Search

Based on the theoretical approaches to interlanguage and discourse cohesiveness, this study examines international students' growth in discourse awareness and discourse competence in the teaching of elementary Chinese reading and writing, and explores a set of assessment methods for teaching discourse. The learners' knowledge and production of Chinese conjunctive words were assessed with multiple test methods, including the sentence sequencing test for testing learners' cognitive understanding of discourse cohesiveness, the elicitation tasks for assessing learners' production of conjunctive words, the tests of picture-based sentence writing and essay writing for evaluating learners' discourse awareness of the cohesiveness in form and meaning. 13 students were recruited by us to participate in these tests. A comprehensive discussion of the data supports that appropriate reading and writing instruction can develop learners' discourse competence and enhance their discourse awareness to a certain extent.

This study investigates the capacity of elementary-level Chinese second language learners to memorize serial verb sentences based on the results of a questionnaire. Our findings show that adult second language learners employ the concept of temporal sequence in language learning, consciously or unconsciously, whether or not the chronological principle has been formally taught. Therefore, we propose that specific cognitive principles, such as the chronological principle, should be carefully applied or formally taught in language training to maximize teaching and learning effectiveness.

Due to its own structural characteristics, a harmonic drive system has a wide range of nonlinear factors, such as flexible deformation, friction and external uncertain interference. Most of the traditional controllers simplify the system to a certain extent, or do not consider the nonlinear external disturbance, resulting in that the performance of the designed controller cannot achieve the desired results. In order to improve the accuracy of the system, the dynamic model of the harmonic drive system is established considering the nonlinear stiffness and nonlinear friction of the system. Based on the test data, the parameters of the model are identified by the least square method. Radial basis function （RBF） neural network is used to approximate the nonlinear friction and external uncertain disturbance torque of the system on-line, and an adaptive inversion controller based on RBF neural network is proposed. Using Lyapunov stability theory, the convergence of the closed-loop system is proved. The simulation results show that, compared with the ordinary Back-stepping control, the proposed RBF neural network adaptive inversion control can effectively approach the system nonlinear friction and external unknown disturbance after being subjected to external unknown disturbance, and its peak value of tracking error can be quickly stabilized to 0.000 82 rad. The Back-stepping control is sensitive to external unknown interference, and the peak value of its tracking error increases to about 0.012 3 rad. The proposed RBF neural network adaptive inversion control can suppress the influence of parameter dynamic changes and external disturbances on the transmission accuracy of the system, and improve the transmission accuracy of the system.