Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery

Neagoe, Victor-Emil; Chirila-Berbentea, Vlad

doi:10.1109/igarss.2016.7729792

Cited by 29 publications

(13 citation statements)

References 6 publications

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“…The study of improved gaussian mixture model with expectationmaximiziation for clustering of remote sensing imagery had been done by Neagoe et. al [22]. In this study, author had reviewed the performance of kmean clustering method and Gaussian Mixture Model with expectationmaximization clustering method.…”

Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

Johnathan¹,

Moh²

2021

International Journal of Innovative Research in Electrical, Ele

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Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

Johnathan¹,

Moh²

2021

International Journal of Innovative Research in Electrical, Ele

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“…The GMM has been widely used in the fields of pattern recognition, information processing and data mining [19,20]. The probability density function of the GMM is given by…”

Section: Gmmmentioning

confidence: 99%

A multi-target tracking algorithm based on Gaussian mixture model

Sun

Cao

et al. 2020

J. of Syst. Eng. Electron.

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Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm, this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

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“…In this paper, the K-means algorithm is used to initialize the EM [43]. Besides, the number of the single Gaussian probability density functions M is another important parameter of GMM.…”

Section: Initialization and Determination Of Hybrid Number Of Gmmmentioning

confidence: 99%

A Health Status-Based Performance Evaluation Method of Photovoltaic System

Ding

Zhang

et al. 2019

IEEE Access

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The large scale application of the photovoltaic (PV) systems is significantly beneficial to the mitigation of energy crisis. The quality and performance of PV systems directly influence the energy yield. It is necessary to establish more scientific and effective methods to maximize the energy yield. In this paper, the concept of health status is proposed to describe the performance of PV systems within a certain period of time. A health status based performance evaluation model is built by the Gaussian mixture models (GMM) and the empirical mode decomposition (EMD). Then, the health index (HI) of PV array is defined. Under the outdoor ambient conditions, the proposed model can sensitively detect the slight performance reduction caused by faults or partial shadings of the PV array in real-time. At last, the proposed method is verified by the simulations and experiments. Experimental results show that on the sunny day the average daily HI is 1.2 and the average performance ratio (PR) is 0.85, which both show the PV array is healthy. When one of the PV modules in the PV array is partially shaded, the PR is still approximate 0.9. However, the calculated HI is greater than the threshold and the fault is reported. The results indicate that the proposed method can sensitively identify pseudo health status that cannot be identified by the PR. The proposed evaluation method based on health status provides an alternative option to assess the performance of the PV systems. Combining with the PR, the comprehensive performance of the PV systems can be reflected more accurately.

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Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery

Cited by 29 publications

References 6 publications

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

A multi-target tracking algorithm based on Gaussian mixture model

A Health Status-Based Performance Evaluation Method of Photovoltaic System

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