Simultaneous optimization of a structure and control for mechanical systems using the Nash bargaining game

Nakamoto, Kunihiro; Konishi, Yasuo; Kondo, Katsuya; Ishigaki, Hiroyuki

doi:10.1109/idc.1999.754171

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…This method has been widely used in many previous studies on behavior recognition. [30][31][32][33][34][35][36] Some researchers explored the impact of window size on activity recognition accuracy. Banos et al [37] concluded that the 1-2 s size would be a good trade-off between calculation speed and classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

AI‐Enabled Micro Motion Sensors for Revealing the Random Daily Activities of Caged Mice

Liu

Chen

Chan³

et al. 2023

Advanced Intelligent Systems

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More than 120 million mice and rats are used yearly for scientific purposes. While tracking their motion behaviors has been an essential issue for the past decade, present techniques, such as video‐tracking and IMU‐tracking have considerable problems, including requiring a complex setup or relatively large IMU modules that cause stress to the animals. Here, we introduce a wireless IoT motion sensor (i.e., weighing only 2 g) that can be attached and carried by mice to collect motion data continuously for several days. We also introduce a combined segmentation method and an imbalanced learning process that are critical for enabling the recognition of common but random mouse behaviors (i.e., resting, walking, rearing, digging, eating, grooming, drinking water, and scratching) in cages with a macro‐recall of 94.55%. An interactive preprint version of the article can be found at: https://doi.org/10.22541/au.166005321.10787501/v1.

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