disaggregated hot and cold water sensing with minimal calibration
semi-supervised training for infrastructure mediated sen...
how can indirect sensing and machine
learning be used to reduce our
environmental footprint?
3
lake mead 1983
4
lake mead 2011
5
we are using water faster
than it is being replenished
Pacific Institute for Studies in Development, Environment, and Secur...
we are using water faster
than it is being replenished
Pacific Institute for Studies in Development, Environment, and Secur...
$2,994.83
7
8
water usage is vastly
misunderstood
eco-feedback
10
eco-feedback
11 image: gardena, inc
eco-feedback
11 image: gardena, inc
image: showersmart
image: iSave
eco-feedback
Geographic Comparisons
 Dashboards
Metaphorical Unit Designs
 Recommendations
12
eco-feedback
13
eco-feedback
14
eco-feedback
15
eco-feedback
16
eco-feedback
17 video: courtesy Jon Froehlich
what are the potential
water savings?
eco-feedback in electricity
19
0%
5%
10%
15%
20%
1 2 3 4 5 Untitled 1
20%
12%
9.2%8.4%
6.8%
3.8%
Enhanced
Billing
Web
Based
Daily
Feedback
Realtime
Feedb...
how can we sense
water usage?
22
15
Turbine
Insert
Thermistor
Flow
image: LBNL
22
15
Turbine
Insert
Thermistor
Flow
image: LBNL
meters
flow rate fixture flow
inline water
23
meters
flow rate fixture flow
inline water
water
pressure
pressure
sensor
machine
learning
estimated
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
25
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
25
40#
50#
60#
70#
80#
Cold Lin...
HydroSense
26
kitchen sink
upstairs toilet
template matching
unknown event
27
downstairs toilet
kitchen sink
upstairs toilet
template matching
unknown event
27
downstairs toilet
feasibility study
• 10 homes
• staged calibration
• ~98% accuracy
Froehlich, J., Larson, E., Campbell, T., Haggerty, C., F...
70
50
30
pressure(psi)
70
50
30
pressure(psi)
initial study: staged events
kitchen sink kitchen sink
29
70
50
30
pressure(psi)
70
50
30
pressure(psi)
natural water use
30
how well does HydroSense work
in a natural setting?
longitudinal evaluation
32
33
34
35
36
totals
days
 33
 33
 30
 27
 33
 156
events
 2374
 3075
 4754
 2499
 2578
 14,960
events/day
 71.9
 93.2
 158.5
 92.6
 78....
bathroom sink
natural water usage
70
50
30
pressure(psi)
70
50
30
pressure(psi)
8AM
2 minutes
38
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pressure(psi)
70
50
30
pressure(psi)
8AM
2 min...
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pressure(psi)
70
50
30
pressure(psi)
template ...
need a more realistic approach
templates feature vectors
matching parametric model
minimize training
39
need a more realistic approach
templates feature vectors
matching parametric model
minimize training
39
10 psi
7.32 psi
15 Hz
200 ms
feature vectors: dense features
43
10 psi
7.32 psi
15 Hz
200 ms
feature vectors: dense features
xd
1
44
feature vectors: sparse features
unlabeledinstances
xd
1
45
feature vectors: sparse features
codebook
x1
x56
x132
x240
0
0...
0
0...
0
0...
0
0...
xd
1
46
feature vectors: sparse features
codebook
xd
1
xs
1
70
50
30
pressure(psi)
70
50
30
pressure(psi)
xd
1
xs
1 xs
2
xd
2 xd
3
xs
3
xd
4
xs
4
xd
5
xs
5
xd
6
xs
6 xs
7
xd
7
featur...
templates
matching parametric model
feature vectors
Traditional Methods
KNN
SVM
Decision Trees
CRF
DBN (i.e., HMM)
Ensembl...
minimal training set
0
10
20
30
40
50
60
70
80
90
100
NN KNN TM HMM KNN-sub SVM HMM-TM CRF TB SVM+CRF TB+CRF
valvelevelacc...
