With the rapid development of social economy,the market and enterprises have higher and higher requirements for application-oriented talents,and secondary vocational schools,as an important position for cultivating vocational and technical talents,should actively adapt to the requirements of the times and change the way of talent training.And with the development of the times,the disadvantages of the traditional teaching mode of secondary vocational schools of"emphasizing theory over practice"have gradually emerged,which has been unable to meet the personalized learning needs of students,and can not adapt to the requirements of the development of the times for talents,so teaching reform is imperative.In this context,this paper will focus on the secondary vocational CNC machining major.Through the what,why,and how to do the line of thought,starting from the concept of"integration of theory and practice"teaching mode,the study goes deep into the necessity of application in CNC machining major,and then explores relevant teaching strategies,aiming to realize the efficient application of the"integration of theory and practice"teaching mode in CNC machining,so as to improve the teaching quality and teaching efficiency,further enhance the professional ability and professionalism of students,and achieve comprehensive development.
Lidaris an important sensing component of the intelligent connected vehicle. Its performance is critical to the safety and reliability of automated driving system. Angle precision and accuracy are key performance indexes of lidar. Accurate testing is of great significance for improving the performance and safety of the automated driving system. This paper proposes three testing methods for the angle precision and accuracy of lidar, namely plate method, pyramid method and average method. By selecting key indicators, analyzing errors, and comparing advantages and disadvantages of these three methods, recommended testing methods for lidar with different scanning methods are provided, providing reference for the testing of lidar and its application in vehicles.
With the rapid development of machine-learning technology,more and more insurers are applying machine-learning methods to improve their car insurance pricing strategies.Measuring car insurance pricing factors is of great importance to insurers and car owners,as it can reveal the extent to which different factors affect insurance premiums and help develop more accurate and personalized insurance strategies.This study aims to compare the performance of different machine learning methods in measuring the importance of car insurance pricing factors,focusing on standard methods such as generalized linear models(GLM),random forests,and XGBoost,and to conduct an empirical study based on two real car insurance datasets.Through experiments and data analysis,we find consistency and variability in the essential measures of car insurance pricing factors across different algorithmic models.Some factors have consistent importance measures across the models,such as the reward and penalty coefficients and the manufacturer's guide price.However,there were also instances where some factors were inconsistent across models,which may be due to differences in model algorithms and data characteristics.These measures provide an essential reference for insurers and guide further improvements to car insurance pricing models and methods.
Proton Exchange Membrane (PEM) fuel cell is one of the most popular fuel cells because of its higher efficiency among the other fuel cells. Because of the expensive materials in designing of this type of fuel cell, it should be first design and simulated in the best and optimum way to reduce the construction costs as much as possible. In the present study, a new model identification is proposed for optimal parameters identification of the PEM fuel cells. The major idea in this study is to provide a new optimal methodology to parameters estimation of the unknown variables in the PEM fuel cell model so that the absolute error (IAE) between the estimated data based on the proposed model and the real data has been minimized. The proposed method uses a new improved design of Archimedes Optimization Algorithm (IAOA) to this purpose. The designed model is then implemented on two practical case studies and the results are compared with some well-known methods. Final results shows that the proposed method with 0.10 and 0.14 error values for Nexa and NedStack PS6 models, respectively, provides the best solution among the other comparative methods.
Advanced Materials Science and Technology is a peer-reviewed open access journal published semi-annual online by Omniscient Pte. Ltd. The journal covers the properties, applications and synthesis of new materials related to energy, environment, physics, chemistry, engineering, biology and medicine, including ceramics, polymers, biological, medical and composite materials and so on.
