D. Kuswardhana, E. Haritman, F. Jabbar, F. Nashrullah
{"title":"基于机器学习算法的计算机视觉训练工具包设计,以克服COVID-19大流行期间的远程学习","authors":"D. Kuswardhana, E. Haritman, F. Jabbar, F. Nashrullah","doi":"10.1063/5.0139406","DOIUrl":null,"url":null,"abstract":"The situation in the pandemic COVID-19 period has forced the education system to come into a different way of learning. This paper has oriented how to present laboratory workouts through a computer vision training kit to overcome distance learning in the pandemic. Thus, students (as the user) can use it anywhere. The environment used is on a Single Board Computer of NVIDIA Jetson Nano. The method is using the waterfall model. The way to access the training kit is to use the camera input on the user camera attached to their device connected through a streaming website. We establish the connection using the Remote Desktop Protocol, and The IoT part of the tool uses Antares Cloud Storage. Thus, the device state in the ESP32 microcontroller could be changed by the computer vision program. The entire process, as a result, can be inspected on another web portal containing the summary of the process on each module. © 2023 Author(s).","PeriodicalId":206673,"journal":{"name":"PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of computer vision training kit with machine learning algorithm to overcome distance learning in pandemic COVID-19\",\"authors\":\"D. Kuswardhana, E. Haritman, F. Jabbar, F. Nashrullah\",\"doi\":\"10.1063/5.0139406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The situation in the pandemic COVID-19 period has forced the education system to come into a different way of learning. This paper has oriented how to present laboratory workouts through a computer vision training kit to overcome distance learning in the pandemic. Thus, students (as the user) can use it anywhere. The environment used is on a Single Board Computer of NVIDIA Jetson Nano. The method is using the waterfall model. The way to access the training kit is to use the camera input on the user camera attached to their device connected through a streaming website. We establish the connection using the Remote Desktop Protocol, and The IoT part of the tool uses Antares Cloud Storage. Thus, the device state in the ESP32 microcontroller could be changed by the computer vision program. The entire process, as a result, can be inspected on another web portal containing the summary of the process on each module. © 2023 Author(s).\",\"PeriodicalId\":206673,\"journal\":{\"name\":\"PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0139406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/5.0139406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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