AMM: Adaptive Multilinear Meshes

We present Adaptive Multilinear Meshes (AMM), a new framework that significantly reduces the memory footprint compared to existing data structures. AMM uses a hierarchy of cuboidal cells to create continuous, piecewise multilinear representation of uniformly sampled data. Furthermore, AMM can selectively relax or enforce constraints on conformity, continuity, and coverage, creating a highly adaptive and flexible representation to support a wide range of use cases. AMM supports incremental updates in both spatial resolution and numerical precision establishing the first practical data structure that can seamlessly explore the tradeoff between resolution and precision. We use tensor products of linear B-spline wavelets to create an adaptive representation and illustrate the advantages of our framework. AMM provides a simple interface for evaluating the function defined on the adaptive mesh, efficiently traversing the mesh, and manipulating the mesh, including incremental, partial updates. Our framework is easy to adopt for standard visualization and analysis tasks. As an example, we provide a VTK interface, through efficient on-demand conversion, which can be used directly by corresponding tools, such as VisIt, disseminating the advantages of faster processing and a smaller memory footprint to a wider audience. We demonstrate the advantages of our approach for simplifying scalar-valued data for commonly used visualization and analysis tasks using incremental construction, according to mixed resolution and precision data streams

Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment

International audienc

Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. For this purpose, this dissertation develops methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding. The first part of this dissertation develops a monitoring algorithm that targets near-zero misdetection by integrating univariate control charts and a multivariate control chart using the Mahalanobis distance. The proposed algorithm is capable of monitoring non-normal multivariate observations with adjustable control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. The proposed algorithm proves to be effective in achieving near-zero misdetection in process monitoring in ultrasonic welding processes. The second part of the dissertation develops a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The proposed within-cycle monitoring technique integrates real-time monitoring and within-cycle control opportunity for defect prevention. The optimal decision point for achieving the most benefit in defect prevention is determined through the formulation of an optimization problem. The effectiveness of the proposed method is validated and demonstrated by simulations and case studies. The third part of this dissertation develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The research presented in this dissertation can be applied to general discrete cyclic manufacturing processes that have online sensing and control capabilities. The results of this dissertation are also applicable or expandable to mission-critical applications when improving product quality and preventing defects are of high interests.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113405/1/graceguo_1.pd