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While Markov jump systems (MJSs) are more appropriate than LTI systems in terms of modeling abruptly changing dynamics, MJSs (and other switched systems) may suffer from the model complexity brought by the potentially sheer number of switching modes. Much of the existing work on reducing switched systems focuses on the state space where techniques such as discretization and dimension reduction are performed, yet reducing mode complexity receives few attention. In this work, inspired by clustering techniques from unsupervised learning, we propose a reduction method for MJS such that a mode-reduced MJS can be constructed with guaranteed approximation performance. Furthermore, we show how this reduced MJS can be used in designing controllers for the original MJS to reduce the computation cost while maintaining guaranteed suboptimality.
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Evaluating the Scalability of Non-Preferred Hand Mode Switching in Augmented Reality
Mode switching allows applications to support a wide range of operations (e.g. selection, manipulation, and navigation) using a limited input space. While the performance of different mode switching techniques has been extensively examined for pen- and touch-based interfaces, investigating mode switching in augmented reality (AR) is still relatively new. Prior work found that using non-preferred hand is an efficient mode switching technique in AR. However, it is unclear how the technique performs when increasing the number of modes, which is more indicative of real-world applications. Therefore, we examined the scalability of non-preferred hand mode switching in AR with two, four, six, and eight modes. We found that as the number of modes increase, performance plateaus after the four-mode condition. We also found that counting gestures have varying effects on mode switching performance in AR. Our findings suggest that modeling mode switching performance in AR is more complex than simply counting the number of available modes. Our work lays a foundation for understanding the costs associated with scaling interaction techniques in AR.
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- Award ID(s):
- 1750840
- PAR ID:
- 10175137
- Date Published:
- Journal Name:
- International Conference on Advanced Visual Interfaces
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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While Markov jump systems (MJSs) are more appropriate than LTI systems in terms of modeling abruptly changing dynamics, MJSs (and other switched systems) may suffer from the model complexity brought by the potentially sheer number of switching modes. Much of the existing work on reducing switched systems focuses on the state space where techniques such as discretization and dimension reduction are performed, yet reducing mode complexity receives few attention. In this work, inspired by clustering techniques from unsupervised learning, we propose a reduction method for MJS such that a mode-reduced MJS can be constructed with guaranteed approximation performance. Furthermore, we show how this reduced MJS can be used in designing controllers for the original MJS to reduce the computation cost while maintaining guaranteed suboptimality.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3399715.3399850
