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物联网场景下的英语信息教学的多元智能新模式。

A New Model of Multiple Intelligence for Teaching English Informatics in the IoT Scenario.

机构信息

School of Education, Xi'an Fanyi University, Xi'an, Shaanxi 710105, China.

出版信息

Comput Intell Neurosci. 2022 Jun 21;2022:5642284. doi: 10.1155/2022/5642284. eCollection 2022.

DOI:10.1155/2022/5642284
PMID:35774434
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9239778/
Abstract

This paper presents an in-depth study on the new mode of intelligent multidistance teaching of English with the help of virtual scenes of the Internet of Things. The virtual simulation technology is integrated into the traditional IoT teaching, and the professional education of IoT application technology is tapped; from the analysis of the current situation of IoT skills teaching and the feasibility of carrying out virtual simulation teaching, the "four-driven" design principle is proposed, and the teaching design is combined with the virtual simulation technology teaching, and the case design of the skill-based virtual simulation technology teaching of experience, demonstration, interaction, and assessment in the virtual environment is given. This paper presents the case design of virtual simulation teaching in a virtual environment with experience, demonstration, interaction, and assessment, and the multidimensional effect evaluation of IoT skills teaching and researches the application of virtual simulation to IoT skills teaching through the above four aspects. In this paper, a framework for distributed collaborative computing is built using an asynchronous message queue MQ, which enables multiple nodes to serve a task through task splitting. The DeepCluster module can effectively cluster the time series by deep representation learning and obtain the typical variation of time series patterns. In the task offloading module of the framework, a task offloading decision algorithm based on a value-constrained multi 0-1 backpacking model is designed to minimize task processing latency with an optimal offloading solution. The system test results show that the proposed distributed computing framework and offloading decision algorithm can significantly reduce the processing latency of large tasks.

摘要

本文深入研究了借助物联网虚拟场景实现英语智能远程教学的新模式。将虚拟仿真技术融入到传统物联网教学中,挖掘物联网应用技术专业教育;从物联网技能教学现状和开展虚拟仿真教学的可行性分析出发,提出“四驱动”设计原则,将教学设计与虚拟仿真技术教学相结合,给出了虚拟环境中基于经验、演示、交互和评估的技能型虚拟仿真技术教学案例设计。本文在虚拟环境中提出了具有体验、演示、交互和评估的虚拟仿真教学案例设计,并通过以上四个方面研究了虚拟仿真在物联网技能教学中的应用。本文构建了一个基于异步消息队列 MQ 的分布式协同计算框架,通过任务拆分使多个节点可以共同完成一个任务。DeepCluster 模块可以通过深度表示学习有效地对时间序列进行聚类,并获取时间序列模式的典型变化。在框架的任务卸载模块中,设计了一种基于值约束多 0-1 背包模型的任务卸载决策算法,以最优的卸载方案最小化任务处理延迟。系统测试结果表明,所提出的分布式计算框架和卸载决策算法可以显著降低大型任务的处理延迟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/c2a0c11ae43b/CIN2022-5642284.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/21a47cf5efd4/CIN2022-5642284.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/08c7c46c3c9c/CIN2022-5642284.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/c2a0c11ae43b/CIN2022-5642284.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/21a47cf5efd4/CIN2022-5642284.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/a36253340ca3/CIN2022-5642284.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/a7b121f33e77/CIN2022-5642284.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/ab6be9d5c2ed/CIN2022-5642284.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/b1c66e7d9805/CIN2022-5642284.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/860dda1bdd59/CIN2022-5642284.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/08c7c46c3c9c/CIN2022-5642284.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b305/9239778/c2a0c11ae43b/CIN2022-5642284.008.jpg

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Strategies for building robust prediction models using data unavailable at prediction time.利用预测时不可用的数据构建稳健预测模型的策略。
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