Gaussian process regression based optimal design of combustion systems using flame images

Chen, Junghui; Chan, Lester Lik Teck; Cheng, Yi‐Cheng

doi:10.1016/j.apenergy.2013.04.036

Cited by 44 publications

(30 citation statements)

References 22 publications

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“…Li et al [9] extracted a set of heterogeneous features such as color, global and local features of the flame image to predict the burning state of rotary kiln combustion. Chen et al [10] utilized the principal component analysis (PCA) to obtain the principal components or combinational variables of flame images, which are then used to describe the important variations of the oxygen content in the combustion process. Although PCA is recognized as an effective reduction method of data dimensionality, the linear transformation can cause lower accuracy [11].…”

Section: Introductionmentioning

confidence: 99%

“…More importantly, the GPC is a probabilistic model that provides probabilistic outputs which are valuable to recognize the changes of the combustion state. The Gaussian process regression has been applied for predicting the oxygen content [10] and equivalence ratio estimation [32] through flame imaging. However, the GPC is rarely adopted for classification purposes such as combustion state prediction, so it is worth further exploration.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of combustion state through a semi-supervised learning model and flame imaging

Han

Zhang

et al. 2021

Fuel

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show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of combustion state through a semi-supervised learning model and flame imaging

Han

Zhang

et al. 2021

Fuel

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“…González-Cencerrado et al [15] worked with images obtained by a CCD camera in a swirl-stabilized, semi-industrial scale burner for biomass processing with pulverized coal and evaluated the influence of the air-fuel ratio on the structure and stability of the flame. Chen et al [16] applied principal component analysis to flame images by the combustion process developed at a heavy oil burner. To apply PCA algorithm, each RGB image was previously represented as a matrix line to form one big matrix with every image information in it.…”

Section: Introductionmentioning

confidence: 99%

On the dynamics of flame images identified through computer vision and modal methods

Chui

Neto

Trigo

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Combustion processes in industrial furnaces pose challenging difficulties when one attempts to model flame. Chemical and physical phenomena interact and affect flame behavior in such a complex way that several different approaches have been under development to tackle the modeling problem. Flame instabilities could potentially harm safety operation of the furnace. Ultimate goal of flame control is to develop means to actuate over the flame state in order to avoid dangerous situations. A critical step towards this objective is to come up with a reliable model for the flame, so that control theory could be applied to automatically maintain flame state within safe standards. This work proposes a methodology to model the dynamics of flames through computer vision to acquire and process flame images in order to extract image features evolution in time. Then, a dynamic model is identified through random decrement algorithm and Ibrahim time domain method, which are operational modal analysis techniques, together with a random contribution. Flames from seven different combustion conditions were modeled by such methodology, and their estimated values were compared with experimental data for validation. Results show that estimated and experimental data possess a high degree of correlation, thus confirming the viability of the proposed dynamic model of flames.

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“…The computer-simulated image becomes increasingly clear and close to the real world [3,4]. However, when applying NRAP to image processing in modern product design, two major problems are faced: the computational complexity of image processing is too large and the storage of measurement matrices is huge [5]. The computational complexity of problematic image processing is too large.…”

Section: Introductionmentioning

confidence: 99%

Imagery non-photorealistic art processing in modern product design relying on an inline block optimization hierarchy tree algorithm

2018

J Image Video Proc.

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In view of the high complexity of image arts processing in modern product design, this paper proposes an image non-realistic art processing method in modern product design that relies on an inline block optimization hierarchical tree algorithm. This method is adopted in modern product design. In the introduction of a nested block optimization hierarchical tree algorithm, according to the different image block level classification, the statistical autocorrelation function matrix is estimated, followed by the non-realistic art processing of each image block in the product design. Because the algorithm uses an inline block optimization hierarchical tree algorithm, this avoids the non-linear iterative phenomenon in the process of traditional product design image processing, and greatly shortens the image processing time. The simulation experiment results show that the image processing quality of the proposed algorithm is not affected due to the reduced computational complexity for the image with less complex texture details. The reduction of image processing quality affects the image processing algorithms used in modern product design.

show abstract

Gaussian process regression based optimal design of combustion systems using flame images

Cited by 44 publications

References 22 publications

Prediction of combustion state through a semi-supervised learning model and flame imaging

Prediction of combustion state through a semi-supervised learning model and flame imaging

On the dynamics of flame images identified through computer vision and modal methods

Imagery non-photorealistic art processing in modern product design relying on an inline block optimization hierarchy tree algorithm

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