Search Results - (( probable estimation methods algorithm ) OR ( probable detection method algorithm ))
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Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. …”
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2
Fault section detection and location on distribution network using analytical voltage sags database
Published 2006“…By doing this all the possible sections due to the fault can be selected. Finally, the most probable faulty section is identified using probability approach.This paper presents the implemented algorithms and the test of the algorithms on typical distribution networks. …”
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3
Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…The existence of outliers in circular-circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. …”
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4
Improved robust estimator and clustering procedures for multivariate outliers detection
Published 2023“…Three measurements are used to assess the effectiveness of the proposed robust estimator and clustering procedure, which are the probability that all the outliers are successfully detected (pout), the probability that the outliers are falsely detected as inliers (pmask), and the probability of inliers detected as outliers (pswamp). …”
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5
GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE
Published 2010“…From the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering
Published 2011“…This is achieved by a pre-estimation using fuzzy clustering that provides a prior knowledge and forms a rough model to be fine tuned using the least square method. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…Lastly, we consider the problem of detecting multiple outliers in circular regression models based on the clustering algorithm. …”
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9
Robust estimation methods for fixed effect panel data model having block-concentrated outliers
Published 2019“…The Ordinary Least Squares (OLS) is the commonly used method to estimate the parameters of fixed effect panel data model. …”
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10
Forecasting Tourism Market for Fiji based on Indicator Approach
Published 2019“…Apart from utilising the composite leading indicator approach, a filtering extraction method, a dating algorithm for turning point detection and directional accuracy and binomial tests, this study also included Markov regime switching as well, for identifying the transition probabilities. …”
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11
Robust Kernel Density Function Estimation
Published 2010“…The classical kernel density estimation technique is the commonly used method to estimate the density function. …”
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12
Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
Published 2006“…A gain-based correction method; probability density function model are used to cluster white and gray matters, cerebrospinal fluid, and meninges. …”
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13
Engine fault diagnosis using probabilistic neural network
Published 2021“…The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. …”
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14
Human tracking in video surveillance using particle filter
Published 2016“…The particle filter on the other hand is a tool for estimating the posterior probability density of state of a dynamic model that includes non-linear and non-Gaussian real world applications. …”
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15
Spectrum averaging in a MIMO FMCW maritime radar for a small fluctuating target range estimation
Published 2022“…This paper provides an alternative detection method for a small fluctuating target by deploying a frequency modulated continuous waveform (FMCW) in a multiple-input multiple-output (MIMO) configuration. …”
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CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS
Published 2011“…Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. …”
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17
Identification and mitigation of non-line-of-sight path effect using repeater for hybrid ultra-wideband positioning and networking system
Published 2021“…From the simulation results, the NLOS paths have been successfully identified under the NLOS environment. Hence, a higher probability of accuracy across the entire tested area can be achieved for the UWB positioning system by mitigating the NLOS path effect using the proposed algorithm. …”
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Microcrack Detection And Noise Reduction In Integrated Circuit Packages
Published 2018“…Three algorithms are tested and evaluated in terms of microcrack detection and noise reduction which are probability based thresholding, histogram equalization, and modified Perona-Malik’s anisotropic diffusion methods. …”
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Population genetic structure of Malayan Tapir (Tapirus indicus Desmarest) in Peninsular Malaysia
Published 2019“…Using K-means clustering algorithm, five clusters were inferred among the wild samples (N = 57), which showed a complex population structure probably comprising multiple continuous populations that also experiencing considerably restricted gene flow due to isolation by geographical barriers especially mountain ranges. …”
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20
Simulation algorithm of bayesian approach for choice-conjoint model
Published 2011“…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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