检索结果-内蒙古大学图书馆

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Mi, Chunqiao Deng, Qingyou Zhao, Changhua Yin, Dong Liu, Yiwen School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

At present, online teaching has become more and more popular especially in the context of the current epidemic, and quality governance has become the internal needs of modern education development, while there is no simple and easy to use learning situation risk cognition method for specific online teaching class. In order to deal with this problem, in this study a data-driven method of learning situation risk cognition and measurement for online teaching is provided, which uses student initiative degree, concentration degree, duration degree and interaction degree to measure comprehensive learning effective degree and reflect student learning situation risk in an online class. Besides, normalized score earned by student in knowledge point test after online class is used to validate the calculation method designed, and the obtained results show that it is promising and easy to calculation. It provides a basis for decision making of students’ learning situation risk early warning and also provides a data-driven management method for the guarantee of online teaching quality, which has both academic and practical significance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A Study on Random Differentiation Methods for Homework Based on Fuzzy Test 17th

A Study on Random Differentiation Methods for Homework Based...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Liu, Yiwen Shi, Yuanquan Peng, Xiaoning Mi, Chunqiao Yang, Yujun School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

A fuzzy test-based method for random differentiation of homework is proposed to address the problems of low update frequency, serious plagiarism and weakened barriers in university coursework. In order to generate fuzzy homework questions, a semi-automatic framework for fuzzy test-based homework is designed and the four key steps are described in formal language, including pre-processing, variable construction, variable screening and difficulty assessment. The effectiveness of the method is illustrated through a comparison of student teaching data, questionnaire feedback and faculty interviews in a pilot course on principles of computer composition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Formal languages

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-dimensional Bilingual Teaching Model of Computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "Computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling 2

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

引用

2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Yuan, Lilian Fang, Weilin Xiao, Hongbo Xiao, Jianhua Shi, Yuanquan Yang, Yujun School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Wuling-Mountain Ecological Agriculture in Hunan Province Key Laboratory of Intelligent Control Technology for Huaihua Hunan418000 China

ISBN: (纸本)9781665464017

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches. © 2022 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

引用

10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network 6th

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

引用

6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Lin, Jing Mi, Chunqiao School of Computer Science and Engineering Huaihua University HuaihuaHunan418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities HuaihuaHunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province HuaihuaHunan418000 China

ISBN: (纸本)9789811579837

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Zhirong Liu, Yiwen Gao, Yanxia Wen, Wenkan Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Preliminary Study on University Computer Basic Curriculum Reform Aimed at Cultivating Computational Thinking from the Perspective of Smart Education 18th

A Preliminary Study on University Computer Basic Curriculum ...

引用

18th International Conference on Computer Science and Education, ICCSE 2023

作者： Yu, Niefang Liu, Yiwen Peng, Xiaoning Li, Xiaomei Lu, Youmin School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819707904

With the rapid advancement of information technology such as artificial intelligence, cloud computing, big data, virtual reality, etc., in the context of the deep integration of information technology and education and teaching, smart classroom has set off a new round of revolution. Based on this background, this paper discuss how to cultivate students’ computational thinking in a smart education environment, and explore the cultivation of computational thinking in basic university computer courses from three aspects: before class, during class, and after class in smart classrooms. In the background of smart education, only by comprehensively cultivating students’ computational thinking that we can achieve more results in the future. Thereby improving students’ ability to comprehensively apply computer technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Evaluation of numerical wave model for typhoon wave simulation in South China Sea

引用

Water Science and Engineering 2018年第3期11卷 229-235页

作者： Zhi-yuan Wu Chang-bo Jiang Bin Deng Jie Chen Yong-gang Cao Lian-jie Li School of Hydraulic Engineering Changsha University of Science and TechnologyChangsha 410004China Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province Changsha 410004China School for Marine Science and Technology University of Massachusetts DartmouthNew BedfordMA 02744USA Key Laboratory of Technology for Safeguarding of Maritime Rights and Interests and Application State Oceanic AdministrationGuangzhou 510310China

The simulating waves nearshore(SWAN) model has typically been designed for wave simulations in near-shore regions. In this study, the model's applicability to the simulation of typhoon waves in the South China Sea(SCS) was evaluated. A blended wind field, consisting of an interior domain based on Fujita's model and an exterior domain based on Takahashi's model, was used as the driving wind field. The waves driven by Typhoon Kai-tak over the SCS that occurred in 2012 were selected for the numerical simulation research. Sensitivity analyses of time step, grid resolution, and angle resolution were performed in order to obtain optimal model settings. Through sensitivity analyses, it can be found that the time step has a large influence on the results, while grid resolution and angle resolution have a little effect on the results.

关键词： Typhoon wave South China Sea SWAN model Numerical wave model Wave prediction and simulation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：