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An Identification Method of IR Signals to Collect Control Logs of Home Appliances

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2017 ACIS Conference Series BCD
July 11, 2017

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An Identification Method of IR Signals to Collect Control Logs of Home Appliances

  1. 1. An Identification Method of IR Signals to Collect Control Logs of Home Appliances 〇Yuta Takahashi1,Teruhiro Mizumoto1 1. Nara Institute of Science and Technology 2017 ACIS Conference Series BCD July 11, 2017
  2. 2. Background & Motivation ❖Control logs of home appliance 2 ❖More intelligent smart home Log 18:00 Cold 24℃ - ON/OFF - Channel - Volume - Temperature … Home which can understand user’s preference - Automation - Energy efficient - Recommendation Smart home Goal
  3. 3. Method for collecting control logs ❖Information appliance 3 ❖Estimation by electric consumption 〇 Accurate logs 〇 Remote control Products are not diverse 〇 Compatible with various products Need for attachments (smart mater) Difficult to estimate detail usage
  4. 4. IR signal & Problems ❖Collecting IR signals 4 ❖Problems of identification ▪ Many protocols (NEC, AEHA…) ▪ Repeater functions ▪ Environmental noise - Various appliances are controlled by IR - Installing IR receiver to each room Difficult to identifying
  5. 5. Proposed method ❖Process of IR signal 5 IR remote controller Preprocess Comparison IR Database Identification of appliance type Identification of command type Unknown signal No match Command type Appliance type IR receiver Identifying by statistical model
  6. 6. Preprosess ❖Raw IR signal ▪ Consist of high/low pulses (PWM, Pulse Width Modulation) ▪ High memory-capacity for devices ▪ High computation for identifying 6 Raw IR Pulse width sequence ❖Pulse width sequence ▪ Consist of length of high/low pulses ▪ Range is 0 to 255 ▪ Easy to handle ▪ Low memory-capacity
  7. 7. Comparison method of two signals 7 Two signals 𝑆1 0 𝑆1 1 𝑆1 2 𝑆1 3 𝑆1 4 𝑆1 5 𝑆1 6 𝑆1 7 𝑆1 0 𝑆1 1 𝑆1 2 𝑆1 1 𝑆1 2 𝑆1 3 𝑆1 2 𝑆1 3 𝑆1 4 𝑆1 𝑆𝑠𝑢𝑏 𝑆2 𝑆2 0 𝑆2 1 𝑆2 2 𝑆2 0 𝑆2 1 𝑆2 2 𝑆2 0 𝑆2 1 𝑆2 2 𝑀𝐴𝐸0, 𝑆𝐴𝐸0 𝑀𝐴𝐸1, 𝑆𝐴𝐸1 𝑀𝐴𝐸2, 𝑆𝐴𝐸2 𝑝 = arg min(𝑀𝐴𝐸 𝑛) 𝑴𝑨𝑬 𝒑, 𝑺𝑨𝑬 𝒑 A captured signal A referenced signal 𝑀𝑒𝑎𝑛 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 = 1 𝑁 ෍ 𝑖=0 𝑁 |𝑆𝑠𝑢𝑏 𝑖 − 𝑆2 𝑖 | Sum 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 = σ𝑖=0 𝑁 |𝑆𝑠𝑢𝑏 𝑖 − 𝑆2 𝑖 | (long) (short)
  8. 8. Dataset 8 14 appliances ↓ 140 commands 10 signals ↓ 1,400 signals irMagician 1400 2 = 979,300 combinations
  9. 9. Error frequency of same appliance and other appliance 9 Same appliance (any command) : Other appliance (any command) : A appliance A1 command A appliance A2 command A appliance A1 command B appliance B1 command Small overlapped Difficult to fit a model (over fitting) Constructing a model of “same appliance” of MAE
  10. 10. Model for identifying appliance type ❖Fitting ▪ Inverse gaussian, Gamma, Inverse gamma, Weibull, Chi and F distributions ▪ Maximum likelihood estimation ▪ AIC (Akaike's Information Criterion) ▪ Inverse gamma (k=3) and F (k=4) are best fitting 10 ❖Decision ▪ 95% confidence interval ▪ 𝑒 ≤ 𝑒𝑡ℎ : same appliance ▪ 𝑒 > 𝑒𝑡ℎ : other appliance Bad fitting (Weibull) Inverse gamma 95% 5% 3.72 𝑒𝑡ℎ
  11. 11. Error frequency of same command and other command 11 Same command (same appliance) : Other command (same appliance) : A appliance A1 command A appliance A1 command A appliance A1 command A appliance A2 command Good shape of histogram Constructing a model of “same & other command” of SAE
  12. 12. Model for identifying command type ❖Fitting ▪ Inverse gaussian, Inverse gamma and F are better than other ▪ We chose Inverse gamma as well as model of appliance type 12 ❖Decision ▪ Bayes’ decision 𝑙𝑜𝑔 𝑝 𝑦 = "same"|𝑥 𝑝 𝑦 = "other" 𝑥 ▪ Positive : same command ▪ Negative : other command
  13. 13. Evaluations 1. Accuracy of identifying appliance type ▪ Verifying by 10-fold cross validation 2. Accuracy of identifying command type ▪ Verifying by 10-fold cross validation 3. Simple simulation ▪ Identification depends on signals in database ▪ Constructing database randomly ▪ Check how many signals are needed in database 13
  14. 14. Identification accuracy 14 ❖Accuracy of appliance type (total support : 199,778) ❖Accuracy of command type (total support : 12,636)
  15. 15. Result of simple simulation ▪ Simulating 1,400 signals in each number of appliances ▪ Correct match rate is stable if 6 signals, or more, are included in the database 15 Stable
  16. 16. Conclusions ❖Proposed method for identifying IR signal by statistical model ❖Identifying appliance accuracy is 95.5% ❖Identifying command accuracy is 92.0% ❖Identification stability is achieved when 6 signals, or more, of each appliance are included in database ❖We plan to collect and identify the IR signals in real environment 16
  17. 17. Simple simulation Process 1. Construct database from signals of each appliance 2. Identifying the test signals 3. Increment the number of signals in database 17 ❖Matching method ▪ One appliance type most identified is selected ▪ No match : Several types are estimated or no types of identification Signal:TV Signal:TV Signal:TV Signal:Fan → TV → TV → Fan Signals identified as same appliance TV Test Compared signals Labeled appliance & command to signals Database Result

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