Optimization of indoor thermal environment based on sensor networks and multimedia assisted physics teaching
DOI:
https://doi.org/10.4108/ew.6546Keywords:
Sensor network, Indoor thermal environment optimization, Multimedia assistance, Physics teachingAbstract
Multimedia technology combines various media elements such as text, images, sound, and video, making classroom teaching more vivid, intuitive, and interesting. This article introduces a multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology. This application has two functional modules: user user end and cluster control end. On the user end, obtain user commands through the virtual machine end and share the original multimedia files using the CIFS protocol. The user command is redirected to the user end, and then the corresponding command is executed on the user end to directly transmit and play the original multimedia file. At the same time, the user end will also provide data feedback and recording for subsequent data analysis and evaluation. At the cluster control end, based on the information collected by the sensor network, an adaptive linear regression algorithm is used to predict the reference value. By analyzing and processing the collected information, the cluster control end can reasonably arrange the playback content of multimedia courseware to meet the learning needs of students. This multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology provides new help and support for physics teaching.
Downloads
References
[1] Park C, Kim D G, Cho S, Han H J (2019) Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior 92:288-296
[2] Vaganova O I, Bakharev N P, Kulagina J A, Lapshova A V, Kirillova I K (2020) Multimedia technologies in vocational education. Amazonia investiga 9(26):391-398
[3] Shu Y (2020) Experimental data analysis of college English teaching based on computer multimedia technology. Computer-Aided Design and Applications 17(S2):46-56
[4] Liu G, Zhuang H (2022) Evaluation model of multimedia-aided teaching effect of physical education course based on random forest algorithm. Journal of Intelligent Systems 31(1):555-567
[5] Weng S S, Chen H C (2020) Exploring the role of deep learning technology in the sustainable development of the music production industry. Sustainability 12(2):625
[6] Yin X (2015) A study of the effect of multimedia courseware on oral college English teaching. Journal of Language Teaching and Research 6(5):1106
[7] Fang P E N G (2021) Optimization of music teaching in colleges and universities based on multimedia technology. Advances in Educational Technology and Psychology 5(5):47-57
[8] Bykonia O P, Borysenko I V, Zvarych I M, Harbuza T V, Chepurna M V (2019) Teaching Business English to Future Economists Using a Multimedia Textbook. International Journal of Higher Education 8(4):115-123
[9] Kratzke N (2014) A lightweight virtualization cluster reference architecture derived from open source paas platforms. Open Journal of Mobile Computing and Cloud Computing 1(2):17-30
[10] Volk T, Keimel C, Moosmeier M, Diepold K (2015) Crowdsourcing vs. laboratory experiments–QoE evaluation of binaural playback in a teleconference scenario. Computer Networks 90:99-109
[11] Colloton E, Moomaw K (2018) Rewind, Pause, Playback: Addressing a Media Conservation Backlog at the Denver Art Museum. The Electronic Media Review 5:2017-2018
[12] Kim D Y, Kim S, Hassan H, Park J H (2017) Adaptive data rate control in low power wide area networks for long range IoT services. Journal of computational science 22:171-178
[13] Mohapatra H, Rath A K (2020) Fault‐tolerant mechanism for wireless sensor network. IET wireless sensor systems 10(1):23-30
[14] Deb S (2011) Effective distance learning in developing countries using mobile and multimedia technology. International Journal of Multimedia and Ubiquitous Engineering 6(2):33-40
[15] Sharma S, Khare S, Huang B (2016) Robust online algorithm for adaptive linear regression parameter estimation and prediction. Journal of Chemometrics 30(6):308-323
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
![Creative Commons License](http://i.creativecommons.org/l/by/3.0/88x31.png)
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.