Published on March 19, 2009
SMART HOMES AND FUZZY LOGIC presented by Nicolas Bettenburg 1
1,000,000 years ago 2
Photograph by Sisse Brimberg © 2007 National Geographic 250,000 years ago 3
not so long ago 4
What’s next? Latest Trend: Smart Homes 6
Imagine when you come home ... ... your front door opens on its own ... lights turn on automatically ... your fridge is ﬁlled ... the pets are already fed 7
You need: + + A home Lots of Sensors Controller + Actuators 9
Location Time Day Rules Devices 10
The system should be • context sensitive • adaptive • invisible 11
Context-sensitive • act application-speciﬁc lighting for a party • context triggered actions the cake comes in usually achieved using Machine Learning 12
Adaptive • our habits change summer vs. winter ... • different persons have different perceptions male vs. female ... usually achieved with Neural Networks 13
Capturing the Environment Time == 2pm Month == September Date == 21 Humidity == 35% Luminosity == 100 lx Location == 30.12 , 41.21, 8.51 ... we will end up with millions of rules! 14
Humans perceive their environment differently! 15
We use natural language! Time is ‘around noon‘ Date is ‘beginning of fall’ Weather is ‘still warm and dry’ Location is ‘in the bathroom’ 16
Sensors Humans vs. measure crisp use natural values language 27.14 ºC pretty warm 17
How can we solve this? 18
Use Fuzzy Logic 19
Two Obstacles (1) learn from user’s actions (2) pro-actively anticipate user’s needs 20
invisible. It has removable ﬂoor and ceiling tiles, lots of space for equipment and customized electrics, which allow us to reconﬁgure lights, wall sockets and Example: Lighting Control System switches as needed. A picture of the smart home is shown in ﬁgure 1. 21
Example: Lighting Control System Inputs outdoor light level person activity time Outputs ceiling light power venetian blinds position 22
Example: Lighting Control System dark normal bright 1 0 0 120 250 Outdoor Illuminance 23
Example: Lighting Control System at home absent 1 0 0 255 Person activity Sensor gives either 0 or 255 (binary) 24
Example: Lighting Control System t1 t2 t3 t4 t5 1 ... 0 -20 0 120 1440 Time 1440 minutes mapped on 50 ‘zones’ 25
Example: Lighting Control System on on off off 1 1 0 0 0 255 0 255 Ceiling Blinds Override: on/off Override: on/off 26
Example: Lighting Control System quite small quite much much small normal 1 0 250 0 250 Output 1: Ceiling Light Power Defuzzify using ‘Center of Gravity’ 27
Example: Lighting Control System down up closed up closed center 1 0 250 0 250 Output 2: Venetian Blinds Position Defuzzify using ‘Center of Gravity’ 28
event-based control. Example: Lighting Control System Table 1. An example of a rule table Example Rule Fuzzify input, map to output and defuzzify output Table 2 shows all the possible types of rules used and the possible values in the rule table with the used rules. In autonomous control, the override ﬂags of outputs on the input side are deﬁned to be oﬀ, marked with number one. The output states on the input side are marked with zeros, so that the state of an output is ignored during the input aggregation. All the other values of 29
Just another Mamdani-like system ... 30
... But this system can learn its rule table without prior knowledge! 31
Learning Process Data Fuzziﬁcation Data Filtering Sensors Server Rule Database Update Fuzzy control process 32
Automatic Data Gathering • Monitor Input and Output devices • Record their values periodically • Reasonable Timer: 1 minute Data Fuzziﬁcation Data Filtering Sensors Server Rule Database Update Fuzzy control process 33
Data Fuzziﬁcation • Read recorded input and output values. • Determine membership function with greatest degree of membership. • Store fuzzy value for later use in learning process. Data Fuzziﬁcation Data Filtering Sensors Server Rule Database Update Fuzzy control process 34
Data Filtering • Search most common combinations of inputs and outputs within a time period. • Time period no longer than one fuzzy time unit. Data Fuzziﬁcation Data Filtering Sensors Server Rule Database Update Fuzzy control process 35
Rule Base Updating • Search database for input combinations determined in previous step. • If not found: add rule with small weight • If found: increase/ decrease weights • If weight becomes 0: remove Data Fuzziﬁcation Data Filtering Sensors Server Rule Database Update Fuzzy control process 36
Discussion • System well suited for pro-active control • Learns behavior quickly • Needs tweaking of values and thresholds • Timer too small: data explosion • Timer too long: behavior not adaptive enough 37
Still there are many more problems to solve... Scale system up to hundreds of sensors and thousands of rules? Control Interfaces? Interaction between controller systems? 38
Research Work Covered A.Vainio et al. : Learning and adaptive fuzzy control system for smart home. H.Sunghoi et al. : Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment. Minkyoung Kim et al. : Behavior Coordination Mechanism for Intelligent Home. 39
SMART HOMES AND FUZZY LOGIC presented by Nicolas Bettenburg 1 1,000,000 years ago 2
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