55%
valve
fixture
64%
category
78%
supervised results summary
53
fixture level confusions
KitchenSink
MasterBathroomSink
SecondaryBathroomSink
SecondaryBathroomToilet
MasterBathroomToilet
...
accuracy
trusted
not trusted
how accurate should the system be?
how can we be sure the user trusts the system?
highly crit...
accuracy
trusted
not trusted
category 78%
~80%
~99%
85%
90%
80%
goalsminimal
55%valve
64%fixture
laundry
dishwasher
noticea...
time
psi
fill
fill
fill
fill
cycle
cycle
cycle
57
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
0.5 1 1.5 2 2.5 3 3.5 4
hours
template
5...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
0.5 1 1.5 2 2.5 3 3.5 4
hours
template
X...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
0.5 1 1.5 2 2.5 3 3.5 4
hours
template
X...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
0 0.5 1 1.5 2 2.5 3 3.5 4
hours
template...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
0 0.5 1 1.5 2 2.5 3 3.5
46
48
50
52
54
5...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
pressure difference
time difference
X
n
...
0 0.5 1 1.5 2 2.5 3 3.5 4
46
48
50
52
54
56
58
60
hours
psi finding the dishwasher
pressure difference
time difference
X
n
...
laundry
dishwasher
true
positives
false alarms precision
73% 15 90%
75% 16 89%
43% 58 85%
39% 119 82%
75% of all showering...
accuracy
trusted
not trusted
category 78%
~80%
~99%
85%
90%
80%
goalsminimal
55%valve
64%fixture
laundry
dishwasher
noticea...
leveraging unlabeled data
labeled unlabeled
classifier classifier
feature set 2
high confidence high confidence
agree?
feature...
training 48%
49%
labeled unlabeled
multi-view classification
self labeled
TB
SVM+CRF
55%
53%
feature split: hot sensor vs. ...
0 5 10 15 20
46
48
50
52
54
56
58
60
time of day (hours)
psi
kitchen sink, hot master bathroom toilet
multi-view classifica...
semi-supervised learning
rule based classifier
0 0.5 1 1.5 2
46
48
50
52
54
56
58
60
hourspsi
self labels
expert
knowledge
...
A B
C Dvirtual
evidence
1
semi-supervised learning
virtual evidence
kitchen sink kitchen sink
P=1
bath sink bath sink
P=1
...
semi-supervised learning
virtual evidence
67
semi-supervised learning
virtual evidence
68
semi-supervised learning
virtual evidence
69
semi-supervised learning
virtual evidence
70
semi-supervised learning
virtual evidence
71
semi-supervised learning
results
0
10
20
30
40
50
60
70
80
90
100
TM HMM SVM TB SVM+CRF TB+CRF HMM-VE-Co
valvelevelaccurac...
room shower.
57
6.6
0.6
1.1
4.2
56
1.7
0.8
6.4
4.6
31
0.8
82
1.5
9.1
4.3
14
1.5
2.6
4.2
2.0
6.1
1.1
8.8
1.5
28
2.0
81
10
9...
room shower.
57
6.6
0.6
1.1
4.2
56
1.7
0.8
6.4
4.6
31
0.8
82
1.5
9.1
4.3
14
1.5
2.6
4.2
2.0
6.1
1.1
8.8
1.5
28
2.0
81
10
9...
semi-supervised learning
leveraging the homeowner
which labels are needed most?
can we leverage multi-view models?
74
semi-supervised learning
leveraging the homeowner
can we leverage multi-view models?
• select low confidence examples
• ask...
semi-supervised learning
leveraging the homeowner
can we leverage multi-view models?
• select low confidence examples
• ask...
semi-supervised learning
leveraging the homeowner
can we leverage multi-view models?
