A method for robotic grasping based on improved Gaussian mixture model

Tao, Yong; Ren, Fan; Chen, You Dong; Wang, Tian Miao; Zou, Yu; Chen, Chao Yong; Jiang, Shan

doi:10.3934/mbe.2020077

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…PGM is a clustering model based on probabilistic graph, similar to the Gaussian Mixture Model (GMM) (Tao et al, 2020). There are m sub-distributions in PGM.…”

Section: Probabilistic Mf (Pmf) and Probabilistic Graph Model (Pgm)mentioning

confidence: 99%

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Qiu,

Cui

2024

Sage Open

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Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.

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“…PGM is a clustering model based on probabilistic graph, similar to the Gaussian Mixture Model (GMM) (Tao et al, 2020). There are m sub-distributions in PGM.…”

Section: Probabilistic Mf (Pmf) and Probabilistic Graph Model (Pgm)mentioning

confidence: 99%

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Qiu,

Cui

2024

Sage Open

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Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

2021

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Based on operation status assessment and self‐optimizing regulation, an optimization strategy for the smelting process of a fused magnesium furnace is proposed to improve the benefits of smelting magnesia. In the online evaluation stage, according to the qualitative information, the data is divided by the fuzzy model recognition method, and then the posterior probability of online data in different Gaussian mixture models is calculated to obtain the evaluation results. When the evaluation results are not optimal, the non‐optimal variables are obtained by calculating the contribution rate, and then the self‐optimizing adjustment is carried out by case search. In order to improve the recovery rate of self‐optimizing regulation, a time series prediction method is used to predict the operation status, and the corresponding operation schemes for different prediction results are proposed. Experimental results show that the proposed method is accurate and effective.

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Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing

Zhang

Huang

et al. 2022

Int J Adv Manuf Technol

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A method for robotic grasping based on improved Gaussian mixture model

Cited by 7 publications

References 12 publications

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing

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