An Indoor Smart Lamp For Environments Illuminated Day Time

Cihan, Ayse Nur; Güğül, Gül Nihal

doi:10.1109/ewdts50664.2020.9224900

Cited by 4 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Acosta et al, [72], lighting energy consumption in the building is 15% to 30%. Cihan and Güğül [73] adds that energy consumption in commercial buildings in the US is up to 25%, and in Turkey, it is up to 9%. The primary strategy for energy savings in the lighting sector is to utilize daylight.…”

Section: ) Office and Building Smart Lightingmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: ) Office and Building Smart Lightingmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The second design represents a smart lamp controller that can turn off a light bulb or light a bulb at three different intensities: low light intensity, medium intensity and high intensity. In [22], a device similar to the one verified in this paper is presented. These intensities are controlled by a sensor or by receiving commands from a button.…”

Section: Employed Duts and Coverage Targetsmentioning

confidence: 99%

“…The target of the implemented automation process consists of stimuli generation to obtain at least one result in each of these intervals during the 20 operations performed.The second design represents a smart lamp controller that can turn off a light bulb or light a bulb at three different intensities: low light intensity, medium intensity and high intensity. In[22], a device similar to the one verified in this paper is presented. These intensities are controlled by a sensor or by receiving commands from a button.…”

mentioning

confidence: 99%

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

Danciu

Dinu

2022

Applied Sciences

View full text Add to dashboard Cite

The functional verification process is one of the most expensive steps in integrated circuit manufacturing. Functional coverage is the most important metric in the entire verification process. By running multiple simulations, different situations of DUT functionality can be encountered, and in this way, functional coverage fulfillment can be improved. However, in many cases it is difficult to reach specific functional situations because it is not easy to correlate the required input stimuli with the expected behavior of the digital design. Therefore, both industry and academia seek solutions to automate the generation of stimuli to reach all the functionalities of interest with less human effort and in less time. In this paper, several approaches inspired by genetic algorithms were developed and tested using three different designs. In all situations, the percentage of stimulus sets generated using well-performing genetic algorithms approaches was higher than the values that resulted when random simulations were employed. In addition, in most cases the genetic algorithm approach reached a higher coverage value per test compared to the random simulation outcome. The results confirmed that in many cases genetic algorithms can outperform constrained random generation of stimuli, that is employed in the classical way of doing verification, considering coverage fulfillment level per verification test.

show abstract

“…Ultrasonic sensors are used by Balushi et al [14] to control street lights so that they only turn on when a car passes by. And many more studies that apply smart lamp in indoor and outdoor such as studies [15][16][17]. In this study, the authors focus more on smart homes using smart lights that can adjust lights manually or automatically through an application on a smartphone.…”

Section: Smart Lightmentioning

confidence: 99%

Smart Lamp Using Google Firebase as Realtime Database

Sung¹,

Devi²,

Hsiao³

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Along with modernization in Indonesia, electricity users often do not realize that electrical energy is still used from electronic devices left on and unused. Much electrical energy is wasted due to unwise use. Modernization requires creative automation. This significantly minimizes the amount of human labor needed to complete the job. Energy efficiency is critical due to environmental concerns and limited research on alternative renewable energy sources. When evaluating the impact of technology on the environment, energy is an essential factor to consider. Most big cities and provinces in Indonesia still use conventional lighting systems where electricity users manually turn on and off lights. Current lighting systems combine compact fluorescent lamps (CFLs) with integrated high-pressure sodium lamps to provide illumination. However, average utilization generates high demand, and more than 40% of the total energy generates waste electricity. That's why the smart lamp is one of the technologies that people are looking for to innovate on existing technology to realize the innovative home concept in the smart living era. In this report, the author will discuss the manufacture of smart lamps dedicated to the 315 laboratories as learning materials for the future. In designing this smart lamp, the author uses firebase by google as the network layer. Many researchers use other types of cloud bases, but this time the author tends to prefer firebase because of the advantages of the cloud system by Google.

show abstract

An Indoor Smart Lamp For Environments Illuminated Day Time

Cited by 4 publications

References 7 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

Coverage Fulfillment Automation in Hardware Functional Verification Using Genetic Algorithms

Smart Lamp Using Google Firebase as Realtime Database

Contact Info

Product

Resources

About