Approximating stochastic loads using the EM-Algorithm

Ossevorth, Fabian; Schegner, Peter

doi:10.1016/j.ifacsc.2021.100175

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In order to reduce the computation complexity, the parameter estimation is divided into two steps. The first step is to estimate the parameters for training points using the EM algorithm [24,25]. The second step is to predict the parameters for test points using the predictive equation of Gaussian processes with noise-free observations.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments.

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Section: Research Gap and Motivationmentioning

confidence: 99%

HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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A study on the influence of the spread of Yangming Studies in Japan on the psychology of the Japanese people based on big data analysis

Liu¹

2023

Applied Mathematics and Nonlinear Sciences

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The analysis of the psychological impact of the spread of Yangming studies in Japan on the Japanese people is to enable Yangming studies to be better developed in Japan. Based on big data analysis technology, this paper constructs a hybrid data analysis model using the EM algorithm and proposes performance evaluation indexes for the model. Under the EM data analysis model constructed in this paper, the example indicators of the Japanese people’s psychological impact in disseminating Yangming studies by big data analysis are explored, i.e., the psychological acceptability of the dissemination method and the psychological and moral construction impact. Regarding the dissemination method, the Japanese people are more receptive to disseminating Yangming studies in Japan through “learning rules”, with an average percentage of 39.37%. Regarding psychological and moral construction, 90.22% of the Japanese people believe that disseminating Yangming studies can promote self-improvement of value standards and correct self-examination. Based on the big data analysis, we can effectively see from the data the impact of Yangming studies on the audience in the process of dissemination, and improve the scope of Yangming studies dissemination according to the data feedback, so that more people can recognize the idea of unity of knowledge and action.

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Approximating stochastic loads using the EM-Algorithm

Cited by 2 publications

References 8 publications

HMM-Based Dynamic Mapping with Gaussian Random Fields

HMM-Based Dynamic Mapping with Gaussian Random Fields

A study on the influence of the spread of Yangming Studies in Japan on the psychology of the Japanese people based on big data analysis

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