The DOI matches its Crossref record.
An Improvement of Led Lighting System Accuracy with Voltage Control System
Abstract
This paper proposes an effective LED lighting control system to increase the accuracy of electric power usage. The efficient use of electric power is a problem that needs to be pursued a solution. There are several previous works have been proposed to reduce electric power consumption. However, no one has focused on saving LED lights by using a voltage control system. In this paper, a LED lighting control system has been developed by adjusting environmental conditions using light intensity sensors and the presence of people. Some experiments have been carried out to prove the effectiveness of the system. The results show that the system has successfully adjusted the light intensity based on the presence of people in the room and room light intensity. So, the light intensity could adapt to the environment as the control system work. PWM control system has a high correlation, more than 99%, compare to room light intensity. It shows the effectiveness of the control system. Thus the goal of lighting efficiency has been achieved effectively.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The dimming capability of LED lighting can add to the savings already obtained from manually turning on and off settings.
“Abdurohman et al. [92] explains that the dimming capability of LED lighting can add to the savings already obtained from manually turning on and off settings.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Abdurohman et al. [92]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 14
- mardaljevic, 2011, Daylighting metrics for residential buildings
- datasheet, 0, Sensor (HC-SR501), Diakses tanggal, 30
- 10.1016/j.enconman.2009.11.046 · DOI
- 10.1109/MIA.2002.1044206 · DOI
- laskowsi, 1976, Autoranging digital luxmeter
- 10.1109/ACCESS.2015.2497312 · DOI
- 10.1016/j.enbuild.2015.07.035 · DOI
- abdurohman, 2018, Integrated Lighting Enabler System Using M2M Platforms for Enhancing Energy Efficiency, J Inf Process Syst, 14
- 10.1016/j.buildenv.2018.05.005 · DOI
- ramadhan, 2019, Improving Smart Lighting with Activity Recognition Using Hierarchical Hidden Markov Model, Indones J Comput Indo-JC, 4, 43
- 10.1109/WOCC.2019.8770696 · DOI
- 10.1111/j.1708-8240.2002.tb00524.x · DOI
- jy, 1992, Power supply systems for neon lights
- 10.1109/ICIC47613.2019.8985858 · DOI
Cited by
Sorted by citations
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“Abdurohman et al. [92] explains that the dimming capability of LED lighting can add to the savings already obtained from manually turning on and off settings.”
BackgroundSelf-citation
Metadata
- Authors
- Abdurohman, M., Nugraha, R., & Gautama Putrada, A.
- Affiliations
- Telkom University (ID)
- Venue
- 2020 Fifth International Conference on Informatics and Computing (ICIC)
- Publisher
- IEEE
- Published
- 2020-11-03
- Volume
- 30
- Pages
- 1-5
- OpenAlex
- W3114099540
- Cited by
- 7
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 4 · 2021: 1
- FWCI
- 2.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Informatics and Computing (ICIC) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
- Keywords
- LED lighting control, voltage regulation, energy conservation, light intensity sensor, occupancy detection, PWM control, adaptive lighting, energy consumption reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
