Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

“Learning by Doing”教学模式的探索

"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。

Learning-by-doing教学模式在安全系统工程教学中的应用

安全系统工程课程是安全工程专业的专业基础课,其教学效果的好坏对后续课程的学习以及日后所从事的工作至关重要。因而该课程教学方法的运用选择值得深思。笔者分析了安全工程专业中安全系统工程课程的特点和教学中存在的问题,引用learning-by-doing的教学模式并在教学中加以应用,提出"通过授课得到答案——学会一个解,通过案例讨论得到方法——学会一个方法,通过实践模拟学会学习——学会找到个方法,通过总结学会融会贯通"的安全系统工程教学模式,收到了较好的教学效果。

“Flash动画设计”课程“Learning by doing”教学法探索

本文针对"Flash动画设计"的课程特点引入"Learningbydoing"教学理念,提出"简单实例掌握基本技能→案例讨论学会分析方法→模拟实践学会找到方法→总结经验融会贯通同时实现创新提高"的教学模式,在教学实践中取得了良好的效果。

基于Learning-by-doing的计算机系统结构课程改革

在计算机系统结构的课程教学中,引入由卡内基.梅隆大学提出的"Learning-by-doing"这一适用于工程教学的行之有效的先进教学理念是新教学模式的一种积极探索。文章围绕基于"Learning-by-doing"教学法的计算机系统结构课程改革实施的前期准备、遇到的问题,具体解决方案等环节进行了讨论和分析。

具有不完全技术外部性的随机Learning-by-Doing模型及解法

提出适用于随机Learning-by-Doing模型的"附加效用"值函数解法,并用此方法求解具有不完全技术外部性的随机learning-by-doing模型,得到了均衡时的经济增长路径、消费—资本比和值函数,讨论了技术外部性对私人资本回报率、消费倾向、均值经济增长率和个体福利的影响.

“嵌入式系统程序设计实习”教学改革——探索“Learning by Doing”教学模式

"Learning by Doing"是一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。

基于Learning-by-doing的不确定经济增长与财政政策研究

本文研究基于learning-by-doing的随机增长模型,得到了均衡时的经济增长路径、债券回报率、资产组合份额和消费-资本比,分析财政政策对长期经济增长、资产组合选择、个体消费倾向和个体福利的影响,探讨最优的财政政策。

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

一个多资本的Learning-by-doing模型

考虑一个多资本投入的一般Learning by doing模型 .该模型技术进步的增长率由经济发展过程内生地决定 .当技术对资本为递减规模回报时 ,该经济仅有唯一正的均衡点 .运用单调动力系统理论论证了此模型的稳定性问题 。

Learning by doing方法在移动平台应用开发课中的应用

"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.

The Impact of Industry-Education Integration on College Students’ Motivation to Learn English under the TPACK Framework

This paper explores the impact of industry-education integration on students’motivation in college English courses under the TPACK(Technological Pedagogical Content Knowledge)framework using a comprehensive approach combining quantitative and qualitative methods.Quantitative data analysis indicates a significant positive correlation between the perception of industry-education integration and the level of student learning motivation.There is also a clear association between the perception scores of TPACK framework integration and learning motivation.Qualitative data analysis reveals students’positive experiences and recognition of the TPACK framework integration in practical application projects.The study concludes that industry-education integration and the TPACK framework play a crucial role in enhancing students’learning motivation.It suggests optimizing teaching practices through faculty training,designing practical application projects,and promoting student interaction.This comprehensive analysis provides substantial guidance for the future development of English courses.

Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost

Bloody Mahjong is a kind of mahjong.It is very popular in China in recent years.It not only has the characteristics of mahjong's conventional state space,huge hidden information,complicated rules,and large randomness of hand cards but also has special rules such as Change three,Hu must lack at least one suit,and Continue playing after Hu.These rules increase the difficulty of research.These special rules are used as the input of the deep learning DenseNet model.DenseNet is used to extract the Mahjong situation features.The learned features are used as the input of the classification algorithm XGBoost,and then the XGBoost algorithm is used to derive the card strategy.Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of highdimensional sparse features.In the case of fewer training rounds,accuracy of the model can still reach 83%.In the games against real people,it plays like human.

Learning by Doing Effect in North South Trade under the Global Value Chains： An Empirical Analysis of Various Industries in the U.S.

This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more output and increasing trade value in several industries in the U.S., because the U. S. is at the leading position in the division of labor by global value chains. The empirical evidence reveals that more international outsourcing, there will be more detailed division of labor, and the industry unit production time and production cost will show more declining trend year by year. This is consistent with that the global value chains and the outsourcing play more and more important roles in the international division of labor and economic growth in both developed and developing countries, and helps explain the integration of workforce across countries in the global value chains.

On the Integrated Learning of English and Law

This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which inevitably involves an integrated learning of English and law.Secondly,it points out that the content of legal English reflects a combination of legal knowledge and English skills.Thirdly,it expounds on the difficulties that Chinese English majors are facing in the process of learning English and law simultaneously and furnishes some practical suggestions.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.