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LAB 3 AIRBAG DEPLOYMENT SENSOR PREDICTION NETWORK Warning This lab could save someone’s life!

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Presentation on theme: "LAB 3 AIRBAG DEPLOYMENT SENSOR PREDICTION NETWORK Warning This lab could save someone’s life!"— Presentation transcript:

1 LAB 3 AIRBAG DEPLOYMENT SENSOR PREDICTION NETWORK Warning This lab could save someone’s life!

2 Two Approaches to Investigate Lab 3 Part I: Time Series Forecasting Using Madaline Part II: Time Series Forecasting Using Tapped Delay Line Neural Network (TDNN) Submit formal report on Lab 3 by 11/2/04 (no class 10/26)

3 Part I: Time Series Forecasting Using Madaline  Aim is to predict airbag sensor output at time t +1 based on prior outputs at t, t-1, t-2, t-3 ……. using Madaline  Initially 3 delay elements will be applied to inputs Select and organize appropriate data file AIRBAGx for Training and Test of Madaline where x=last digit of your ss# Select Network Architecture with 1 hidden layer of nodes and one output Select suitable # of hidden layer nodes for problem giving reasons for your choice

4 Construct Madaline Network Train & Test. (Choose suitable epoch) Plot and Comment on Prediction Results How accurate is Madaline prediction for additional future time interval t+2 ? Compile a brief report on the results of Madaline forecasting

5 Example of Training File !Square Rotate Training File (80 samples) 3/27/04 !This data collected at 800 samples per second. !Shaker table moving at 20 Hz with a square wave. !Noise added to the sample by having the MEMs loosely mounted. !MEMS rotated during collection of samples. ! ! t -2 t-1 t t+1 2.07692.07692.07692.0769 2.07692.07692.07692.0134 2.07692.07692.01341.6813 2.07692.01341.68132.0403 2.01341.68132.04031.928 1.68132.04031.9282.1184 2.04031.9282.11842.1819 1.9282.11842.18192.2943 2.11842.18192.29432.4139 2.18192.29432.41392.4481 2.29432.41392.44812.475 2.41392.44812.4752.4945

6 Results for 3-3-1 Madaline Construct, Train and Test Madaline Report your test results using Excel chart Compute the RMS error and comment on accuracy of Madaline prediction

7 Test Results for 3-3-1 Madaline 2.3822001.510400 2.4872002.518900 2.4701002.518900 2.4872002.518900 2.4945002.518900 2.4896002.518900 2.4799002.518900 2.4676002.518900 2.4921002.518900 2.5018002.518900 2.4847002.518900 2.4945002.518900 2.4750002.518900 2.4701002.518900 2.4628002.518900 2.3529002.518900 2.1868001.510400 2.0891001.5104001.510400 1.9353002.518900 Actual Predicted

8 Part II: Time Series Forecasting Tapped Delay-Line Neural Network + Backprop Learning  Aim is to predict airbag sensor output at time t +1 based on prior outputs at t, t-1, t-2, t-3 ……. using a TDNN  Initially 3 delay elements will be applied to inputs Select and Organize Airbag appropriate data file AIRBAGx for Training and Test of TDNN where x=last digit of your ss# Select Network Architecture as Multilayer Perceptron Feedfoward with 1 hidden layer of nodes and one output Select # of hidden layer nodes giving reasons for your choice

9 Construct Network Train & Test. Choose suitable learning coefficient, momentum term and epoch Record train & test parameters as well as RMS Error and Classification Rate after experiment Plot and Comment on TDNN Prediction Results How accurate is your TDNN prediction for additional future time interval t+2 ? Repeat experiment for memory depth of 6 & compare with results for depth of 3 Note that in this scheme, delayed inputs have same weight as current input creating a Linear Trace Memory

10 Non-Linear Trace Memories Should more recent inputs have greater influence than older inputs? Empirically verify your answer by applying kernel function to delayed inputs to produce a non-linear trace memory Refer to Mohan page 140 – Use convolution of input sequence with kernel function c i One example is kernel function in which previous input has half the weight of input immediately succeeding it. Write MATLAB fn to compute transformed inputs  Use a memory depth of 6

11 My TDNN After Training

12 Test Results File 2.3822002.315152 2.4872002.398239 2.4701002.475807 2.4872002.463754 2.4945002.438213 2.4896002.453228 2.4799002.447508 2.4676002.437722 2.4921002.428249 2.5018002.442653 2.4847002.459934 2.4945002.445343 2.4750002.443128 2.4701002.439142 2.4628002.426756 2.3529002.426402 2.1868002.351290 2.0891002.184385 1.5104002.076567 1.9353001.641164 DESIRED PREDICTED RMS Test ERROR = 0.0602 Train for 20,000 presentations 80 Training Samples, 20 test Linear Trace Memory Memory Depth =3

13 TDNN Test Results Linear Trace Memory, Depth = 3

14 Devise a scheme for airbag deployment with advance warning using TDNN Predictor Assume under crash conditions, airbag sensor output voltage is in range 0.25± 0.25 or 4.75 ± 0.25 Modify your database to introduce a minimum of 2 crash conditions at least 10 time delays apart Does your TDNN predict impending crash conditions? How much notice does TDNN offer driver (in time delays) ? If one time delay = 1ms, can the average adult driver react to the information in time to prevent disaster? Comment on usefulness of Advance Warning scheme Airbag Deployment with Advance Warning

15 Automobile airbag sensors sometimes deploy accidentally leading to a many unnecessary car crashes Describe a scheme to enhance airbag sensor reliability through redundancy (multiple sensors) Airbag Sensor Reliability Enhancement

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