A Comparison Between Different Gaussian-Based Mixture Models

Najar, Fatma; Bourouis, Sami; Bouguila, Nizar; Belghith, Safya

doi:10.1109/aiccsa.2017.108

Cited by 47 publications

(14 citation statements)

References 12 publications

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“…Through the known wind power output observation sequence, the hidden state transition matrix in the HMM and the probability distribution between hidden state variables and observation variables are learned. Model parameters include the initial state probability vector π, state transition probability matrix A and joint probability distribution of observation vector B [30]. Furthermore, the hidden state variable of wind power output at each time is represented by discrete variable i t , and the observed variable at each time is represented by the output vector of multiple wind farms o t .…”

Section: Correlated Output Time Series Model Of Multi-wind Farms Based On the Gmm-hmmmentioning

confidence: 99%

GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

Niu

et al. 2021

IEEE Access

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Medium-and long-term wind farm output scenarios are usually required for power system planning. However, with the traditional medium-and long-term wind power output time series generation method, it is difficult to simultaneously take into account the spatiotemporal correlation of multiple wind farms, leading to the failure of the output sequence to reflect their medium-and long-term statistical characteristics. Hidden Markov model (HMM) can simultaneously consider the temporal and spatial correlation of multiple wind farms, but the existing HMM method is not accurate in describing the output distribution of multiple wind farms, which leads to a large error between the model output and the actual wind power output. Therefore, this paper proposes a method for generating medium-and long-term correlated output time series of multiple wind farms based on the Gaussians mixture Hidden Markov model (GMM-HMM). The discrete state variable in the hidden Markov model is used to describe the meteorological state. The Markov chain between discrete state variables is used to describe the temporal correlation of wind power output. The wind power output vector of multiple wind farms is used as the observation variable, and the mixed Gaussian probability distribution mapping relationship between the state variable and the multidimensional wind power output vector is established. Based on the Monte Carlo sampling method, the multi-wind farm output series satisfying the spatiotemporal correlation of historical output series are generated monthly. In the calculation example, the monthly wind power output series generated by five wind farms in Jilin Province are analyzed. The results show that the main statistical characteristics of the multi-wind power output time series generated by the proposed method are generally superior to those obtained with the traditional wind power output modeling method, which proves the superiority of the proposed method. INDEX TERMSMultiple wind farms, spatiotemporal correlation, Gaussians mixture Hidden Markov model (GMM-HMM), time series generation NOMENCLATURE Most of the symbols and notations used throughout this paper are defined below for quick reference.

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Section: Correlated Output Time Series Model Of Multi-wind Farms Based On the Gmm-hmmmentioning

confidence: 99%

GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

Niu

et al. 2021

IEEE Access

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“…In WSNs, real-time implementation in the energy limited sensor node requires a compact and efficient classifier [28]. We evaluate the proposed method for moving vehicles classification by comparing with the baseline method, and perform experiments in the framework with the popular classifier Gaussian mixture model (GMM) [17], [29], [30]. In our experiment, we adopt the target detection algorithm presented in [31].…”

Section: B Experimentsmentioning

confidence: 99%

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

Zhao

Guo

et al. 2019

IEEE Access

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The acoustic classification technology of moving vehicles is a significant application in wireless sensor networks (WSNs). However, the wild environment makes it more intractable for the acoustic target classification owing to the complicated interference noise. The widely used mel-frequency cepstral coefficients (MFCCs) are somewhile contaminated by the acoustic noise, resulting in degrading the classification performance in real environments. To increase the classification accuracy of the moving vehicles, this paper presents an acoustic feature extraction method, which integrates the nonlinear function with the MFCC method, termed as NMFCC. Through nonlinear transformation, the proposed method could amplify the acoustic features in a nonlinear way, and obtain more robust acoustic features, thereby achieving a superior classification performance. Comparing the NMFCC with the baseline feature extraction method, experimental results not only demonstrate the viability of the proposed algorithm but also show a satisfactory performance of the moving vehicle classification for diverse practical scenarios in the wild. INDEX TERMS Acoustic feature extraction, moving target classification, nonlinear function, mel-frequency cepstral coefficients, microphone array.

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“…We used a Gaussian mixture model (GMM using the Mclust R software package to determine discrete groupings of frequently performed turn angles by animals under all test conditions. GMMs allow for an unbiased and accurate model-based approach to estimate the density of data by fitting multiple gaussian components that describe the total probability density estimate (PDE, Najar et al, 2017). We used the PDE to identify distinct turn angle categories using the components of each gaussian fit.…”

Section: Turn Angle Distribution and Criteriamentioning

confidence: 99%

Unbiased analysis of C. elegans behavior reveals the use of distinct turning strategies during magnetic Orientation

Bainbridge

McDonald

Ahlert

et al. 2019

Preprint

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To successfully navigate their surroundings, animals detect and orient to environmental stimuli possessing unique physical properties. Most animals can derive directional information from spatial or temporal changes in stimulus intensity (e.g. chemo- and thermo-taxis). However, some biologically relevant stimuli have constant intensity at most organismal scales. The gravitational and magnetic fields of the earth are examples of uniform stimuli that remain constant at most relevant scales. While devoid of information associated with intensity changes, the vectorial nature of these fields intrinsically encodes directional information. While much is known about behavioral strategies that exploit changes in stimulus intensity (gradients), less is understood about orientation to uniform stimuli. Nowhere is this truer than with magnetic orientation. While many organisms are known to orient to the magnetic field of the earth, how these animals extract information from the earth’s magnetic field remains unresolved.Here we use the nematode C. elegans to investigate behavioral strategies for orientation to magnetic fields, and compare our findings to the better characterized chemical and thermal orientation strategies. We used an unbiased cluster analysis to categorize, quantify, and compare behavioral components underlying different orientation strategies as a way to quantify and compare animal orientation to distinct stimuli. We find that in the presence of an earth-like magnetic field, worms perform acute angle turns (140-171°) that significantly improved their alignment with the direction of an imposed magnetic vector. In contrast, animals performed high amplitude turns (46-82°) that significantly increased alignment of their trajectory with the preferred migratory angle. We conclude that C. elegans orients to earth-strength magnetic fields using two independent behavioral strategies, in contrast to orientation strategies to graded stimuli. Understanding how C. elegans detects and orients to magnetic fields will provide useful insight into how many species across taxa accomplish this fascinating sensory feat.

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A Comparison Between Different Gaussian-Based Mixture Models

Cited by 47 publications

References 12 publications

GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

Unbiased analysis of C. elegans behavior reveals the use of distinct turning strategies during magnetic Orientation

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