Artificial Intelligence Education in Primary and Secondary Schools from the Perspective of Thinking Quality

With the vigorous development of social economy in China,various advanced technologies and equipment have emerged,among which artificial intelligence(AI)has rapidly developed and achieved remarkable results when applied to many fields.Therefore,leaders and teachers in primary and secondary schools should pay more attention to AI education and explore effective measures to optimize the effectiveness of this education.Among them,carrying out artificial intelligence education and teaching from the perspective of thinking quality,with an aim to improve students’technical ability and effectively cultivate their thinking skills,may improve students’learning efficiency and teachers’teaching efficiency.How to carry out AI education from the perspective of thinking quality is an important issue that teachers need to address urgently.Through in-depth research,we focus on this issue,in hope to benefit primary and secondary school teachers.

Research on the Practical Path of Collaborative Innovation Talent Training in Supply-Side Applied Undergraduate Colleges

The cultivation of supply-side collaborative innovation talents mainly focuses on four elements:labor,capital,technology,and system.The improvement of these four elements is essentially a long-term process,which determines the potential growth rate in the medium and long term.In this paper,we analyzed the current situation,put forward two major problems,the low degree of specialization and insufficient participation,and focus on the research on the practical path of collaborative innovation talent training in applied undergraduate colleges from the perspective of the system.

A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed.As such,the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system.This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations.The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties.Thus,the controller can reduce the need for manual adjustments.The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance.A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics.Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively.The performance of the controller algorithm is verified by both simulation,and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing.Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers,which verifies the effectiveness of this approach in improving the long-term uniformity of slivers.