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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
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
- RecommendationSmart home
Goal
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
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
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
Preprosess
❖Raw IR signal▪ Consist of high/low pulses (PWM, Pulse Width Modulation)
▪ High memory-capacity for devices
▪ High computation for identifying
6
Raw IRPulse width
sequence
❖Pulse width sequence▪ Consist of length of
high/low pulses
▪ Range is 0 to 255
▪ Easy to handle
▪ Low memory-capacity
Comparison method of two signals
7
Two signals
𝑆10
𝑆11
𝑆12
𝑆13
𝑆14
𝑆15
𝑆16
𝑆17
𝑆10
𝑆11
𝑆12
𝑆11
𝑆12
𝑆13
𝑆12
𝑆13
𝑆14
𝑆1
𝑆𝑠𝑢𝑏
𝑆2 𝑆20
𝑆21
𝑆22
𝑆20
𝑆21
𝑆22
𝑆20
𝑆21
𝑆22
𝑀𝐴𝐸0, 𝑆𝐴𝐸0 𝑀𝐴𝐸1, 𝑆𝐴𝐸1 𝑀𝐴𝐸2, 𝑆𝐴𝐸2
𝑝 = argmin(𝑀𝐴𝐸𝑛) 𝑴𝑨𝑬𝒑, 𝑺𝑨𝑬𝒑
A captured signal
A referenced signal
𝑀𝑒𝑎𝑛 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 =1
𝑁
𝑖=0
𝑁
|𝑆𝑠𝑢𝑏𝑖
− 𝑆2𝑖|
Sum 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 = σ𝑖=0𝑁 |𝑆𝑠𝑢𝑏
𝑖− 𝑆2
𝑖|
(long)
(short)
Dataset
8
14 appliances
↓
140 commands
10 signals
↓
1,400 signals
irMagician1400
2= 979,300 combinations
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
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
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
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
𝑙𝑜𝑔𝑝 𝑦 = "same"|𝑥
𝑝 𝑦 = "other" 𝑥
▪ Positive : same command
▪Negative : other command
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
Identification accuracy
14
❖Accuracy of appliance type (total support : 199,778)
❖Accuracy of command type (total support : 12,636)
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
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
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