• select low confidence examples
• ask...
simulating labels from homeowner
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
• ask for two labels every other d...
10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85
Co−Labeling in H1
Number of Labels
ValveLevelAccuracyofCoLabel−HMM
Co−Label...
totals
totals
0 1 2 3 4 5 6 7 8 9 10 11 12 13
60%
70%
80%
90%
100%
valve fixture category
error bars=std err.
co-label iter...
usion is the secondary bathroom shower for the master bathroom shower.
-374
8.5
1.5
-3
6.1
72
1.1
0.8
5.0
0.7
1.5
0.6
-7
1...
implications for homeowner
week one
• homeowner installs system
• 1-2 examples per fixture
• show sparse classes:
dishwashe...
implications for homeowner
week one
• homeowner installs system
• 1-2 examples per fixture
• show sparse classes:
dishwashe...
implications for homeowner
week one
• homeowner installs system
• 1-2 examples per fixture
• show sparse classes:
dishwashe...
implications for homeowner
week one
• homeowner installs system
• 1-2 examples per fixture
• show sparse classes:
dishwashe...
summary contributions
• comprehensive disaggregated dataset
• multi-view classification
• expert knowledge
• compressed sen...
how can indirect sensing and machine
learning be used to reduce our
environmental footprint?
disaggregated hot and cold water sensing with minimal calibration
semi-supervised training for infrastructure mediated sen...
disaggregated hot and cold water sensing with minimal calibration
semi-supervised training for infrastructure mediated sen...
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Larson.defense

  1. 1. disaggregated hot and cold water sensing with minimal calibration semi-supervised training for infrastructure mediated sensing Eric C. Larson UbiComp Lab electrical engineering computer science and engineering University of Washington
  2. 2. how can indirect sensing and machine learning be used to reduce our environmental footprint?
  3. 3. 3
  4. 4. lake mead 1983 4
  5. 5. lake mead 2011 5
  6. 6. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011 6
  7. 7. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011 6 image: weiku.com
  8. 8. $2,994.83 7
  9. 9. 8
  10. 10. water usage is vastly misunderstood
  11. 11. eco-feedback 10
  12. 12. eco-feedback 11 image: gardena, inc
  13. 13. eco-feedback 11 image: gardena, inc image: showersmart image: iSave
  14. 14. eco-feedback Geographic Comparisons Dashboards Metaphorical Unit Designs Recommendations 12
  15. 15. eco-feedback 13
  16. 16. eco-feedback 14
  17. 17. eco-feedback 15
  18. 18. eco-feedback 16
  19. 19. eco-feedback 17 video: courtesy Jon Froehlich
  20. 20. what are the potential water savings?
  21. 21. eco-feedback in electricity 19
  22. 22. 0% 5% 10% 15% 20% 1 2 3 4 5 Untitled 1 20% 12% 9.2%8.4% 6.8% 3.8% Enhanced Billing Web Based Daily Feedback Realtime Feedback Appliance Level + Personalized Feedback Annual%Savings Based on 36 studies between 1995-2010. Summarized by Ehrhardt-Matinez et al. >20% reduction: Gardner et al. (2008) and Laitner et al. (2009) Appliance Level eco-feedback in electricity aggregate disaggregated Courtesy: Sidhant Gupta 20
  23. 23. how can we sense water usage?
