The Patterns of Invitational Discourse in Japanese by Chinese Learners: Two Situations with Different Degrees of Burden

The differences in the patterns of Japanese invitations were analyzed in the conversations among Japanese native speakers only or among Japanese native speakers and Chinese learners of Japanese language. The subjects were asked to work in pairs to perform role-playing with different degrees of burden degrees and the resulting data were analyzed by following the use of spoken paragraphs (wadan) including the opening, head, and closing sections. The results showed clear differences in each section between Japanese native speakers and Chinese learners. Firstly, in the opening section, there were fewer Chinese learners than native speakers who used the preceding stage before entering an invitation. Secondly, in the head section, the Chinese learners seemed to use more combinations of spoken paragraphs in situations with a high degree of burden. Thirdly, in the closing section, Chinese learners were found to provide information about invitation after accepting the invitation in situations with a low degree of burden. However, native Japanese speakers tended to provide invitation information at an early stage before the invitation was accepted

中国人日本語学習者の「誘い」に関する語用論的研究 : 言語使用と産出過程に注目して

内容の要約広島大学(Hiroshima University)博士(教育学)Doctor of Philosophy in Educationdoctora

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.Comment: Accepted in AAAI 202

Research on optimal three-vector model predictive current control of 2 permanent magnet synchronous motor

Aiming at the two-vector model predictive current control(TV-MPCC)strategy of permanent magnet synchronous motor (PMSM), in a sampling period, can merely change the amplitude magnitude by adjusting the action time of the zero vector, resulting in the synthesized voltage vector direction can only be fixed in the direction of the six basic voltage vectors and the current fluctuations generated by the problem. This paper proposes an optimized three-vector model predictive current control (OTV-MPCC) method, which first finds the desired voltage vector and then finds the position angle of the desired vector through the inverse derivation of the formula and determines the sector in which it is located. The two fundamental vectors are selected and the zero vector at the boundary of the sector as the three voltage vectors requires for the model predictive control. Moreover, the time of each vector action is calculated by using the dead-beat control method, and the zero vector is selected by the switching frequency and switching minimum principle, which makes the algorithm computation significantly reduced. The simulation experimental results show that the proposed optimized three-vector model based on the predictive current control strategy can effectively decline the straight-axis and cross-axis current pulsations and enhance the stability of the system

Efficiently Solving High-Order and Nonlinear ODEs with Rational Fraction Polynomial: the Ratio Net

Recent advances in solving ordinary differential equations (ODEs) with neural networks have been remarkable. Neural networks excel at serving as trial functions and approximating solutions within functional spaces, aided by gradient backpropagation algorithms. However, challenges remain in solving complex ODEs, including high-order and nonlinear cases, emphasizing the need for improved efficiency and effectiveness. Traditional methods have typically relied on established knowledge integration to improve problem-solving efficiency. In contrast, this study takes a different approach by introducing a new neural network architecture for constructing trial functions, known as ratio net. This architecture draws inspiration from rational fraction polynomial approximation functions, specifically the Pade approximant. Through empirical trials, it demonstrated that the proposed method exhibits higher efficiency compared to existing approaches, including polynomial-based and multilayer perceptron (MLP) neural network-based methods. The ratio net holds promise for advancing the efficiency and effectiveness of solving differential equations