针对现阶段普通高校尤其是新升本高校计算机专业生,在基础理论课堂的听课效果不佳,在实践操作课堂的效率不高的现象,文章提出了基于成果导向型的计算机模块化项目教学模式,结合管理信息系统开发与应用课程(visual studio C#)的实际教学...针对现阶段普通高校尤其是新升本高校计算机专业生,在基础理论课堂的听课效果不佳,在实践操作课堂的效率不高的现象,文章提出了基于成果导向型的计算机模块化项目教学模式,结合管理信息系统开发与应用课程(visual studio C#)的实际教学经验,对项目式开发教学中的教学方式、教学模式、实践教学环节、考核形式等进行探讨,提高学生专业理论知识能力、模块化功能项目开发实践动手和管理项目的能力。展开更多
A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
文摘针对现阶段普通高校尤其是新升本高校计算机专业生,在基础理论课堂的听课效果不佳,在实践操作课堂的效率不高的现象,文章提出了基于成果导向型的计算机模块化项目教学模式,结合管理信息系统开发与应用课程(visual studio C#)的实际教学经验,对项目式开发教学中的教学方式、教学模式、实践教学环节、考核形式等进行探讨,提高学生专业理论知识能力、模块化功能项目开发实践动手和管理项目的能力。
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
