Hydro Sense Languagemodel
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A language model approach to extracting water usage

A language model approach to extracting water usage

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Hydro Sense Languagemodel Presentation Transcript

  • 1. Disaggregated Water Activity Sensing via a Single Sensor: a Language Modeling Approach design: use: electrical computer build: engineering science univ. of and engineering
  • 2. hydrosense toilet kitchen sink shower
  • 3. 72 89 1398 778 24 98 78 92 2350 1664 926 1308 29 algorithmcanbeimplemented realtimetrendtowardparallelpro- 41 widerange.Finally,if thecurrentwith calculations in 101 2797 778 24 84 95 1978 1100 cessing frequencies thedownsampled of machines realized, carried in parallel of the 34 each thecenter for is being out version constant Q features 104 3327 654 20 90 98 2350 926 29 107 3956 550 17 by 156 processors. beimplemented realtimewith calculations algorithmcan in 96 101 2797 778 24 110 102 104 4710 3327 462 654 14 20 for each thecenter of frequencies beingcarried in parallel out 113 5608 388 12 108 107 3956 550 17 RESULTS III. by 156 processors. 116 6675 326 10 114 110 4710 462 14 i19 120 113 7942 5608 274 388 9 12 All calculations were programmed C and run on a in 122 9461 230 7 III. RESULTS 126 116 6675 326 HewlettPackardModel 9000Series "Bobcat"Comput- 10 300 125 11 216 194 6 132 i19 7942 274 er. For thoseAll calculations can be obtained from brow-run on a 9 interested,the code programmed C and were in 128 13 432 162 5 n @ems.media.mit.edu Model 9000Series "Bobcat"Comput- HewlettPackard the arpanet.Examples sounds on 300 of toilet 138 122 9461 230 7 144 125 11 216 194 6 of musicalinstruments er. For thoseinterested,the codecan be live perfor- brow- were digitized from obtained from 150 128 13 432 162 5 mances @ems.media.mit.edu the arpanet.Examples sounds in theMusicandCognition n on Groupat Massachusetts of Instituteof Technology. Otherexamples of musicalinstruments weregenerated us- perfor- were digitized from live ing Barry Vercoe's MusicandCognition in Csoundsoftware. mances the The calculationMassachusetts Groupat is car- cies. Second, bandwidthis less the than the frequen- riedoutevery500samples corresponding about15msat a Instituteof Technology. to Otherexamples weregenerated us- plingintervalfor the binswhereQ = 68. The latter sampling Barry Vercoe's ing of 32 000 samples/s, it should calculation car- rate Csoundsoftware. be recalled is but The requencies. problemsinceone of is lessthan the frequen- Eq. (3) that differentfrequencies analyzed 15msat a considered a Second, bandwidththe real advan- the from riedoutevery500samples are to over corresponding about thismethodis that the the binswhereQ = 68. The latter analysis centerfrequencies differenttime periods.Examplesof the analysis cy sampling intervalfor but windows sampling of 32 000 samples/s, it should recalled rate be was not considered problemsinceone of the real advan- a from Eq. (3) that differentfrequencies analyzed are over ages thismethodis that the analysis of centerfrequencies differenttime periods.Examplesof the analysis windows FIG. 2. ConstantQ transformof three complex soundswith fundamentalsG 3 (196 Hz), (}4 (392 Hz), and G• (784 Hz), andeachhaving20 harmonics with ) equalamplitude. ConstantQ transformof three FIG. 2. complex soundswith fundamentalsG 3 (196 Hz), (}4 (392 Hz), and G• (784 .I Hz), andeachhaving20 harmonics with equalamplitude. FREQUENCY(Hz) '-• J. Acoust.Soc. Am., Vol. 89, No. 1, January 1991 JudithC. Brown:Constant O spectral transform 428
  • 4. constant Q features
  • 5. previous approach showe fixture library toilet r dishwashe kitchen faucet r bath faucet bath tub
  • 6. previous approach S1 S1 S1 S1 S2 S2 showe S2 fixture library toilet r S2 dishwashe kitchen faucet r bath faucet bath tub
  • 7. previous approach unclassified fixture fixture library showe toilet r dishwashe kitchen faucet bath faucet r bath tub S1 S2 S1 S2 S1 S2 S1 S2
  • 8. previous approach unclassified fixture fixture library showe toilet r dishwashe kitchen faucet bath faucet r bath tub S1 S2 S1 S2 S1 S2 arg max P(V|Cfeat., 1:T) S1 S2 valves Cfeat., 1:T = Constant-Q Cepstral
  • 9. HMM approach unclassified fixture fixture library showe toilet r dishwashe kitchen faucet bath faucet Disadvantage: series of valve r bath tub events S S 1 2 S1 S2 S1 S2 arg max P(V|Cfeat., 1:T) S1 S2 valves Cfeat., 1:T = Constant-Q Cepstral
  • 10. markov chain language model P(I,saw,the,house) ~P(I) P(saw|I) P(the|saw) P(house|the) P(I,saw,the,house) ~P(I) P(saw|I) P(the|I,saw) P(house| saw,the) I saw the house S S2 S3 S4 1 S1 S2 S1 S2 S1 S2 S1 S2 C1 C2 C3 C4 C5 C6 C7 C8
  • 11. generalized model valve event string P(v1, v2, v3,v4) ~P(v1) P(v2|v1) P(v3|v1,v2) ---P(v6|v4,v5) S1 S2 S1 S2 ... S1 S2 C1 C2 C3 C4 CN-1 CN
  • 12. S1 S2 S3 ... S4 s1 s2 s1 s2 s1 s2 ... s1 s2 C1 C2 C3 C4 C5 C6 ... C7 C8
  • 13. sparse N-grams valve event string P(v1, v2, v3,v4) ~ P(v1) P(v2|v1) P(v3|v1,v2) ---P(v6|v4,v5)  C(w i w i−1 ) /C(w i−1 ) , r > k  PKatz (w i | w i−1 ) =  drC(w i w i−1 ) /C(w i−1 ) , k ≥ r > 0  α (w )P(w ) , r = 0  i−1 i−1 € r * (k + 1)n k +1 n r +1 − 1− ∑ PKatz (wi | wi−1) r* = (r + 1) r n1 w i :r>0 nr dr = α (w i ) = 1− (k + 1)n k +1 1− ∑ P(w )i P(a) = r* w i :r>0 n1 N
  • 14. what about context?
  • 15. what about context?
  • 16. bayesian context valve event string P(v1, v2, v3,v4) ~P(v1) P(v2|v1) P(v3|v1,v2) ---P(v6|v4,v5) incorporate priors: P(v1) = P(v1|time of day, day of week) x P(v1|valve string active in last T minutes) x P(v1|type=open, close, quick,adjust) x (<-requires grammar) P(v1|type=close, duration of valve) x ... feature set superposition P(v1|C) vs. P(v1|C, type = open, close, quick, adjust)
  • 17. bayesian context feature set superposition P(v1|C) vs. P(v1|C, type = open, close, quick, adjust) isolated: faucet opening compound: faucet opened while toilet filling
  • 18. data collection - naturalistic usage - comprehensive staged events
  • 19. data collection - naturalistic usage - comprehensive staged events
  • 20. data collection - naturalistic usage - comprehensive staged events
  • 21. Disaggregated Water Activity Sensing via a Single Sensor: a Language Modeling Approach design: use: electrical computer build: engineering science univ. of and engineering