A new PSO-based approach to fire flame detection using K-Medoids clustering

Khatami, Amin; Mirghasemi, Saeed; Khosravi, Abbas; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1016/j.eswa.2016.09.021

Cited by 90 publications

(47 citation statements)

References 16 publications

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“…To overcome these disadvantages, researchers have also established their flame chromatic models in YCbCr [5] and HSV [6] spaces to relieve the influence of luminance [13]. Alternatively, the color features are transformed to a new space with a conversion matrix trained by the particle swarm optimization (PSO) with both flame and non-flame pixels, to enhance the classification performance [14]. However, the colors of non-flame pixels are in a very wide range and not easy to be covered by training data.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-This paper proposes a flame detection framework based on the color, dynamics and flickering properties of flames. The distribution of flame colors is modelled by a Gaussian Mixture Model whose number of Gaussian component is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet Process Gaussian Mixture Model based approach for autonomous flame detection is tested on various videos and achieves frame-wise accuracy higher than 95%.

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Section: Introductionmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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show abstract

“…Experiments show that both the proposed model and the algorithm are feasible. Khatami et al [28] constructed a PSO for flame detection, which enabled image detection techniques for flame detection. PSO can distinguish the different colors, and the color conversion matrix can detect different color flames.…”

Section: Particle Swarm Optimization Based Methodsmentioning

confidence: 99%

Research on the Scheduling Problem of Movie Scenes

Liu

Sun

Zhang

et al. 2019

Discrete Dynamics in Nature and Society

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The scheduling problem of movie scenes refers to the reasonable ordering of the shooting sequence of the scene, thereby minimizing the total cost of the movie scenes. This paper considers the factors affecting the cost of movie scenes shooting in the real world and constructs an integer linear programming model. A tabu search based method (TSBM) and a particle swarm optimization based method (PSOBM) are designed to solve larger-scale problems. Numerical experiments show that both TSBM and PSOBM are suitable for solving small-scale movie scenes scheduling problem. Moreover, through the comparison experiments between TSBM and PSOBM, it is verified that the TSBM can effectively solve the large-scale movie scenes scheduling problem.

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“…To verify the effectiveness of the proposed method further, the features extracted by the proposed method and the 4 comparative methods were input into clustering algorithm, respectively, for fault classification. According to [41], K-medoids clustering is more robust to noise and outliers than K-means clustering. erefore, it is applied in this paper.…”

Section: Shock and Vibrationmentioning

confidence: 99%

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Lü

Zhang²,

Zhang

et al. 2019

Shock and Vibration

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Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. en, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. e feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. e results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.

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A new PSO-based approach to fire flame detection using K-Medoids clustering

Cited by 90 publications

References 16 publications

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Research on the Scheduling Problem of Movie Scenes

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

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