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Modeling Frames Stefan Klikovits 1 Joachim Denil 2 Alexandre Muzy 3 - PowerPoint PPT Presentation

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Modeling Frames Stefan Klikovits 1 Joachim Denil 2 Alexandre Muzy 3 Rick Salay 4 1 University of Geneva, Switzerland 2 University of Antwerp, Belgium 3 CNRS, I3S, Universit Cte dAzur, France 4 University of Toronto, Canada Experimental

  1. Modeling Frames Stefan Klikovits 1 Joachim Denil 2 Alexandre Muzy 3 Rick Salay 4 1 University of Geneva, Switzerland 2 University of Antwerp, Belgium 3 CNRS, I3S, Université Côte d’Azur, France 4 University of Toronto, Canada

  2. Experimental Frames Modeling 2 Frames Zeigler. 1984 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  3. Experimental Frames Modeling 2 Frames Zeigler. 1984 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  4. Experimental Frames Modeling 2 Frames Zeigler. 1984 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  5. Experimental Frames Modeling 3 Frames ◮ DEVS specification hierarchy ◮ Frame Interface → Frame Behaviour - Frame System Traoré, Muzy. 2005 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  6. Experimental Setups Modeling 4 Frames P M IC M P E IC E Model / System O E I M O M I E Experimental Setup Denil, Klikovits, Mosterman, Vallecillo, Vangheluwe. 2017 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  7. Experimental Setups Modeling 4 Frames P M IC M P E IC E Model / System O E I M O M I E Experimental Setup Denil, Klikovits, Mosterman, Vallecillo, Vangheluwe. 2017 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  8. Experimental Setups Modeling 4 Frames Observation collector C M C E S M S E P M IC M P E IC E Model / System O E I M O M I E Experimental Setup Denil, Klikovits, Mosterman, Vallecillo, Vangheluwe. 2017 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  9. Experimental Setups Modeling 4 Frames Observation collector C M C E S M S E P M IC M P E IC E Model / System O E I M O M I E Experimental Setup Solver(s) P S Denil, Klikovits, Mosterman, Vallecillo, Vangheluwe. 2017 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  10. Validity Frames Modeling 5 Frames ◮ focus on activities ◮ process centric ◮ calibration, validation, verification Denil, Klikovits, Mosterman, Vallecillo, Vangheluwe. 2017 Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  11. Need a context Modeling 6 Frames Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  12. What is it good for? Modeling 7 Frames ◮ choosing models from libraries ◮ model composition & decomposition ◮ validation, verification, reproducibility, . . . ◮ safety certification Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  13. Running example Modeling 8 Frames Sidewalk Road Sidewalk Traffic Light schema Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  14. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context :Observe system O : Create a model to learn under study about TL timing . :Conceptualise A : Colour sequence fixed. A : Phase lengths constant. :Model system under study C : Model must be a state machine . :Model Modeling process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  15. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context :Observe system O : Create a model to learn under study about TL timing . :Conceptualise A : Colour sequence fixed. A : Phase lengths constant. :Model system under study C : Model must be a state machine . :Model Modeling process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  16. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context :Observe system O : Create a model to learn under study about TL timing . :Conceptualise A : Colour sequence fixed. A : Phase lengths constant. :Model system under study C : Model must be a state machine . :Model Modeling process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  17. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context :Observe system O : Create a model to learn under study about TL timing . :Conceptualise A : Colour sequence fixed. A : Phase lengths constant. :Model system under study C : Model must be a state machine . :Model Modeling process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  18. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context 2. Activity :Observe system O : Create a model to learn under study about TL timing . :Conceptualise A : Colour sequence fixed. A : Phase lengths constant. :Model system under study C : Model must be a state machine . :Model Modeling process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  19. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context 2. Activity after( T r ) Red Green O : Create a model to learn about TL timing . after( T y ) after( T g ) A : Colour sequence fixed. A : Phase lengths constant. Yellow C : Model must be a state machine . Traffic light state machine Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  20. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context 2. Activity O : Create a model to learn about TL timing . A : Colour sequence fixed. A : Phase lengths constant. C : Model must be a state machine . Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  21. Let’s define a model creation frame. . . Modeling 9 Frames 1. Context 2. Activity after( Tg ) Red Green O : Create a model to learn after( Tr ) about TL timing . after( Ty ) after( Tg ) a after( Ty ) f A : Colour sequence fixed. t e r ( T r ) A : Phase lengths constant. Yellow C : Model must be a state machine . Traffic light state machine Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  22. All you need is . . . frames! Modeling Frames 10 All modeling activities performed in contexts! Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  23. All you need is . . . frames! Modeling Frames 10 All modeling activities performed in contexts! Modeling Activity: � Inputs , Outputs , Process � Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  24. All you need is . . . frames! Modeling Frames 10 All modeling activities performed in contexts! Modeling Activity: � Inputs , Outputs , Process � Modeling Context: � Objectives , Assumptions , Constraints � Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  25. All you need is . . . frames! Modeling Frames 10 All modeling activities performed in contexts! Modeling Activity: � Inputs , Outputs , Process � Modeling Context: � Objectives , Assumptions , Constraints � Modeling Frame: � Activity , Context , Frame ∗ � Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  26. Validation frame Modeling Frames 11 General Objective Make sure that modeling assumptions hold! :Data :Simulation collection O Assert colour sequence is fixed. O Assert phase times are constant. :Compare :Compare sequences durations A 48 hours of data suffice. :Assess C Precision in seconds at least. :Properties satisfied Validation process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  27. Validation frame Modeling Frames 11 General Objective Make sure that modeling assumptions hold! :Data :Simulation collection O Assert colour sequence is fixed. O Assert phase times are constant. :Compare :Compare sequences durations A 48 hours of data suffice. :Assess C Precision in seconds at least. :Properties satisfied Validation process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  28. Validation frame Modeling Frames 11 General Objective Make sure that modeling assumptions hold! :Data :Simulation collection O Assert colour sequence is fixed. O Assert phase times are constant. :Compare :Compare sequences durations A 48 hours of data suffice. :Assess C Precision in seconds at least. :Properties satisfied Validation process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  29. Validation frame Modeling Frames 11 General Objective Make sure that modeling assumptions hold! :Data :Simulation collection O Assert colour sequence is fixed. O Assert phase times are constant. :Compare :Compare sequences durations A 48 hours of data suffice. :Assess C Precision in seconds at least. :Properties satisfied Validation process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  30. Validation frame Modeling Frames 11 General Objective Make sure that modeling assumptions hold! :Data :Simulation collection O Assert colour sequence is fixed. O Assert phase times are constant. :Compare :Compare sequences durations A 48 hours of data suffice. :Assess C Precision in seconds at least. :Properties satisfied Validation process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  31. Frame types Modeling Frames 12 ◮ Modeling Frame ◮ Validation Frame Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  32. Frame types Modeling Frames 12 ◮ Modeling Frame ◮ Validation Frame :(System) Data collection ◮ Calibration Frame :Data analysis :Parameter calculation Calibration process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

  33. Frame types Modeling Frames 12 ◮ Modeling Frame :Model execution ◮ Validation Frame :Data collection ◮ Calibration Frame ◮ Verification Frame :Data comparison :Result assessment Verification process Klikovits et. al. Modeling Frames stefan.klikovits@unige.ch

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