Cost-Effective LED Dimming Driver With Single Chip Design for Smart Lighting System

Hsia, Shih-Chang; Sheu, Ming‐Hwa; Ciou, Jyun-Jia

doi:10.1109/access.2020.3013302

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…The complexity of a problem in smart lighting affects the proposed control solution. According to Hsia et al [34] there are two types of control in smart lighting, namely manual control and automatic control. Automatic control can apply simple control using a passive infrared (PIR) sensor to detect motion.…”

Section: ) Smart Lighting Controlmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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Section: ) Smart Lighting Controlmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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“…In this system, the electric energy consumed was only 10.5% of that consumed by existing methods. A high-power (60 V/1 A) single-chip driver for LED dimming was presented [34]. To keep high-stable LED lighting, the dimming techniques used current mode rather than PWM (Pulse Width Modulation) to reduce flicker.…”

Section: Smart Street Lighting Control Mechanismsmentioning

confidence: 99%

Smart City: Recent Advances in Intelligent Street Lighting Systems Based on IoT

Omar

AlMaeeni²,

Attia

et al. 2022

Journal of Sensors

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Based on the importance of energy saving in terms of reducing the carbon impact and global warming problems, smart street lighting systems have been proposed in the past few years with different specifications. These systems include sensors for controlling the light intensity and connectivity for recording weather conditions and diagnosing lamp failure remotely. This paper discusses many published research studies regarding smart street lighting systems, providing a comparison between these systems which emphasizes the limitations of each one of them. Current and future trends are highlighted.

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“…In the papers [8][9][10][11][12][13], systems using artificial intelligence and machine-learning algorithms are proposed. However, the authors of these systems mention considerable computational effort required for the stable control of the solid-state light source, and the complexity of systems for continuous measurement of the illuminance.…”

Section: Startmentioning

confidence: 99%

The Embedded System to Control the Illuminance of an Office Workplace with LED Light Sources

Górecki

Ptak

Wnuczko³

2022

Energies

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In the paper, an embedded system dedicated to control lighting of the workplace is proposed. The construction of the considered lighting system with a power LED (Light Emitting Diode) and the idea of its control are presented. Some results of investigations of the constructed lighting system, particularly those of the measurements of its optical and electrical parameters, are shown and discussed. The presented results show that by using this system, it is possible to obtain a high value of lighting uniformity in the workplace at different values of the main lighting in the room. The control is fast, and none of the reaction times achieved during the tests exceeded even 1 s while ensuring a smooth change. Although the layout and algorithms used are simple, it can be considered an advantage as they do not introduce unnecessary complexity while meeting all the requirements of standards. A discussion of the obtained results is given, particularly in the range of energy savings.

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Cost-Effective LED Dimming Driver With Single Chip Design for Smart Lighting System

Cited by 11 publications

References 28 publications

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

Smart City: Recent Advances in Intelligent Street Lighting Systems Based on IoT

The Embedded System to Control the Illuminance of an Office Workplace with LED Light Sources

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