  24. 24. 22 15 Turbine Insert Thermistor Flow image: LBNL
  25. 25. 22 15 Turbine Insert Thermistor Flow image: LBNL
  26. 26. meters flow rate fixture flow inline water 23
  27. 27. meters flow rate fixture flow inline water water pressure pressure sensor machine learning estimated
  28. 28. • central sensing point • easy to install • low cost • can observe every fixture HydroSense 25
  29. 29. • central sensing point • easy to install • low cost • can observe every fixture HydroSense 25 40# 50# 60# 70# 80# Cold Line Pressure (Hose Spigot) 0 94.5 time (s) psi open close
  30. 30. HydroSense 26
  31. 31. kitchen sink upstairs toilet template matching unknown event 27 downstairs toilet
  32. 32. kitchen sink upstairs toilet template matching unknown event 27 downstairs toilet
  33. 33. feasibility study • 10 homes • staged calibration • ~98% accuracy Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244. Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged experiments. Pervasive and Mobile Computing, (2010). 28
  34. 34. 70 50 30 pressure(psi) 70 50 30 pressure(psi) initial study: staged events kitchen sink kitchen sink 29
  35. 35. 70 50 30 pressure(psi) 70 50 30 pressure(psi) natural water use 30
  36. 36. how well does HydroSense work in a natural setting?
  37. 37. longitudinal evaluation 32
  38. 38. 33
  39. 39. 34
  40. 40. 35
  41. 41. 36
  42. 42. totals days 33 33 30 27 33 156 events 2374 3075 4754 2499 2578 14,960 events/day 71.9 93.2 158.5 92.6 78.1 95.9 compound 22.2% 21.8% 16.6% 32% 21.3% 21.9% data collection Larson, E., Froehlich, J., Saba, E., et al. A Longitudinal Study of Pressure Sensing to Infer Real- World Water Usage Events in the Home. Pervasive Computing, Springer (2011), 50–69. most comprehensive labeled dataset of hot and cold water ever collected 37
  43. 43. bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes 38
  44. 44. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes 38
  45. 45. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 74% 10 fold cross validation 35% 8AM 2 minutes minimal 38
  46. 46. need a more realistic approach templates feature vectors matching parametric model minimize training 39
  47. 47. need a more realistic approach templates feature vectors matching parametric model minimize training 39
  48. 48. 10 psi 7.32 psi 15 Hz 200 ms feature vectors: dense features 43
  49. 49. 10 psi 7.32 psi 15 Hz 200 ms feature vectors: dense features xd 1 44
  50. 50. feature vectors: sparse features unlabeledinstances xd 1 45
  51. 51. feature vectors: sparse features codebook x1 x56 x132 x240 0 0... 0 0... 0 0... 0 0... xd 1 46
  52. 52. feature vectors: sparse features codebook xd 1 xs 1
  53. 53. 70 50 30 pressure(psi) 70 50 30 pressure(psi) xd 1 xs 1 xs 2 xd 2 xd 3 xs 3 xd 4 xs 4 xd 5 xs 5 xd 6 xs 6 xs 7 xd 7 feature vectors: sequence
  54. 54. templates matching parametric model feature vectors Traditional Methods KNN SVM Decision Trees CRF DBN (i.e., HMM) Ensemble Methods KNN-subspace Bagged Trees Stacking Methods TB+CRF SVM+CRF TB+DBN 51
  55. 55. minimal training set 0 10 20 30 40 50 60 70 80 90 100 NN KNN TM HMM KNN-sub SVM HMM-TM CRF TB SVM+CRF TB+CRF valvelevelaccuracy(%) error bars=std err. 1-2 labels per valve 52 densefeatures sparsefeatures
