Bulletin of the American Physical Society
54th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Volume 68, Number 7
Monday–Friday, June 5–9, 2023; Spokane, Washington
Session F02: Virtual Poster Session (4:00pm-6:00pm, PT)
4:00 PM,
Tuesday, June 6, 2023
Room: Virtual Platform
Chair: Jianming Wen, Kennesaw State University
Abstract: F02.00019 : CNN-LSTM neural network for high-fidelity, short-time state measurement and calculation of its cutoff detection time based on Jasen-Shannon divergence*
Presenter:
Junho Jeong
(Seoul National University)
Authors:
Junho Jeong
(Seoul National University)
Taehyun Kim
(Seoul National University)
Dongil D Cho
(Seoul National University)
In this paper, we propose a combination of convolutional neural network and long short-term memory (CNN-LSTM) model to improve the measurement fidelity. The model is designed to combine the strengths of each model. That is, CNN is specialized in capturing local features, and LSTM is specialized in processing sequential time information. The result shows that the combined model outperforms both CNN and LSTM models and it is robust to the size of time information.
Moreover, we show that the amount of available information from the histograms of each state can be calculated based on Jasen-Shannon divergence. This calculated result can be exploited to determine the offset detection time and discard the redundant information, which in turn can save computational resources.
*This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1901-09.
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