Date post: | 24-May-2015 |
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Georg Hackenberg
Progress on
Complex Cyber-Physical Systems Engineering
Progress on complex cyber-physical systems engineering 2Georg Hackenberg
Question
What is a complex cyber-physical system?
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Answer
The smart grid!
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Answer continued
● Cyber-physical systemA cyber-physical system is a system featuring a tight combination of, and coordination between, the system’s computational and physical elements.
● Complex systemA complex system is a system composed of interconnected parts that as a whole exhibit one or more properties not obvious from the properties of the individual parts.
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Challenges
1Cyber-physical models
● Physical structure● Physical behavior● Computational struct.● Computational behav.
2Emergent properties
● Specification● Refinement● Implementation● Analysis
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Progress
Basic Framework Experimentation Solid Framework Evaluation
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Impressions
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Framework
1Modeling language
● Component hierarchy● Deterministic behavior● Probabilistic behavior● Exploration annotations
2Exploration engine
● Dynamic programming● Alternative generation● Alternative comparison● Space pruning
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Deterministic behavior
(Model
(Observation name:“Temperature“ type:“number“
(Expression
(Sum
(Observation name:“Temperature“ offset:“1“)
(Number value:“1“)
)
)
)
...
)
T (t)=T (t−1)+1
Semantics
Syntax
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P (C (t)= true)=P (C (t)= false)=11+1
=0.5
Probabilistic behavior
(Model
...
(Observation name:“Command“ type:“boolean“
(Distribution
(Option
(Value (Boolean value:“true“))
(Frequency (Number value:“1“))
)
(Option
(Value (Boolean value:“false“))
(Frequency (Number value:“1“))
)
)
)
)
Semantics
Syntax
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Exploration annotations
require
● State verification● True value
equal
● State pruning● Equal value
cost
● State sorting● Lower value
minimize/maximize
● State pruning● Lower/higher value
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Refrigerator example
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Dynamic programming
1. Select– states to follow
2. Generate– following states
3. Calculate– observations
4. Verify– required observations
5. Prune– dominated states
6. Sort– remaining states
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Algorithm complexity
10 refrigerators
15 minute steps
210≈1000 commands per step
210*96≈100096 commands per day
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Complexity reduction
1. Selection● Follow bounded
number of states● Select x% states
according to sorting● Select (100-x)%
states randomly
2. Generation● Generate bounded
number of states● Select options
according to frequency
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Exploration results
-250
-200
-150
-100
-50
0
50
100
150
200
250
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
2500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95
-1500000
-1000000
-500000
0
500000
1000000
1500000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95
1 refrigerator
10 refrigerators
5000 refrigerators
0
5
10
15
20
25
30
35
40
45
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
0
0,2
0,4
0,6
0,8
1
1,2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
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Exploration results continued
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Open questions● Is the language sufficient?
– What about dynamic structure?– What about property specification?– What about exploration guidance?
● Is the engine sufficient?– What about linear programming?– What about genetic programming?– What about machine learning?
● Is the toolbox operationalizable?
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Outlook
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Outlook continued● Software Campus Iteration
– Presenting results– Evaluating toolbox– Discussing evolution
● Analyze models with respect to ...– Communication overhead– Failure robustness– Attack robustness
● Extend models with respect to …– Mobility requirements– Business requirements– Engineering perspectives
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The end.