Paphana Yiwsiw
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Paphana Yiwsiw has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Chanon Tantiwattanapaibul
Sirindhorn International Institute of Technology, Thammasat University, 12120, Thailand
2 shared publications - Somrudee Deepaisarn
Sirindhorn International Institute of Technology, Thammasat University
2 shared publications - Suphachok Buaruk
Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
2 shared publications - Virach Sornlertlamvanich
Asia AI Institute (AAII), Faculty of Data Science, Musashino University, Japan
2 shared publications Chanon Tantiwattanapaibul
Sirindhorn International Institute of Technology, Thammasat University,Pathum Thani,Thailand
1 shared publication- Leeyakorn Cheewakriengkrai
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
1 shared publication
Latest Publications
- Open Access
- 30 September 2023
- Published by Korea Institute of Information and Communication Engineering in Journal of information and communication convergence engineering
- Vol. 21 (3) , 216-224
Data-centric Smart Street Light Monitoring and Visualization Platform for Campus Management
Somrudee Deepaisarn, Paphana Yiwsiw, Chanon Tantiwattanapaibul, Suphachok Buaruk, and Virach Sornlertlamvanich. Journal of information and communication convergence engineering 2023;21:216-24. https://doi.org/10.56977/jicce.2023.21.3.216
- 30 September 2023
- Published by Korea Institute of Information and Communication Engineering in Journal of information and communication convergence engineering
- Vol. 21 (3) , 216-224
Open AccessData-centric Smart Street Light Monitoring and Visualization Platform for Campus Management
Somrudee Deepaisarn, Paphana Yiwsiw, Chanon Tantiwattanapaibul, Suphachok Buaruk, and Virach Sornlertlamvanich. Journal of information and communication convergence engineering 2023;21:216-24. https://doi.org/10.56977/jicce.2023.21.3.216
- Open Access
Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics
The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.
Open AccessAutomated Street Light Adjustment System on Campus with AI-Assisted Data Analytics
The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.
- 5 November 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Smart Street Light Monitoring and Visualization Platform for Campus Management
As a recent trend in urbanization and intelligent technologies, smart lighting systems have been implemented in many major cities to support smart urban environments. This research developed a web application platform for data visualization and lighting device monitoring at Thammasat Uni-versity, Rangsit Campus, Thailand. This implementation provides administrative and operative staff with an all-in-one platform through a convenient interface for monitoring, controlling, and collecting data from area devices and sensors. Platform devel-opment was divided into two sections: back-end application, providing application programming interface (API) endpoints, and front-end application, offering an interface for interacting with on-campus staff. Finally, the web application was deployed on a cloud platform so that responsible persons may access it on any device and acquire data in real time. Given the platform's capabilities, further data analytics may be proposed for building a smarter lighting system.
- 5 November 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Smart Street Light Monitoring and Visualization Platform for Campus Management
As a recent trend in urbanization and intelligent technologies, smart lighting systems have been implemented in many major cities to support smart urban environments. This research developed a web application platform for data visualization and lighting device monitoring at Thammasat Uni-versity, Rangsit Campus, Thailand. This implementation provides administrative and operative staff with an all-in-one platform through a convenient interface for monitoring, controlling, and collecting data from area devices and sensors. Platform devel-opment was divided into two sections: back-end application, providing application programming interface (API) endpoints, and front-end application, offering an interface for interacting with on-campus staff. Finally, the web application was deployed on a cloud platform so that responsible persons may access it on any device and acquire data in real time. Given the platform's capabilities, further data analytics may be proposed for building a smarter lighting system.