7, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(1), 011007 (Nov 07, 2016) (12 pages) Paper No: JCISE-16-1077; doi: 10.1115/1.4034034 History: Received February 12, 2016; Revised June 22, 2016

There are many real-life processes whose smart control requires processing context information. Though processing time-varied context information is addressed in the literature, domain-independent solutions for reasoning about time-varying process scenarios are scarce. This paper proposes a method for dynamic context computation concerning spatial and attributive information. Context is interpreted as a body of information dynamically created by a pattern of entities and relationships over a history of situations. Time is conceived as a causative force capable of changing situations and acting on people and objects. The invariant and variant spatial information is captured by a two-dimensional spatial feature representation matrix (SFR-matrix). The time-dependent changes in the context information are computed based on a dynamic context information (DCI) management hyper-matrix. This humble but powerful representation lends itself to a quasi-real time computing and is able to provide information about foreseeable happenings over multiple situations. Based on this, the reasoning mechanism proposed in this paper is able to provide informative instructions for users who needed to be informed in a dynamically changing situation. This paper uses the practical case of evacuation of a building in fire both as an explorative case for conceptualization of the functionality of the computational mechanism and as a demonstrative and testing application. Our intention is to use the dynamic context computation mechanism as a kernel component of a reasoning platform for informing cyber-physical systems (I-CPSs). Our future research will address the issue of context information management for multiple interrelated spaces.

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Figures

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Fig. 1

Basic constituents of an informing CPS

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Fig. 4

Hyper-matrix for DCI management

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Fig. 5

Reasoning mechanism used for developing personalized evacuation strategies

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Fig. 6

Typical attribute profiles used in the SFRH-matrix: (a) attribute profile of an exit and (b) attribute profile of a person

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Fig. 7

Time periods available for using different types of exits

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Fig. 8

Flowchart of the algorithm used to develop raw strategy

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Fig. 9

Flowchart of the algorithm used to refine the escape strategy considering human dynamics

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Fig. 10

Time needed by the different category of people to escape in different situations

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Fig. 11

Flowchart of the algorithm used to reduce the number of people involved in a jam

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Fig. 12

Floor plan of the considered building: (a) physical arrangement of the space and (b) the starting place of fire

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Fig. 13

Simulation of the indoor fire situations without informing by the system at (a) T = 20 s, (b) T = 80 s, and (c) T = 200 s

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Fig. 14

Simulation of the indoor fire situations with informing by the system at (a) T = 30 s, (b) T = 50 s, (c) T = 80 s, and (d) T = 200 s

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Fig. 15

The number of people trapped in a jam at various exits: (a) without reducing the number of people potentially involved in jam and (b) with application of strategy refinement in step 2

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