Writing is a distinctly important language output skill. Students can organize and process the learned language knowledge through writing to realize the re-creation of knowledge. Writing is the best performance of students’ comprehensive use of language ability and it plays an essential role in language instruction. The Outcome-Based Education originated in the United States in the 20th century. It is represented by Spicer, based on the four basic principles of clear objectives, expanded opportunities, high expectations and reverse design. In order to solve the unfavorable tendency of the separation of learning and use in instruction in China, this study attempts to apply the instruction model based on OBE to a unit teaching referring to an eight-week experiment for eight teaching hours. The research subjects are senior students of two classes in Grade 2 of a middle school in Henan Province. After collecting data, the qualitative and quantitative analysis have been carried out with SPSS 17.0. Meanwhile, students and teachers are surveyed and interviewed before and after the experiment to show their psychological feedback and actual changes during the teaching experiment with OBE. Through research and experiments, the following findings are obtained: First, in view of current conditions of English writing instruction in Chinese high schools, the English writing instruction model with OBE can help students improve their abilities of language production. Second, compared with the traditional English writing teaching mode, it can stimulate students’ interest in learning and applying language, and reduce their negative emotions such as anxiety, passiveness and helplessness.
To advance the machinery performances, obviously the intelligent decision-making technologies will bring into play the significant power. Aimed at knowledge acquisition bottleneck, in the paper to take Rough Set Theory (RST) as a knowledge discovery tool and resolve the puzzle was explored. Both the tool's principle and its application way in machinery engineering were investigated. A novel knowledge operation model is brought forward. It shows that the knowledge discovery based on RST is a system engineering project. But to obtain the original knowledge resource should be the essential foundation. The special request for RST tool is that the data must also be concise. So, to protect the expected knowledge with scientific data mode has become the significant task in knowledge discovery researches at present. But the case of simulative faults experiments on a rotating machinery model indicates that the task can be accomplished by arduous efforts.
In order to solve the optimization problems of convergence characteristics of a class of single-input single-output (SISO) discrete linear time-varying systems (LTI) with time-iteration-varying disturbances, an optimal control gain design method of PID type iterative learning control (ILC) algorithm with forgetting factor was presented. The necessary and sufficient condition for the ILC system convergence was obtained based on iterative matrix theory. The convergence of the learning algorithm was proved based on operator theory. According to optimization theory and Toeplitz matrix characteristics, the monotonic convergence condition of the system was established. The accurate solution of the optimal control gain and the relationship equation between the forgetting factor and the optimal control gains were obtained according to the optimal theory which ensures the fastest system convergence speed, thereby reaching the end of the system convergence improvement. The convergence condition is weaker than the known results. The proposed method overcomes the shortcomings of traditional optimal control gain in ILC algorithm with forgetting factor, effectively accelerates the system convergence speed, suppresses the system output track error fluctuation, and provides a better solution for LTI system with time-iteration-varying disturbances. Simulation verifies the effectiveness of the control algorithm.
Iterative learning control with forgetting factor (ILCFF) is widely used in control engineering. However, choosing the optimal parameters of ILCFF to improve system-output characteristics has been a challenging issue for controller designers. This paper proposes an iterative learning control (ILC) algorithm that involves a variable forgetting factor based on optimal gains for a class of discrete linear time-invariant systems with aperiodic disturbances. The convergence of the algorithm is analyzed, and the necessary and sufficient condition for its convergence is derived in terms of proportional–integral–derivative coefficients. A design method based on optimal gains is established to determine the algorithm coefficients and to accelerate system convergence. Furthermore, the influence of the forgetting factor on both the system-output error and the scope of the proposed algorithm is analyzed. Additionally, the most suitable system type for the application of the forgetting factor is determined. The effectiveness of the algorithm is verified by performing a theoretical analysis and a case-based simulation. The proposed iteration-varying optimal forgetting-factor-based ILC algorithm undergoes fast convergence with a small system-output error. The findings disrupt the conventional view that the use of the forgetting factor increases system-output error. In fact, in a system with small trajectory and increased disturbances, the error induced by the forgetting factor may be smaller than that of the traditional optimal ILC algorithm.
The rise of geothermal water level can be observed in the discharge section in a convective hydrothermal system. It is hard to explain in terms of gravity-driven groundwater systems, suggesting the existence of an additional driving force. This geothermal driving force is closely related to the changes in density, salinity, and viscosity caused by the increase in groundwater temperature. It is significant to quantitatively characterize the geothermal driving force for the interpretation of geothermal water level and flow. A case study is carried out in a convective geothermal system in Huangshadong, South China. The recharged groundwater migrated to a position with an average depth of 2.09 km and was heated to 100-130 ℃. The geothermal driving force produced by the increase in temperature and in salinity is +125.33 m and −2.62 m, respectively. The actual pressure head produced by the geothermal driving force is +122.71 m. The migration speed of geothermal water under the action of geothermal driving force from the heating position to the discharge area increased from 2.14×10 -3 m/d to 3.09×10 -3 m/d. The results indicate the presence of a geothermal driving force accelerates the groundwater migration in the discharge section of convective hydrothermal system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.