Wyniki wyszukiwania - BazTech

1

Efficient learning variable impedance control for industrial robots

Li C., Zhang Z., Xia G., Xie X., Zhu Q.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 2

201--212

EN

Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.

2

A Type Design Method of Large Hydraulic Turbine Based on The Gaussian Mixture Model

Lin L., Zhong S., Xie X.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 9b

153-156

EN

Because of the complex fluid motion in hydraulic turbine and the imperfect design theory, the selection design of Large-scale hydraulic turbine is achieved based on the calculation and analysis on the synthetic characteristic curves, which is subjectivity and low efficiency. To solve this problem, the Gaussian mixture model is used to extract the geometric features from the synthetic characteristic curves so that the retrieval process of the model wheel can be achieved by these geometric features. The search model of the running area from the synthetic characteristic curves is build based on the contour curve similarity transformation method. In the paper, the Monte Carlo method is adopted to obtain the mean values of the synthetic characteristics in the running area so that the evaluation targets can be established by combining the mean values with the hydraulic turbine design experiences. Finally the validity of running area can be evaluated by the evaluation targets. The test results show that the accuracy and efficiency of the selection design of Large-scale hydraulic turbine can be improved by the proposed method.

PL

W artykule przedstawiono problemy projektowania dużych turbin hydraulicznych. Zaproponowano model matematyczny bazujący na mieszanym modelu Gaussa w celu wydobycia parametrów geometrycznych. Zaadaptowano metodę Monte Carlo do określania średnich wartości syntetycznych charakterystyk w obszarze pracy.

3

Gaussian mixture model for time series-based structural damage detection

Słoński M.

Computer Assisted Methods in Engineering and Science

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2012

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Vol. 19, no. 4

331-338

EN

In this paper, a time series-based damage detection algorit hm is proposed using Gaussian mixture model (GMM) and expectation maximization (EM) framework. The vib ration time series from the structure are modelled as the autoregressive (AR) processes. The first AR coefficients are used as a feature vector for novelty detection. To test the efficacy of the damage detec tion algorithm, it has been tested on the pseudo-experimental data obtained from the FEM model of the ASCE benchmark frame structure. Results suggest that the presented approach is able to detect mainly major and moderate damage patterns

4

Fault classification in cylinders using multi-layer perceptrons, support vector machines and Gaussian mixture models

Marwala T., Mahola U., Chakraverty S.

Computer Assisted Mechanics and Engineering Sciences

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2007

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Vol. 14, No. 2

307-316

EN

Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are reduced into low dimension using the principal component analysis and are then used to train the GMM, SVM and MLP. It is observed that the GMM gives 98% classification accuracy, SVM gives 94% classification accuracy while the MLP gives 88% classification accuracy. Furthermore, GMM is found to be more computationally efficient than MLP which is in turn more computationally efficient than SVM.