Confidence Intervals for the Ratio of Variances of Delta-Gamma Distributions with Applications

Khooriphan, Wansiri; Niwitpong, Sa-Aat; Niwitpong, Suparat

doi:10.3390/axioms11120689

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Like Threshold1, Threshold2 can be determined using a confidence interval approach, ensuring that it captures the variability in texture features observed within crack areas with a certain level of confidence [24,25].…”

Section: Mathematical Analysis For Variance Thresholdmentioning

confidence: 99%

Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images

Shahsavarani,

Ibarra-Castanedo,

Lopez

et al. 2024

NDT

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Infrared and visible imaging play crucial roles in non-destructive testing, where accurate defect segmentation and detection are paramount. However, the scarcity of annotated training data or the limited number of data availability often poses a challenge. To address this, we propose an innovative framework tailored to the domain of infrared and visible imaging, integrating segmentation and detection tasks. The proposed approach eliminates the dependency on annotated defect data during training, enabling models to adapt to real-world scenarios with limited annotations. By utilizing super-pixel segmentation and texture analysis, the proposed method enhances the accuracy of defect detection. Concrete structures, globally subjected to aging and degradation, demand constant monitoring for structural health. Traditional manual crack detection methods are labor-intensive, necessitating automated systems. The proposed approach combines deep learning-based super-pixel segmentation with texture analysis, offering a solution for limited-defect-data situations. Utilizing convolutional neural networks (CNNs) for super-pixel segmentation and texture features for defect analysis, the proposed methodology improves the efficiency and accuracy of crack detection, especially in scenarios with limited labeled data or a limited number of data available. Evaluation on public benchmark datasets have validated the effectiveness of the proposed approach in detecting cracks in concrete structures.

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Section: Mathematical Analysis For Variance Thresholdmentioning

confidence: 99%

Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images

Shahsavarani,

Ibarra-Castanedo,

Lopez

et al. 2024

NDT

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show abstract

“…Several researchers have investigated rainfall dispersion in Thailand, such as Kumphon et al [3], Szyniszewska and Waylen [4], and Thodsan et al [5]. Additionally, studies on statistical inference for rainfall distribution in Thailand have been reported by Maneerat et al [6], Khooriphan et al [7], Yosboonruang et al [8,9], and Thangjai et al [10]. Previous research on statistical inference has primarily focused on measuring rainfall dispersion using variance and coefficient of variation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand

Yosboonruang

Niwitpong

2023

Symmetry

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The coefficient of quartile variation is a valuable measure used to assess data dispersion when it deviates from a normal distribution or displays skewness. In this study, we focus specifically on the delta-lognormal distribution. The lognormal distribution is characterized by its asymmetrical nature and comprises exclusively positive values. However, when these values undergo a logarithmic transformation, they conform to a symmetrical (or normal) distribution. Consequently, this research aims to establish confidence intervals for the difference between coefficients of quartile variation within lognormal distributions incorporating zero values. We employ the Bayesian, generalized confidence interval, and fiducial generalized confidence interval methods to construct these intervals, involving data simulation using RStudio software. We evaluate the performance of these methods based on coverage probabilities and average lengths. Our findings indicate that the Bayesian method, employing Jeffreys’ prior, performs well in low variability, while the generalized confidence interval method is more suitable for higher variability. Therefore, we recommend using both approaches to construct confidence intervals for the difference between the coefficients of the quartile variation in lognormal distributions that include zero values. Furthermore, we apply these methods to rainfall data in Thailand to illustrate their alignment with actual and simulated data.

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Confidence Intervals for the Difference and Ratio of Medians of the Delta-Lognormal Distribution

Janthasuwan,

Niwitpong,

Niwitpong

2023

Lobachevskii J Math

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Confidence Intervals for the Ratio of Variances of Delta-Gamma Distributions with Applications

Cited by 4 publications

References 21 publications

Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images

Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images

Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand

Confidence Intervals for the Difference and Ratio of Medians of the Delta-Lognormal Distribution

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