Knot removal for parametric B-spline curves and surfaces
Computer Aided Geometric Design
A virtual metrology system for semiconductor manufacturing
Expert Systems with Applications: An International Journal
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
Knot line refinement algorithms for tensor product B-spline surfaces
Computer Aided Geometric Design
Segmenting sensor data for activity monitoring in smart environments
Personal and Ubiquitous Computing
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In semiconductor manufacturing processes, sensor data are segmented and summarized in order to reduce storage space. This is conventionally done by segmenting the data based on predefined chamber step information and calculating statistics within the segments. However, segmentation via chamber steps often do not coincide with actual change points in data, which results in suboptimal summarization. This paper proposes a novel framework using abnormal difference and free knot spline with knot removal, to detect actual data change points and summarize on them. Preliminary experiments demonstrate that the proposed algorithm handles arbitrarily shaped data in a robust fashion and shows better performance than chamber step based segmentation and summarization. An evaluation metric based on linearity and parsimony is also proposed.