Hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information

Hagiwara, Yoshinobu; Taguchi, Keishiro; Ishibushi, Satoshi; Taniguchi, Akira; Taniguchi, Tadahiro

doi:10.1080/01691864.2021.2004224

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The Bayesian learning [14][15][16][17] with Gibbs sampler [18][19][20][21][22] is used to estimate ω.…”

Section: Bayesian Learningmentioning

confidence: 99%

Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

Zhang

2021

JERR

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The surface roughness has an important influence on the fatigue life of the structures. The fatigue life reduces due to the stress concentration caused by surface roughness. The stress concentration governs the fatigue crack initiation and propagation. The accurate acquisition of the stress concentration factor of rough surfaces is a key issue in determining fatigue life. Nevertheless, semi-empirical models may be biased for various machining processes. Besides, finite element method simulations cannot give explicit expression of the stress concentration factor. Bayesian learning can construct accurate prediction models which offering a number of additional advantages. In this paper, based on several data pairs constructed by finite element method, the correlation expression between the stress concentration factor and statistical roughness parameters of surfaces is established quickly through Bayesian learning. Compared with some other semi-empirical models, the accuracy and stability of the proposed method are certified. This paper provides a simple and effi-cient approach to determine the stress concentration factor for rough surfaces under different processing conditions.

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“…The Bayesian learning [14][15][16][17] with Gibbs sampler [18][19][20][21][22] is used to estimate ω.…”

Section: Bayesian Learningmentioning

confidence: 99%

Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

Zhang

2021

JERR

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Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society

et al. 2021

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The installation of remotely-operated service robots in the environments of our daily life (including offices, homes, and hospitals) can improve work-from-home policies and enhance the quality of the so-called new normal. However, it is evident that remotely-operated robots must have partial autonomy and the capability to learn and use local semiotic knowledge. In this paper, we argue that the development of semiotically adaptive cognitive systems is key to the installation of service robotics technologies in our service environments. To achieve this goal, we describe three challenges: the learning of local knowledge, the acceleration of onsite and online learning, and the augmentation of human-robot interactions.

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Active exploration based on information gain by particle filter for efficient spatial concept formation

et al. 2023

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Hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information

Cited by 4 publications

References 27 publications

Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

Surface Roughness Characterization for Stress Concentration Factor Predictions: A Bayesian Learning Approach

Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society

Active exploration based on information gain by particle filter for efficient spatial concept formation

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