  56. 56. 55% valve fixture 64% category 78% supervised results summary 53
  57. 57. fixture level confusions KitchenSink MasterBathroomSink SecondaryBathroomSink SecondaryBathroomToilet MasterBathroomToilet dishwasher laundry MasterBathroomBath/Shower MasterBathroomBath/Shower WashingMachine Dishwasher 54
  58. 58. accuracy trusted not trusted how accurate should the system be? how can we be sure the user trusts the system? highly critical noticeable Lim, B. and Dey, A. Investigating intelligibility for uncertain context-aware applications. Proceedings of the 13th international conference on ubiquitous computing, (2011), 415. ~80% ~99% 55
  59. 59. accuracy trusted not trusted category 78% ~80% ~99% 85% 90% 80% goalsminimal 55%valve 64%fixture laundry dishwasher noticeable 10% 8% 0% 56
  60. 60. time psi fill fill fill fill cycle cycle cycle 57
  61. 61. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher 0.5 1 1.5 2 2.5 3 3.5 4 hours template 59 dishwasher
  62. 62. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher 0.5 1 1.5 2 2.5 3 3.5 4 hours template X n tn 59 dishwasher
  63. 63. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher 0.5 1 1.5 2 2.5 3 3.5 4 hours template X n tn X n pn 59 dishwasher
  64. 64. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher 0 0.5 1 1.5 2 2.5 3 3.5 4 hours template pressure difference time difference X n tn X n pn 59 dishwasher
  65. 65. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher 0 0.5 1 1.5 2 2.5 3 3.5 46 48 50 52 54 56 58 60 hours psi template pressure difference time difference X n tn X n pn 59 dishwasher
  66. 66. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher pressure difference time difference X n tn X n pn 59 dishwasher
  67. 67. 0 0.5 1 1.5 2 2.5 3 3.5 4 46 48 50 52 54 56 58 60 hours psi finding the dishwasher pressure difference time difference X n tn X n pn X n tcyclelaundry machine 59 laundry dishwasher
  68. 68. laundry dishwasher true positives false alarms precision 73% 15 90% 75% 16 89% 43% 58 85% 39% 119 82% 75% of all showering category 60
  69. 69. accuracy trusted not trusted category 78% ~80% ~99% 85% 90% 80% goalsminimal 55%valve 64%fixture laundry dishwasher noticeable 10% 8% 0% 61
  70. 70. leveraging unlabeled data labeled unlabeled classifier classifier feature set 2 high confidence high confidence agree? feature set 1 self labeled multi-view classification 62
  71. 71. training 48% 49% labeled unlabeled multi-view classification self labeled TB SVM+CRF 55% 53% feature split: hot sensor vs. cold sensor 88% 90%TB SVM+CRF 99% dense features sparse features 63 training training training
  72. 72. 0 5 10 15 20 46 48 50 52 54 56 58 60 time of day (hours) psi kitchen sink, hot master bathroom toilet multi-view classification 64
  73. 73. semi-supervised learning rule based classifier 0 0.5 1 1.5 2 46 48 50 52 54 56 58 60 hourspsi self labels expert knowledge virtual evidence 65
  74. 74. A B C Dvirtual evidence 1 semi-supervised learning virtual evidence kitchen sink kitchen sink P=1 bath sink bath sink P=1 toilet toilet P=1 P=0.01 otherwise 66 arg max A,B P(A)P(B|A)P(C = c|A)P(D = d|B)P(ve|A, B)
  75. 75. semi-supervised learning virtual evidence 67
  76. 76. semi-supervised learning virtual evidence 68
  77. 77. semi-supervised learning virtual evidence 69
