Project Award Number: #0097438
First Name:† Wesley
Middle Initial: W.
Last Name: Chu
Department: Computer Science Department
Address line 1: UCLA Computer Science Department
First Name: Chih-Ming
Last Name: Ho
Department: Mechanical and Aerospace Engineering Department
Daniel H. Whang: Civil & Environmental Engineering Department, UCLA firstname.lastname@example.org
MEMS sensors and actuators†††† Dynamic control†††† Delta wing flight control††††
Temporal and spatial data mining
This is an interdisciplinary project through the departments of Computer Science and Mechanical Engineering. The aerodynamic group has been focusing on data protocols, sub-baseline data, and the design of the proposed experimental setup. During this period, we have completed the automatic movement mechanisms simulating the transient conditions for the delta wing aircraft in the wind tunnel. This consists of motion control hardware and actuation mechanisms that will be mounted inside the wind tunnel.
In regards to data mining, we have focused on developing algorithms for finding maximum frequent itemsets (MFI) to generate targeted (attributes) association rules [ZCYK03], developing a framework for the distributive aggregation of multidimensional objects [TC03] and the mining of temporal spatial data sequences.
In the mining of temporal spatial data sequences, the input/output variation may follow certain patterns. We plan to extract a set of sequence patterns, which are formed by a series of input/output pairs over a range of input values. Based on similarities among the sequence patterns, we can cluster them into groups and organize them into a cluster hierarchy via an inter-cluster error measure. Each node in the cluster summarizes the input/output relationship of that group.
Mining temporal spatial patterns generated from multiple sources (e.g. multiple sensors) is different from the mining of a single time sequence. We need to capture the pattern generated from all the sources from both temporal and spatial conditions. For the spatial dimension, we use dynamic time warping (DTW) to match similar patterns in the multiple time sequences. DTW [PCY00] may be used to stretch and shrink the time sequences to increase their matching probability. For the temporal dimension, we use moving windows on the temporal sequences to find patterns contained in the windows that match with the patterns among the multiple time sequences [Oat02]. We plan to use the aforementioned algorithm for mining the dynamic temporal spatial data sequences from the wind tunnel experiments in the coming year.
[LCHFH 01] Z. Liu, Wesley. W. Chu, A. Huang, C. Folk, C.M. Ho., "Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control", PAKDD, (2001), p. 270.
[HFHLC 01] A. Huang, C. Folk, C.-M. Ho, Z. Liu, Wesley W. Chu, Y. Xu, Y.-C. Tai., "Gryphon M3 system: integration of MEMS for flight control.", In MEMS Components and Applications for Industry, Automobiles, Aerospace, and Communication, Proceedings of SPIE (The Int'l Society for Optical Engineering), vol. 4559, (2001), p. 85.
[ZCJC 02] Q. Zou, Wesley. W. Chu, D.Johnson, and H.Chiu, "A pattern Decomposition Algorithm for Finding all Frequent patterns", Journal of Knowledge and Information System, vol. 1, (2002), p. 101.
[ZCL 02] Q.
[ZCCK 03] Q.
[TC 03] Meng-Feng Tsai and Wesley W. Chu, "A Multidimensional Aggregation Object (MAO) Framework for Computing Distributive Aggregations", 5th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2003,September 3-5, 2003 Prague, Czeck republic, (2003), Accepted.
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 in the mining of medical clinical data to determine the success of certain surgical procedures based on the patientís demographics, and pre and post surgery conditions. It can also be used for mining in Civil Engineering to discover the relationship of soil characteristics with settlement for certain seismic input data.
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 behaviors, and are extending the mining algorithms to handle massive amounts of multivariate training data. We plan to develop a methodology to generate temporal and spatial rules, as well as a strategy to automatically select actuation rules for real time system control.
Data mining, dynamic control of macro-scale machine, distributed sensors
[LCHFH 01] Z. Liu, Wesley W. Chu, A. Huang, C. Folk, C.M. Ho, Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control. PAKDD 2001: 270-281.
[HT 98] C.M. Ho, Y.C. Tai, Micro-Electro-Mechanical-Systems (MEMS) and Fluid Flows Annual Review of Fluid Mechanics. 1998 30:579-612.
[PCY 00] S. Park, Wesley W. Chu, J. Yoon, C. Hsu. Efficient Search for Similar Subsequences of Different Lengths in Sequence Database, 16th International Conference on Data Engineering, San Diego 2000.
[Oat 02] Tim Oates. PERUSE: an unsupervised algorithm for finding recurring patterns in time series, IEEE International Conference on Data Mining (ICDM'02), Maebashi City, Japan 2002.
There are several temporal spatial data mining projects (e.g. Christos Faloutsos, Lin Liu and Christopher Lee) in the IDM. They can all be leveraged on each otherís research.