“SDSPR教学法”在程序设计类课程中的应用

本文详细介绍了SDSPR教学方法的五个环节,并总结了SDSPR教学方法的特点,说明了"SDSPR"教学方法在程序设计类课程中的重要作用,对于推行"理论实践一体化"的教学模式和提高高职软件专业学生的编程技能具有重要的意义。     

Java程序设计课程的案例设计

解释了"项目驱动、案例教学、理论实践一体化"教学方法的基本思路,给出了Java程序设计课程的案例设计.     

Delphi常见文件操作

介绍Delphi编程中传统文件操作与面向对象的流文件操作的方法,并举例说明面向对象的流文件操作的优势。     

基于C#的打字测试软件研究与开发

信息时代下,掌握和熟练操作计算机成为社会对现代人的基本要求.文字录入的快慢在一定程度上影响着工作效率.开发一套较完善的打字测试软件来公平公正的考核学生的打字水平成为必要.基于C#语言开发的打字测试软件具有界面简捷、组织测试方便、评分精确合理、成绩回收容易、统计成绩方便.学生水平分析准确等优点.     

QoE enhancement of the industrial metaverse based on Mixed Reality application optimization

As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

如何有效培养和提高高级语言程序设计学习兴趣

针对大学本科高级语言程序设计教学中对学生学习兴趣的培养不够重视的实际状况,文章在分析学习兴趣对教学重要性的基础上,就如何培养和提高学生的学习兴趣进行了深入的研究;结合笔者在计算机本科教学改革中所作的尝试,着重从融洽师生感情、认识课程重要性、精心选择例题、多模式教学、丰富学习交流互动渠道和改进实验体系6个方面给出了合理有效的方法,有效地培养和提高学生的学习兴趣,在很大程度上提高了整体教学效果。     

Fast k most similar neighbor classifier for mixed data (tree k-MSN)

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. For this reason, many fast k-NN classifiers have been developed, some of them are based on a tree structure, which is created during a preprocessing phase using the prototypes in T. Then, in a search phase, the tree is traversed to find the nearest neighbor. The speed up is obtained, while the exploration of some parts of the tree is avoided using pruning rules which are usually based on the triangle inequality. However, in soft sciences as Medicine, Geology, Sociology, etc., the prototypes are usually described by numerical and categorical attributes (mixed data), and sometimes the comparison function for computing the similarity between prototypes does not satisfy metric properties. Therefore, in this work an approximate fast k most similar neighbor classifier, for mixed data and similarity functions that do not satisfy metric properties, based on a tree structure (Tree k-MSN) is proposed. Some experiments with synthetic and real data are presented.