深入浅出讲解“Windows程序设计—Visual C++”课程

针对Windows程序设计——Visual C++课程涉及知识面广、实践性强、复杂难学的特点,分析了该课程难点之所在及传统教学过程中存在的问题,并从教学内容、教学方法和模式等方面采取了一些改革措施。实践证明,改革措施能够激发学生的编程兴趣,提高学生自主学习的能力,使学生较快进入状态,提高教学质量。     

项目实训中的协同编程方法研究

在高职院校的软件项目实训环节中,大多项目需要团体协作进行编程。针对如果有效提高学生的协同编程效率、团队合作意识以及合作学习能力的问题,提出了基于Dillenbourg原则的协同编程方法。通过实践证明,该方法取得了良好的实训效果。     

Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

失速型风力发电机的建模与仿真研究

研究风力发电机系统优化控制问题,对风力发电进行设计,应通过风能带动发电机发电。但风力发电机组模型存在高阶、非线性问题,考虑因素很多。传统方法缺乏总体性能分析。为解决上述问题,建立了包括风力发电机组气动性能、传动系统部分和感应电机部分的一种新的较完善的失速型风力发电机组的动态数学模型。应用在MATLAB软件上,以数学模型为基础搭建了失速型风力发电机组数字仿真模型。并以稳态风速和动态风速为例,对600千瓦失速控制型风力发电机组进行了仿真计算。试验结果表明模型的正确性,为风力发电机组的进一步优化设计和优化控制等均提供了科学依据。     

飞行仿真系统数据可视化设计与实现

研究飞行仿真系统结果数据可视化,为验证分析飞行仿真系统结果的正确性,需要对仿真系统输出的飞行性能数据进行实时在线观测并与标准数据进行对比分析。针对飞行仿真系统对飞机飞行数据实时观测的需求,设计了一种可实时观测的飞行仿真数据可视化方案。在建立仿真系统模型的基础上,为了提高系统的可靠性和优化数据结构,提出了新的数据结构和曲线生成算法绘制实时曲线,实现了数据的可视化观测,避免了实时绘制过程屏幕的闪烁和曲线出现的锯齿。并结合飞行仿真特点,利用MapX组件技术加载了具有导航台的全国地图,对传统的电子地图显示进行了改进,实现了飞机在地图上的实时航迹观测。仿真结果表明,系统能够实时观测仿真结果并分析系统误差,实现飞行仿真数据的可视化,并可验证飞行仿真系统的正确性。     

浅谈自增自减运算符在C语言中的应用

C语言的运算符,操作灵活,其中自增自减运算符的功能较为特别。合理的使用自增自减运算符可以提高程序执行的效率,但同时灵活使用自增自减运算符也是个难点。根据自增自减运算符的特点和不同使用形式,剖析自增自减运算符的使用规则,以便更好地帮助初学者学习和使用自增自减运算符。     

动态Web Server在单片机上的实现

通过在单片机上实现网络化功能,从而进行远程控制已成为嵌入式系统发展的一个方向。本文就此需求介绍了动态Web Server的基本原理,讨论了在无操作系统单片机环境中开发动态Web Server的一种可行的方法。