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Modeling of a Bingham model of a magnetorheological damper considering stochastic uncertainties in their geometric variables

Chen Ke, Yu Xiaopeng, Wang Haishuo, Zheng Hongmei, Zhang Guojun, Wu Rui

Journal of Theoretical and Applied Mechanics

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2021

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Vol. 59 nr 1

53--65

EN

Stochastic uncertainty theory is used to develop a new Bingham model of magnetorheological dampers superior to the existing model. Some input variables are defined as stochastic variables by the stochastic factor method, and the stochastic Bingham model is developed by the algebraic synthesis method. Curves of the damping force obtained by the stochastic Bingham model and the Bingham model in the literature are compared with experimental results, revealing that the curves obtained by the stochastic Bingham model are much closer to the experimental curves. Therefore, we confirm that the stochastic Bingham model is superior to the model from the literature.

2

Finding robust transfer features for unsupervised domain adaptation

Gao Depeng, Wu Rui, Liu Jiafeng, Fan Xiaopeng, Tang Xianglong

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 1

99--112

EN

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.

3

Utilizing relevant RGB-D data to help recognize RGB images in the target domain

Gao Depeng, Liu Jiafeng, Wu Rui, Cheng Dansong, Fan Xiaopeng, Tang Xianglong

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 3

611--621

EN

With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other is that the training and test data are drawn from different distributions as they are captured using different equipment. To jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of the most effective ones.

4

An active exploration method for data efficient reinforcement learning

Zhao Dongfang, Liu Jiafeng, Wu Rui, Cheng Dansong, Tang Xianglong

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 2

351--362

EN

Reinforcement learning (RL) constitutes an effective method of controlling dynamic systems without prior knowledge. One of the most important and difficult problems in RL is the improvement of data efficiency. Probabilistic inference for learning control (PILCO) is a state-of-the-art data-efficient framework that uses a Gaussian process to model dynamic systems. However, it only focuses on optimizing cumulative rewards and does not consider the accuracy of a dynamic model, which is an important factor for controller learning. To further improve the data efficiency of PILCO, we propose its active exploration version (AEPILCO) that utilizes information entropy to describe samples. In the policy evaluation stage, we incorporate an information entropy criterion into long-term sample prediction. Through the informative policy evaluation function, our algorithm obtains informative policy parameters in the policy improvement stage. Using the policy parameters in the actual execution produces an informative sample set; this is helpful in learning an accurate dynamic model. Thus, the AEPILCOalgorithm improves data efficiency by learning an accurate dynamic model by actively selecting informative samples based on the information entropy criterion. We demonstrate the validity and efficiency of the proposed algorithm for several challenging controller problems involving a cart pole, a pendubot, a double pendulum, and a cart double pendulum. The AEPILCO algorithm can learn a controller using fewer trials compared to PILCO. This is verified through theoretical analysis and experimental results.