Control of Systems with MEMS Sensors and Actuators via Data Mining Techniques

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Nov 20, 2013 (3 years and 4 months ago)


Control of Systems with MEMS Sensors and Actuators via Data Mining Techniques

Wesley W. Chu*, Chih
Ming Ho**

*Computer Science Department

Department of Mechanical and Aerospace Engineering

University of California, Los Angeles

Contact Information

y W. Chu, P.I.

Computer Science Department

University of California, Los Angeles

Los Angeles, Ca 90095

Phone: (310)825

Fax: (310)825


Ming Ho, Co

Department of Mechanical an
d Aerospace Engineering

University of California, Los Angeles

Los Angeles, Ca 90095

Phone: (310)825

Fax: (310)206


WWW Page



MEMS sensors and actuators

Dynamic control

Delta wing flight control

Temporal and spatial data mining

Project Award Information

Award Number: IIS

Duration: 08/31/2001


Title: Control of Systems with M
EMS Sensors and Actuators via Data Mining Techniques

Project Summary

To process and interpret the vast amount of sensor information in determining the actuation schema to
control the system is an open problem. We use a novel data mining technique to deriv
e classification rules
and association rules from training datasets that consist of multivariate input and output variables. Based on
the system
operating environment, the best applicable rule can be selected to derive the actuation schema
that drives the
system to the desired state. The input
output relationship for delta wing aircraft is highly
linear, specifically the transfer function between the sensors and actuators is extremely complicated.
We plan to use the delta wing aircraft input/output MEMS

test bed samples to develop a scalable data
mining technique that discovers full input
output relationship under a wide range of conditions (dynamic,
temporal, spatial, etc).

This project leverages on our past data mining research of multivariate variabl
es training datasets, as well
as the available MEMS sensors and actuators for UAV (Unmanned Aerial Vehicle) application and wind
tunnel measurement facility to collect the training data. Our current research is on the design of
experiments to collect the d
ata for dynamic system behavior, extending the mining algorithm for
summarizing temporal rules, developing the rule selection strategy for actuation schema, and developing
the wind tunnel experiments to validate our research approach.

Goals, Objectives, a
nd Targeted Activities

The goal of our research is to discover non
linear relationships between the distributed sensor input and
actuation schema output by using data mining techniques. We have designed a set of experiments to collect
the data for dynamic
system behavior, and are extending the mining algorithms to handle massive amounts
of multivariate training data. We plan to develop methodology to generate temporal rules and strategy to
automatically select actuation rules for real time system control.

Indication of Success

We will discover rules for representing sensor and actuation relationships based on vast amounts of non
linear and temporal sensor input and physical output data. Such rules can be used for the flight control of
delta wing aircrafts u
nder dynamic movement.

GPRA Outcome Goals


Discoveries at and across the frontier of science and engineering.

Data mining techniques to generate actuation rules from distributed multivariate sensor data

We are developing methodology to generate temporal
rules that capture and summarize the characteristics
of dynamic data, to handle massive amount of multivariate sensor data and generate rules to represent the
multivariate input and output relations, and to automatically select actuation rules for real tim
e system
control. These data mining techniques are instrumental for any application requiring real time dynamic
control based on continuously streaming data from massively parallel sensors, so the results should play a
important role in controlling micro f
based systems.


Connections between discoveries and their use in service to society.

Develop techniques for dynamic system control

Together with the researchers at Department of Mechanical and Aerospace Engineering, we are developing
to automatically read information from massive distributed sensors, transfer the information to
actuation rules and select the most applicable rule to derive the actuation schema. This process can be used
in other real time control systems, such as environ
mental, medical, etc.

Project Reference

Z. Liu, W.W. Chu, A. Huang, C. Folk, C.M. Ho,
Mining Sequence Patterns from Wind Tunnel
Experimental Data for Flight Control.
PAKDD 2001: 270

G. Giuffrida, W.W. Chu, D.M. Hanssens,
NOAH: An Algorithm for Mining

Classification Rules from
Datasets with Large Attribute Space.

In Proceedings of 12

International Conference on Extending
Database(EDBT), Konsta, Germany, March 2000.

Q. Zou, W.W. Chu, D. Johnson, H. Chiu,
A Pattern Decomposition Algorithm for Finding A
ll frequent
Patterns in Large Datasets.

ICDM2001: 673

W.W. Chu, K. Chiang, C.C. Hsu, H. Yau,
An Error
based Conceptual Clustering Method for
Providing Approximate Query Answers.

Communications of the
, 39(13), December 1996.

W.W. Chu, Q. Chen, A. H
Query Answering via Cooperative Data Inference.

Journal of
Intelligent Information System 3, 57
87, 1994.

J. Han, J. Pei, Y. Yin,
Mining Frequent Patterns without Candidate Generation
Intl. Conference on Management of Data.

C.M. Ho,
P.H. Huang, J. Lew, J.D. Mai, V. Lee, Y.C. Tai,
Intelligent System Capable of Sensing
, Keynote Address, 4

Intl. Conference on Intelligent Materials, Society of Non
Traditional Technology. Tokyo, Japan, October 1998.

C.M. Ho, P.H. Huan
g, J.M. Yang, G.B. Lee, Y.C. Tai,
Active Flow Control by MicroSystems,
FLOWCON, Intl. Union of Theoretical and Applied Mechanics (IUTAM) Symposium on Mechanics
of Passive and Active Flow Control, Gottingen, Germany, Sept.1998. pp18

T. Tsao, F. Jiang, R
.A. Miller, Y.C. Tai, B. Gupta, R. Goodman, S. Tung, C.M. Ho,
An Integrated
MEMS System for Turbulent Boundary Layer Control.
Technical Digest, 1997 Intl. Conf. On Solid
State Sensors and Actuators (Transducers’97), Chicago, IL, Vol.1, pp.315
318, June 16
19 (1997).

C. Liu, J. Huang, A. Zhu, F. Jiang, S. Tung, Y.C. Tai, C.M. Ho,
A Micromachined Flow Shear Stress
Sensor Based on Thermal Transfer Principles
, IEEE/ASME J. of Microelectromechanical Systems
(J.MEMS), 1999.

Lee, G.B., Chiang S., Tai, Y.C, Tsao, T
., Ho, C.M.,
Robust Vortex Control of a Delta Wing Using
Distributed MEMS Actuators.
Journal of Aircraft (2000).

Huang, A., Ho, C.M., Jiang, F., Tai, Y.C.,
MEMS Transducers for Aerodynamics

A Paradym Shift.

Aerospace Science meeting & Exhibit, AIAA
0249. Reno, NV.

Area Background

Data mining, dynamic control of macro
scale machine, distributed sensor

Area References

Z. Liu, W.W. Chu, A. Huang, C. Folk, C.M. Ho,
Mining Sequence Patterns from Wind Tunnel
Experimental Data for Flight Control.
D 2001: 270

C.M. Ho, Y.C. Tai,
Systems (MEMS) and Fluid Flows Annual Review of
Fluid Mechanics

1998 30:579

Potential Related Projects

There are several temporal spatial data mining projects (e.g. Christos Faloutsos, Ma
tthew O. Ward, Vassilis
J. Tsotras) in the IDM. They all can be leveraged on each other’s research.