• Journal of Adhesion and Interface
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• v.9 no.1
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• pp.3-8
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• 2008

Self-sensing was evaluated for carbon nanofiber (CNF)/epoxy composites with two different aspect ratios via electro-micromechanical technique and wettability test. Volumetric electrical resistance was measured to evaluate the comparative dispersion degree indirectly and it decreased due to the increase of electric contacts with increasing CNF concentration. The dispersion degree was evaluated indirectly by calculating coefficient of variation (COV) of volumetric electrical resistance. The CNF type A with a high aspect ratio showed better self-sensing than the case of CNF type B with a short aspect ratio. The CNF type B/epoxy composite showed little self-sensing at a concentration higher than 2 vol% probably due to poor dispersion. The apparent modulus of CNF type B was higher than that of CNF type A due to the orientation effect and the high surface area. The thermodynamic work of adhesion was consistent with the result of apparent modulus.

• Journal of the Korean Geotechnical Society
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• v.26 no.12
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• pp.71-79
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• 2010

The reliable evaluation of the coefficient of variation (COV) of soil properties is required for the determination of adequate design values and the application of a probabilistic method for the design of geotechnical structures. In this paper, the applicability of methods for estimating the standard deviation, such as the. Three-Sigma Rule and a statistical method, is evaluated by using site investigation data of the Songdo area. It is found that the Three-Sigma Rule provides similar results to those of a statistical method when using $N_{\sigma}$=6 for the property with small variability and $N_{\sigma}$=4.2~5.3 for the property with large variability. It is also observed that, for the undrained shear strength that has an increasing trend with depth, a $N_{\sigma}$ value of 4 is adequate for the evaluation of the variability by the Three-Sigma Rule. The COVs of soil properties determined in this paper could be used in the estimation of the confidence interval and characteristic values of soil properties.

• Journal of the Korean Society for Advanced Composite Structures
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• v.1 no.2
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• pp.29-35
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• 2010

In addition to the Young's modulus, the Poisson's ratio is also at the center of attention in the field stochastic finite element analysis since the parameters play an important role in determining structural behavior. Accordingly, the sole effect of this parameter on the response variability is of importance from the perspective of estimation of uncertain response. To this end, a formulation to determine the response variability in laminate composite plates due to the spatial randomness of Poisson's ratio is suggested. The independent contributions of random Poisson's ratiocan be captured in terms of sub-matrices which include the effect of the random parameter in the same order, which can be attained by using the Taylor's series expansion about the mean of the parameter. In order to validate the adequacy of the proposed formulation, several example analyses are performed, and then the results are compared with Monte Carlo simulation (MCS). A good agreement between the suggested scheme and MCS is observed showing the adequacy of the scheme.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.85-92
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• 2007

As the use of drilled shafts for foundation of a large size structure increases, the evaluation of the reliable bearing capacity of the pile has become important. The purpose of this study is to verify the reliability of bearing capacity equations for drilled shafts socketed in weathered rock by comparing the bearing capacity values from static load tests with values from bearing capacity equations. In this study, twelve data from static load test were selected from four field sites, and the data of load test and the properties of weathered rock were analyzed. Three methods widely used in practice were selected for analysis, namely the AASHTO method (1996), Carter & Kulhawy method (1988), and FHWA method (1999). The comparison of the bearing capacity values from the bearing capacity equations to those obtained from load tests showed that the Carter & Kulhawy method (1988) was the most reliable in giving conservative design values and smaller COV (Coefficient Of Variation).

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.249-259
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• 2020

Design criteria for limit state design of underground structures have already been published overseas, and research has been conducted to revise the design method in Korea. In order to estimate the probability of failure under seismic load, the probability variable should be considered in the reliability analysis. In this study, the failure probability of the existing shield tunnel segment lining design was calculated by applying the coefficient of variation (COV) for the earth pressure and the seismic load effect in consideration of the statistical characteristics of the domestic ground properties. Based on the results of calculating the reliability index (β) from the calculated probability of failure and analyzing the reliability index according to the change in the load factor and the results of domestic and foreign research, the target reliability index (β_T) during earthquakes of shield tunnel segment lining is analyzed to be "2.3", it was proposed as the target reliability index for the design of the limit state under seismic load.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.2
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• pp.218-227
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• 2014

In this paper, it performed to the elastic-plastic large deflection series analysis using the experimental model and predicted a failure mode and ultimate strength. The collapse mode of numerical analysis model is formed a plastic hinge on loaded flange and consistent with the collapse mode of experimental model. Also, The yield line is formed in the web could observed that have occurred the crippling collapse mode and the ultimate loads of the experimental model and numerical analysis model have maintained linearly Means 1.07, Standard deviation 0.04, Coefficient of variation(COV) 0.04 and the result of ultimate loads have appeared approximately 8% error rate. it was found that very satisfied to the experimental results and the applied rules. if it is considered to be maintain a reasonable safety level, it is possible to predict the failure modes of aluminium alloy plate girders and ultimate loads.

• Journal of the Korea Concrete Institute
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• v.20 no.2
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• pp.139-146
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• 2008

The characteristics of the biaxial flexural tensile strength of concretes was compared to that of the uniaxial strength. The uniaxial and biaxial strengths in this study were obtained from the classical modulus of rupture test and the biaxial flexural test recently developed by Zi and Oh and Zi et al., respectively. Three different sizes were considered to investigate the effect of the size of aggregates. To estimate the stochastic aspect of the strength, 32 specimens were used for each test. The average biaxial flexural fracture strength was about 20% greater than the uniaxial test. At the same time, the coefficient of variation for the biaxial test was 18% greater than the uniaxial test. This means that the probability of the biaxial cracking can be greater than the uniaxial cracking.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.709-715
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• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.39-46
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• 2020

Carbonation is a deterioration which degrades structural and material performance by permitting CO₂ and corrosion of embedded steel. Service life evaluation through deterministic method is conventional, however the researches with probabilistic approach on service life considering loading and cold joint effect on carbonation have been performed very limitedly. In this study, probabilistic service life evaluation was carried out through MCS (Monte Carlo Simulation) which adopted random variables such as cover depth, CO₂ diffusion coefficient, exterior CO₂ concentration, and internal carbonatable materials. Probabilistic service life was derived by changing mean value and COV (Coefficient of variation) from 100 % to 300 % and 0.1 ~ 0.2, respectively. From the analysis, maximum reduction ratio (47.7%) and minimum reduction ratio (11.4%) of service life were obtained in cover depth and diffusion coefficient, respectively. In the loading conditions of 30~60% for compressive and tensile stress, GGBFS concrete was effective to reduce cold joint effect on carbonation. In the tensile condition, service life decreased linearly regardless of material types. Additionally service life rapidly decreased due to micro crack propagation in the all cases when 60% loading was considered in compressive condition.