  78. 78. semi-supervised learning virtual evidence 70
  79. 79. semi-supervised learning virtual evidence 71
  80. 80. semi-supervised learning results 0 10 20 30 40 50 60 70 80 90 100 TM HMM SVM TB SVM+CRF TB+CRF HMM-VE-Co valvelevelaccuracy(%) 10 fold cross validation 72
  81. 81. room shower. 57 6.6 0.6 1.1 4.2 56 1.7 0.8 6.4 4.6 31 0.8 82 1.5 9.1 4.3 14 1.5 2.6 4.2 2.0 6.1 1.1 8.8 1.5 28 2.0 81 10 9.1 2.0 15 1.0 3.0 22 6.1 6.7 8.5 2.0 6.5 0.6 33 3.3 1.0 0.8 0.5 0.7 17 5.0 39 1.0 1.5 0.6 0.8 24 4.4 0.7 2.5 6.8 1.1 11 6.5 65 2.4 5.4 0.8 3.1 7.9 2.5 6.0 0.9 7.8 5.7 14 3.0 68 1.0 1.6 3.5 1.5 3.9 1.6 9.5 4.8 4.3 1.4 83 0.8 1.4 17 1.9 3.1 3.5 1.9 91 2.5 0.8 11 17 6.4 42 2.4 60 4.3 1.0 3.4 4.8 7.9 0.6 3.7 7.5 1.6 86 1.6 8.4 1.5 1.7 0.7 4.5 1.7 5.7 1.0 6.8 0.6 3.0 3.1 1.9 85 6.4 2.2 6.1 0.7 66 3.2 1.1 38 14 9.1 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 r close,1 open,2 k close,3 open,4 r close,5 open,6 k close,7 open,8 t close,9 open,10 close,11 open,12 close,13 open,14 open,15 close,16 open,17 dishwasher laundry dishwasher laundry 73
  82. 82. room shower. 57 6.6 0.6 1.1 4.2 56 1.7 0.8 6.4 4.6 31 0.8 82 1.5 9.1 4.3 14 1.5 2.6 4.2 2.0 6.1 1.1 8.8 1.5 28 2.0 81 10 9.1 2.0 15 1.0 3.0 22 6.1 6.7 8.5 2.0 6.5 0.6 33 3.3 1.0 0.8 0.5 0.7 17 5.0 39 1.0 1.5 0.6 0.8 24 4.4 0.7 2.5 6.8 1.1 11 6.5 65 2.4 5.4 0.8 3.1 7.9 2.5 6.0 0.9 7.8 5.7 14 3.0 68 1.0 1.6 3.5 1.5 3.9 1.6 9.5 4.8 4.3 1.4 83 0.8 1.4 17 1.9 3.1 3.5 1.9 91 2.5 0.8 11 17 6.4 42 2.4 60 4.3 1.0 3.4 4.8 7.9 0.6 3.7 7.5 1.6 86 1.6 8.4 1.5 1.7 0.7 4.5 1.7 5.7 1.0 6.8 0.6 3.0 3.1 1.9 85 6.4 2.2 6.1 0.7 66 3.2 1.1 38 14 9.1 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 r close,1 open,2 k close,3 open,4 r close,5 open,6 k close,7 open,8 t close,9 open,10 close,11 open,12 close,13 open,14 open,15 close,16 open,17 DW Shower Shower CW dishwasher laundry dishwasher laundry 73
  83. 83. semi-supervised learning leveraging the homeowner which labels are needed most? can we leverage multi-view models? 74
  84. 84. semi-supervised learning leveraging the homeowner can we leverage multi-view models? • select low confidence examples • ask homeowner for label 75
  85. 85. semi-supervised learning leveraging the homeowner can we leverage multi-view models? • select low confidence examples • ask homeowner for label AT&T LTEAT&T LTE 5:23 PM did you just use water? YesNo 75
  86. 86. semi-supervised learning leveraging the homeowner can we leverage multi-view models? • select low confidence examples • ask homeowner for label AT&T LTEAT&T LTE 5:23 PM did you just use water? YesNo 75 AT&T LTEAT&T LTE 5:23 PM Fixture Selection Do Not Disturb Select Notification Times OFF Master Toilet Recently Used: Half Bath Toilet Dishwasher Master Sink Select Temp. Kitchen Sink Select Temp. Master Shower Select Temp. More Half Bath Sink Select Temp.
  87. 87. simulating labels from homeowner AT&T LTEAT&T LTE 5:23 PM did you just use water? YesNo • ask for two labels every other day • one morning and one evening • only from 8AM-9PM • randomly ask for previous event 76 AT&T LTEAT&T LTE 5:23 PM Fixture Selection Do Not Disturb Select Notification Times OFF Master Toilet Recently Used: Half Bath Toilet Dishwasher Master Sink Select Temp. Kitchen Sink Select Temp. Master Shower Select Temp. More Half Bath Sink Select Temp.
