Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.

Decomposition of Mathematical Programming Models for Aircraft Wing Design Facilitating the Use of Dynamic Programming Approach

Aircraft designers strive to achieve optimal weight-reliability tradeoffs while designing an aircraft. Since aircraft wing skins account for more than fifty percent of their structural weight, aircraft wings must be designed with utmost care and attention in terms of material types and thickness configurations. In particular, the selection of thickness at each location of the aircraft wing skin is the most consequential task for aircraft designers. To accomplish this, we present discrete mathematical programming models to obtain optimal thicknesses either to minimize weight or to maximize reliability. We present theoretical results for the decomposition of these discrete mathematical programming models to reduce computer memory requirements and facilitate the use of dynamic programming for design purposes. In particular, a decomposed version of the weight minimization problem is solved for an aircraft wing with thirty locations (or panels) and fourteen thickness choices for each location to yield an optimal minimum weight design.

新医科背景下医学生的Python课程教学设计与实践

在新医科背景下,分析医学生开设Python编程课程的重要意义。针对在医学生Python教学中存在的挑战,从课程组织、教材选择、课程设计和教学模式等方面,将医学数据管理与Python编程教学深度融合,设计并实践了新的Python课程教学方法。经过4年多的教学研究实践,将Python的代码实现与医学知识相融合,启发学生通过实际医学项目应用他们所学的知识,培养学生的问题解决和数据分析能力。通过设计调查问卷和形成性评价的形式对课程实施效果进行评价,课程在增强学生学习Python编程兴趣和提高Python应用能力方面收到了很好的效果。课程组以高质量教育培育高水平、高素质、高融合医科人才为目标,通过医学信息数据处理案例为中心组织课堂,紧跟人工智能、深度学习等新技术,培养新医科人才,服务于“健康中国”大目标。

Python程序设计课程思政教学实践探索

将思政元素融入Python程序设计专业课教学之中,是当前教学的重要任务。围绕课程思政目标、课程思政资源挖掘、课程思政教学实施等方面,阐述Python程序设计课程思政元素挖掘与教学实践,旨在让学生学好专业课程的同时,潜移默化地接受思政教育,进而提高学生正确认识问题、分析问题和解决问题的能力。

基于Python的文件统计程序开发

为解决考试过程中学生提交文件数量大、类型多、易出错等问题,以“实用软件应用”课程考核为例,采用Python语言及第三方库Pandas开发文件统计程序,按照需求分析、总体设计、模块实现,阐述开发流程,侧重介绍交互设计与精确统计函数。此程序既可满足日常作业统计需求,亦可满足考试文件即时统计需求,有效提升了统计的效率和准确性,且易于扩展和移植,具有较好的应用价值与推广价值。

大数据技术背景下Python程序设计研究

Python语言在计算机程序设计中具有编程灵活、功能性强等特点,在实际应用中,Python语言代码排列简洁,资源库丰富,排列修改灵活。大数据技术背景下,Python语言的应用范围不断拓宽,具有良好应用前景。基于此,本文设计了大数据背景下Python程序的整体架构,分析其具体的应用流程,结合某企业质检工作,证明Python语言的应用价值,以期为相关工作的研究提供有效参考。

Python程序设计课程思政在公安教育中的创新与实践

文章以Python程序设计课程为例,探讨了公安大数据背景下课程思政教学改革的实施过程与成效。针对专业认知不足和实践能力不强等问题,提出了“守正创新·踔厉奋发”的课程思政体系。改革内容涵盖了课程思政体系的构建、知识讲授的创新、实践技能培养的强化以及创新能力提升的策略四个方面。通过这一系列的教学改革,学生的专业认同感、学习兴趣和创新能力得到提升。

循序渐进式教学方法在“Python程序设计”课程中的应用

针对Python课程教学过程中存在的课时不足、Python语言的学习难度分层递增等问题,文章提出教师可以通过举一反三、归纳梳理、知识迁移、融入应用、对比分析、项目实践的方法开展系统而有效的编程教学,采用逐级巩固的循序渐进式教学方法,引导学生逐步掌握Python的知识和技术要领。

“Python程序设计”课程与思政教育协同教学模式实践研究

针对思政教学不能很好地融入程序类课程的问题,对“Python程序设计”课程思政教学模式进行了探究。首先分析了该门课程思政教学的现状,然后从知识点导入、理论讲解和应用实践等三个方面探索课程思政教学模式的具体设计,构建出程序类课程与思政教育相融合的协同式思政教学模式,并进一步从这三个角度提炼课程中蕴含的思政元素融入点。最后,对所提出的协同式思政教学模式进行了教学实践并评估了其实际效果。实践结果表明,所提出的协同式思政教学模式实现了课程教学与思政教育的协同融合,激发了学生学习的主观能动性,提升了思政育人的效果。

Improved design of reconfigurable frequency response masking filters based on second-order cone programming

In order to improve the design results for the reconfigurable frequency response masking FRM filters an improved design method based on second-order cone programming SOCP is proposed.Unlike traditional methods that separately design the proposed method takes all the desired designing modes into consideration when designing all the subfilters. First an initial solution is obtained by separately designing the subfilters and then the initial solution is updated by iteratively solving a SOCP problem. The proposed method is evaluated on a design example and simulation results demonstrate that jointly designing all the subfilters can obtain significantly lower minimax approximation errors compared to the conventional design method.

