Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

V., J.-S. Botero; G., F.-E. López; B., J.-F. Vargas

doi:10.21307/ijssis-2017-817

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…Botero et al proposed a low-cost RGB sensor used to create a technique for classifying light sources and selecting an estimating model for CRI and CCT. 21 The findings reveal that the error was less than 3.6% when using a K-Nearest Neighbor classifier. The model estimation error was 1.2%, 0.09% and 1.8% for incandescent light sources, fluorescent light sources and LED sources, respectively.…”

Section: Introductionmentioning

confidence: 94%

Comparison and validation of neural network models to estimate LED spectral power distribution

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

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The spectral power distribution (SPD) is the true fingerprint of a light source and is mainly dependent on electrical and thermal loading. Both the photometric and colorimetric quantities are originally extracted from SPD. Therefore, the dynamic prediction of SPD for LED has become an important aspect to evaluate the performance of LED during its time of operation. Generally, the statistical models are used to predict SPD. However, the statistical model with more than two input makes the system complex and time demanding. Artificial Neural Network (ANN) models, on the other hand, can help with this problem. The major goal of this research is to improve the utility of ANN in lighting applications. This is demonstrated by various neural network (NN) structures referred as models 1, 2 and 3 with combinations of varied neurons and hidden layers (HLs) to forecast SPD for various electrical and thermal stress levels at zero hours. The results are compared and based on absolute prediction error (APE) set to 5%, model 1 is considered as the best model for the SPD prediction. In addition, the time-based SPD prediction with model 1 is investigated using temperature, wavelength and time as input parameters for the LED luminaire and is validated.

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Section: Introductionmentioning

confidence: 94%

Comparison and validation of neural network models to estimate LED spectral power distribution

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

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“…where X 1 and X 2 are the support vectors of each pixel, and γ is a hyperparameter [37]. Similar to the K-nearest neighborhood algorithm, the radial basis function has an advantage in decreasing the space complexity problem [38]. After classification, the remained ROIs are utilized to compute the accuracy and the kappa coefficient to verify the effectiveness of the classification method.…”

Section: Marine Oil Leak Detection and Numerical Simulation Model Setupmentioning

confidence: 99%

Offshore Petroleum Leaking Source Detection Method From Remote Sensing Data via Deep Reinforcement Learning With Knowledge Transfer

Wang

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A marine oil spill is an environmental pollution incident that generally has the attributes of a high speed, widespread, and long duration. It seriously threatens the marine ecological environment and related industries. It is vital to determine the source of the oil leakage so that it may be stopped and related hazards can be reduced. Oil spill accidents in the sea are generally located in offshore and navigation channels. With the rapid development of remote-sensing techniques, oil leak extraction using remote-sensing data has played an essential role in oil spill research. This paper proposes a Monte Carlobased Deep Q-Transfer-learning Network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remotesensing data are utilized to continuously monitor a marine oil spill on the surface. The Estuarine and Coastal Ocean Model (ECOM) is utilized to simulate a marine oil spill event. The Deep Q Network (DQN) method with offline transferred knowledge is then utilized to determine the marine oil spill source location. In an experiment, based on the Bohai oil spill incident on June 2, 2011, the effectiveness of the remote-sensing-based DQTN marine oil spill search algorithm is verified. The accuracy of the targeted oil spill point is up to 98.97%.

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“…Deviations in correlated color temperatures (CCTs) and illuminance levels were always smaller than 5% of their target values. Botero-Valencia et al [31] used low-cost RGB color sensors to classify light sources according to whether they are fluorescent, incandescent, or LED-based. Adopting a k-nearest neighbors approach, a high classification accuracy of more than 96% was reported for a sample of 54 different light sources commonly found in residential and commercial environments.…”

Section: Introductionmentioning

confidence: 99%

Tackling Heterogeneous Color Registration: Binning Color Sensors

Myland

Babilon

Khanh

2021

Sensors

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Intelligent systems for interior lighting strive to balance economical, ecological, and health-related needs. For this purpose, they rely on sensors to assess and respond to the current room conditions. With an augmented demand for more dedicated control, the number of sensors used in parallel increases considerably. In this context, the present work focuses on optical sensors with three spectral channels used to capture color-related information of the illumination conditions such as their chromaticities and correlated color temperatures. One major drawback of these devices, in particular with regard to intelligent lighting control, is that even same-type color sensors show production related differences in their color registration. Standard methods for color correction are either impractical for large-scale production or they result in large colorimetric errors. Therefore, this article shows the feasibility of a novel sensor binning approach using the sensor responses to a single white light source for cluster assignment. A cluster specific color correction is shown to significantly reduce the registered color differences for a selection of test stimuli to values in the range of 0.003–0.008 Δu′v′, which enables the wide use of such sensors in practice and, at the same time, requires minimal additional effort in sensor commissioning.

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Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

Cited by 10 publications

References 23 publications

Comparison and validation of neural network models to estimate LED spectral power distribution

Comparison and validation of neural network models to estimate LED spectral power distribution

Offshore Petroleum Leaking Source Detection Method From Remote Sensing Data via Deep Reinforcement Learning With Knowledge Transfer

Tackling Heterogeneous Color Registration: Binning Color Sensors

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