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

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

Wesley W. Chu, P.I.

Computer Science Department

University of California, Los Angeles

Los Angeles, Ca 90095

Phone: (310)825-2047

Fax: (310)825-7578

Email:

Chih-Ming Ho, Co-P.I.

Department of Mechanical and Aerospace Engineering

University of California, Los Angeles

Los Angeles, Ca 90095

Phone: (310)825-9993

Fax: (310)206-2302

Email:

WWW Page

Keywords

MEMS sensors and actuatorsDynamic controlDelta wing flight control

Temporal and spatial data mining

Project Award Information

• Award Number: IIS-0097538
• Duration: 08/31/2001 – 08/31/2004
• Title: Control of Systems with MEMS 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 derive 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 non-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 variables 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 data 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, and 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 under dynamic movement.

GPRA Outcome Goals

1. 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 time 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 fabricated-based systems.

1. 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 techniques 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 environmental, 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: 270-281.
• G. Giuffrida, W.W. Chu, D.M. Hanssens, NOAH: An Algorithm for Mining Classification Rules from Datasets with Large Attribute Space. In Proceedings of 12th 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 All frequent Patterns in Large Datasets.ICDM2001: 673-674.
• W.W. Chu, K. Chiang, C.C. Hsu, H. Yau, An Error-based Conceptual Clustering Method for Providing Approximate Query Answers. Communications of the ACM, 39(13), December 1996.
• W.W. Chu, Q. Chen, A. Hwang, 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. 2000 ACM SIGMOD 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-Computing-Actuating, Keynote Address, 4th Intl. Conference on Intelligent Materials, Society of Non-Traditional Technology. Tokyo, Japan, October 1998.
• C.M. Ho, P.H. Huang, 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. pp18-19.
• 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. 38th Aerospace Science meeting & Exhibit, AIAA 00-0249. 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. PAKDD 2001: 270-281.
• C.M. Ho, Y.C. Tai, Micro-Electro-Mechanical-Systems (MEMS) and Fluid Flows Annual Review of Fluid Mechanics. 1998 30:579-612

Potential Related Projects

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