新商科背景下“Python程序设计”的课程思政教学探索

新一代信息技术的蓬勃发展推动了数字经济的蓬勃发展,使人类社会迈入了数字经济时代,中国的商业模式和商业形态都发生了巨大的变化,开始由以工业经济为背景的传统商科向以数字经济为背景的新商科迈进,新商科注重学科间的交叉融合,使得跨学科综合型人才的需求量逐渐增大。“Python程序设计”作为新商科学生的公共基础课,具有知识点繁多、理论性和实践性较强等特点。在新商科背景下,将思政元素融入“Python程序设计”教学中,通过分析课程思政的德育目标,使学生在牢固掌握专业知识的同时,提升思想政治价值观念,形成勤奋好学的学习态度,以期为推动新商科人才核心素养的培育和发展提供改革路径及推进方法。

医学影像学生Python课程设计及实践

研究医学影像学生Python课程的设计与实践。Python编程语言在医学影像学领域的应用日益广泛,然而当前以生物医学数据为主的教学往往未能满足医学影像学生的专业需求。为此,设计了以医学影像数据案例驱动的教学模式,并增加了数据展示与可视化和影像组学分析的小组创新项目训练。教学实践表明,学生对课程设计表现出了较高的学习兴趣,小组创新项目训练培养了学生的实践应用和问题解决能力。然而,仍需要进一步加强专业方向内容,提供更多实践机会,以满足学生的学习需求。

面向数据分析的Python语言课程设计

本文旨在探讨面向数据分析的Python语言课程设计,让学生更好地适应大数据时代。阐述了当前的时代背景和Python语言课程的现状,并指出Python语言课程设计的教学改革的必要性。然后分析了行业的需求以及学生需要的技能和知识。指出要实现教学改革目标,可以从教学方法、教学内容、考核方案三个方面入手,最后指明了进一步研究的可能方向。

编译型嵌入式Python的设计与实现

针对面向微控制器的解释型MicroPython具有实时性弱、占用存储空间大和可移植性较差等问题,提出一种将Python语言转化为C++语言并将构件层与应用层分离的编译型嵌入式Python方案,设计基于抽象语法树及类型注释的源码映射机制。在此基础上,设计嵌入式Python编译器,实现集成开发环境AHL-GEC-IDE(for Python版),完成Python源文件的编辑、编译、链接和下载。实践结果表明,该编译型Python方案可行,为嵌入式人工智能领域提供了一种实时性较高、编辑编译方便、可移植性较强的编译型Python集成开发环境。

基于Python的面向对象弹塑性有限元方法实现与数值模拟

随着有限元应用领域的不断扩大,对有限元的可扩充性提出了更高的要求。为克服传统有限元的缺陷,基于Python提出了一种简单、易扩充的面向对象弹塑性有限元程序框架,结合Python的特性,设计了前处理类、后处理类、线性求解类、应力积分类、分析类等有限元类。通过将程序框架应用于几类典型弹塑性问题的有限元分析与模拟,并与ABAQUS计算结果进行了对比,验证了基于Python的面向对象弹塑性有限元程序的准确性和有效性。

布朗运动的python模拟及其应用

布朗运动为物质微观模型的建立提供了重要依据,是分子运动论和统计力学发展的基础.本文基于python语言模拟了布朗运动方差均值与时间之间的关系以及每一条随机运动轨迹上的所有相应点的x方向上位移做平均后的分布,得出的结论与理论导出的一致,直观地说明了布朗运动的实质.此外,本文还基于细胞世界中的某些蛋白的实验数据得出了玻尔兹曼常数.

基于Python语言的课程教学对策分析

文章通过对python语言的面向对象程序设计的课程分析,阐释了Python语言的特点和面向对象程序设计课程内容及方法。同时,探讨了Python语言的面向对象程序设计教学存在的问题。研究结果表明,采用项目驱动的教学方法和实践性的学习任务可以提高学生的学习兴趣和动手能力。基于此,在Python语言的面向对象程序设计课程中,应采用项目驱动的教学方法,结合实践性的学习任务,以提高学生的学习效果。

基于Python和Shell的《Linux实训项目自测平台》软件设计

近年来,Linux操作系统在各国不断地发展与推广,高等院校开设的Linux操作系统课程也越来越受到重视。通过对软件界面设计及在Linux操作系统中检测实训项目的几种常用方式进行分析和研究,给操作系统课程教学提供一种教学思路,同时对Linux教学辅助软件开发者提供一些借鉴和参考。