Education cloud platform is an online education platform built on cloud computing to provide students with a more efficient and convenient teaching and learning environment. However, due to the increasing scale of the...
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ISBN:
(纸本)9789819623907
Education cloud platform is an online education platform built on cloud computing to provide students with a more efficient and convenient teaching and learning environment. However, due to the increasing scale of the platform and the increasing number of users, it is particularly important to effectively manage and optimize the users and resources in the network. On this basis, a teaching cloud platform based on the Louvain algorithm is proposed, which can effectively solve the above problems. The Louvain algorithm is a community discovery method based on graph theory, which maximizes the connectivity within the community and minimizes the connectivity between communities. On this basis, the Louvain algorithm is used to divide student clubs, in order to better meet the personalized learning needs of students. Dividing users into different levels can provide students with richer teaching and learning resources more accurately, improving their learning efficiency and quality. In addition, the teaching cloud platform constructed using the Louvain algorithm has achieved optimal allocation of teaching resources. By properly allocating resources within the platform, the efficiency of resource utilization can be effectively improved, thereby enhancing the operational efficiency of the system. A optimization scheduling method based on user group characteristics and resource requirements was proposed, achieving optimal resource allocation. In addition, the satisfaction level of users is highest at 85% to 89%, indicating a high overall satisfaction. Therefore, this article proposes a teaching cloud platform design and implementation scheme based on the Louvain algorithm, which is of great significance for improving the performance of teaching systems, optimizing resource allocation, and enhancing user experience. By discovering clubs and optimizing resource allocation, the education cloud platform can better meet the personalized needs of users, providing students with a mor
As an important part of digital education, online autonomous learning is expanding its application scope and depth. However, the abnormal learning behaviors associated with it are increasing day by day. Allowing this ...
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A cloud classroom is a new type of online education that has recently evolved within the framework of the Internet and education. learning in a cloud classroom means students access course materials and goals online, ...
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This study aims to explore the application of immersive teaching in electromagnetic spectrum courses, in order to improve teaching effectiveness and cultivate professional talents. Firstly, the learning objectives and...
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Generative Artificial Intelligence (GenAI) is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, the use of GenAI in education raises ethical conc...
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Despite their considerable dissemination, existing UML modeling tools suffer from significant limitations that stand in the way of their profitable use in practice as well as in teaching. This paper presents a new UML...
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ISBN:
(纸本)9783031790584;9783031790591
Despite their considerable dissemination, existing UML modeling tools suffer from significant limitations that stand in the way of their profitable use in practice as well as in teaching. This paper presents a new UML modeling tool, called UML-MX (c), that overcomes these limitations. It is based on a language architecture that not only enables the integration of class and object diagrams, but also the execution of objects in the diagram editor. Thus, it promotes a more inspiring learning experience. At the same time, it goes beyond the limitations of traditional approaches to model-driven software development by enabling a common representation of models and programs.
The proliferation of Artificial Intelligence (AI), academic landscapes in research and learning are shifting from traditional to innovative strategies, greatly impacting students’ learning and teachers’ pedagogies i...
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As a very popular teaching resource in recent years, "micro video" has the characteristics of highly focused learning themes, highly focused learning content and repeatable learning. When applied to life sci...
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Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Mo...
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Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technological advancements in digitalization, machine-learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model-based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model-based control and optimization is increasing rapidly. However, there are no easy-to-use, flexible, and freely available open-source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to solve machine-learning-supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO-MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.
In recent years, the field of automation engineering is rapid advancements and technological breakthroughs, resulting increasing demand for qualified and innovative professionals. To meet these requirements, higher ed...
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ISBN:
(数字)9781510686847
ISBN:
(纸本)9781510686830
In recent years, the field of automation engineering is rapid advancements and technological breakthroughs, resulting increasing demand for qualified and innovative professionals. To meet these requirements, higher education and vocational education in engineering education sector are constantly seeking effective teaching models that foster innovation and nurture talent in automation engineering and other engineering fields. One such teaching model that has gained prominence is the engineering Practice Innovation Project (EPIP) teaching model. This paper aims to promote and evaluate the effectiveness of the EPIP teaching model in fostering innovation and nurturing talents in automation engineering education. The study investigates the impact of the EPIP model on the trainee's ability to apply theoretical knowledge to practical projects, on their learning outcomes, and the development of their creativity and innovative skills. Preliminary findings indicate that the EPIP teaching model positively impacts on students' learning outcomes and innovation skills. Moreover, the collaborative nature of the EPIP model encourages teamwork, thinking out of box, and increase engineering practice skills, essential for success in the automation engineering industry. Furthermore, the EPIP model promotes innovation by providing students with opportunities to explore novel ideas and develop creative solutions and also plays a crucial role in talent development in automation engineering. The EPIP teaching model is designed to bridge the gap between theory and practice by providing trainees with practical experience in solving real-world problems. The results shows that EPIP teaching model for fostering innovation and talent in automation engineering education is effective. Trainees feedback is used to analyses the effectiveness fostering innovation and talents in automation engineering education through engineering Practice Innovation Project (EPIP) teaching model. More than 90% of the
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