  88. 88. 10 15 20 25 30 35 40 45 0.65 0.7 0.75 0.8 0.85 Co−Labeling in H1 Number of Labels ValveLevelAccuracyofCoLabel−HMM Co−Labeling Random Labeling co-labeling random labeling iteration 1 iteration 3 iteration 5 iteration 10 simulating labels from homeowner co-labeling for H1minimaltrainingset totals days 33 33 30 27 33 156 events 2374 3075 4754 2499 2578 14,960 events/day 71.9 93.2 158.5 92.6 78.1 95.9 compound 22.2% 21.8% 16.6% 32% 21.3% 21.9% 77
  89. 89. totals totals 0 1 2 3 4 5 6 7 8 9 10 11 12 13 60% 70% 80% 90% 100% valve fixture category error bars=std err. co-label iteration accuracy 78
  90. 90. usion is the secondary bathroom shower for the master bathroom shower. -374 8.5 1.5 -3 6.1 72 1.1 0.8 5.0 0.7 1.5 0.6 -7 18 92 0.6 5.4 2.6 4.9 3.4 2.3 3.5 0.9 5.1 -7 18 0.9 92 1.4 4.9 0.7 4.4 1.4 1.4 7.3 1.0 0.7 3.2 -753 2.1 -1 4.2 67 0.6 2.4 0.8 31 2.5 5.2 -5 1.2 5.0 0.6 18 4.2 88 1.8 7.5 1.5 1.6 5.7 1.0 -5 1.2 0.5 5.3 4.6 10 1.8 89 1.7 1.7 1.4 0.7 3.2 -2 2.9 0.6 81 12 -2 0.8 94 0.6 8.1 -1 6.7 68 -2 2.8 42 -1 1.9 2.9 0.5 25 0.8 95 0.7 -1 2.2 3.1 10 1.3 96 6.6 -290 -3 1.1 0.7 59 18 -3 0.9 0.9 15 60 er close,1 open,2 k close,3 open,4 er close,5 open,6 k close,7 open,8 et close,9 open,10 r close,11 open,12 k close,13 open,14 open,15 e close,16 open,17 dishwasher laundry dishwasher laundry 79
  91. 91. implications for homeowner week one • homeowner installs system • 1-2 examples per fixture • show sparse classes: dishwasher, shower, laundry 80
  92. 92. implications for homeowner week one • homeowner installs system • 1-2 examples per fixture • show sparse classes: dishwasher, shower, laundry week two • 2-4 labels, every 2 days • fixture category: 85% 80
  93. 93. implications for homeowner week one • homeowner installs system • 1-2 examples per fixture • show sparse classes: dishwasher, shower, laundry week two • 2-4 labels, every 2 days • fixture category: 85% week three • 9-12 more examples • fixture: 82% 80
  94. 94. implications for homeowner week one • homeowner installs system • 1-2 examples per fixture • show sparse classes: dishwasher, shower, laundry week two • 2-4 labels, every 2 days • fixture category: 85% week three • 9-12 more examples • fixture: 82% end of week three • fixture: 87% • valve: 80% 80
  95. 95. summary contributions • comprehensive disaggregated dataset • multi-view classification • expert knowledge • compressed sensing • framework for virtual evidence in IMS • co-labeling with multi-view • idea: inception to industry ready 81
  96. 96. how can indirect sensing and machine learning be used to reduce our environmental footprint?
  97. 97. disaggregated hot and cold water sensing with minimal calibration semi-supervised training for infrastructure mediated sensing UbiComp Lab electrical engineering computer science and engineering University of Washington eclarson.com eclarson@uw.edu @ec_larson Eric C. Larson
  98. 98. disaggregated hot and cold water sensing with minimal calibration semi-supervised training for infrastructure mediated sensing UbiComp Lab electrical engineering computer science and engineering University of Washington eclarson.com eclarson@uw.edu @ec_larson Eric C. Larson

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