• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.3
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• pp.85-90
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• 1988

A probabilistic model for the failure in a homogeneous soil slope is presented. The Safety of the slope is measured through its probability of failure rather than the customary factor of safety. The safety margin of slope failure is assumed to follow a normal distribution. Sources of uncertainties affecting characterization of soil property in a homogeneous soil layer include inherent spatial variability., estimation error from insufficient samples, and measurement errors. Uncertainties of the shear strength-along potential failure surface are expressed by one-dimensional random field models. The rupture surface, created at toe of a soil slope, has been considered to propagate towards the boundary along a path following an exponential (log-spiral) law. Having derived the statistical characteristics of the rupture surface and of the forces which act along it, the probability of failure of the slope was found. Finally the developed procedure has been applied in a case study to yield the reliability of a soil slope.

• Journal of the Korean Vacuum Society
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• v.7 no.s1
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• pp.171-178
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• 1998

The microstructure and properties of plasma-sprayed coatings depend on a great number of spraying parameters, random factors, which lead to vibration in these spraying parameters, may in some degree influence the microstructure and properties of the coatings. Therefore, the property values appear certain distributions, and the description and comparison of the properties of plasma-sprayed coatings should be performed employing statistical analysis. In this paper, $Cr_3C_2$-Nicr coatings of different thickness were sprayed onto stainless steel using atmosphere plasma system and adopting three kinds of gun translation speeds. Then the microhardness measurements were performed on polished surface of the coatings. Forty readings were taken and statistically analyzed by calculating the characteristic values, estimating and comparing the means, and assessing whether they belonged to the Normal or Weibull Distribution. This study has found that statistical analysis could discriminate influence of spraying parameters and coating design on microhardness of the $Cr_3C_2$-Nicr coatings from random vibration, which showed that the microharness of the $Cr_3C_2$-Nicr coatings were related to gun translation speed coating thickness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.1
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• pp.73-84
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• 1986

A statistical method is proposed which estimates an edge that is one of the basic features in image understanding. The conventional edge detection techniques are performed well for a deterministic singnal, but are not satisfactory for a statistical signal. In this paper, we use the likelihood function which takes account of the statistical property of a signal, and derive the decision function from it. We propose the maximum likelihood edge detection technique which estimates an edge point which maximizes the decision function mentioned above. We apply this technique to statistecal signals which are generated by using the random number generator. Simnulations show that the statistical edge detection technique gives satisfactory results. This technique is extended to the two-dimensional image and edges are found with a good accuracy.

• Computers and Concrete
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• v.4 no.5
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• pp.347-362
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• 2007

Due to heterogeneities at all scales, concrete exhibits significant variability in mechanical behavior from sample to sample. An understanding of the fundamental mechanical performance of concrete must therefore be embedded in a stochastic framework. The current work attempts to address the connection between a two-dimensional concrete mesostructure and the random local material properties associated within that mesostructure. This work builds on previous work that has focused on the random configuration of concrete mesostructures. This was accomplished by developing an understanding of the effects of variations in the mortar strength and the mortar-aggregate interfacial strength in given deterministic mesostructural configurations. The results are assessed through direct tension tests that are validated by comparing experimental results of two different, pre-arranged mesostructures, with the intent of isolating the effect of local variations in strength. Agreement is shown both in mechanical property values as well as the qualitative nature of crack initiation and propagation.

• Speech Sciences
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• v.10 no.1
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• pp.95-105
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• 2003

This paper describes a new parameter for voice activity detection which serves as a front-end part for automatic speech recognition systems. The new parameter called run-ratio is derived from the runs test statistic which is used in the statistical test for randomness of a given sequence. The run-ratio parameter has the property that the values of the parameter for the random sequence are about 1. To apply the run-ratio parameter into the voice activity detection method, it is assumed that the samples of an inputted audio signal should be converted to binary sequences of positive and negative values. Then, the silence region in the audio signal can be regarded as random sequences so that their values of the run-ratio would be about 1. The run-ratio for the voiced region has far lower values than 1 and for fricative sounds higher values than 1. Therefore, the parameter can discriminate speech signals from the background sounds by using the newly derived run-ratio parameter. The proposed voice activity detector outperformed the conventional energy-based detector in the sense of error mean and variance, small deviation from true speech boundaries, and low chance of missing real utterances

• Smart Structures and Systems
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• v.2 no.2
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.543-548
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• 2018

Ferroelectric $Bi_4Ti_3O_{12}$ films were deposited on $SrTiO_3(100)$ and Si(100) substrate by using conductive $SrRuO_3$ films as underlayer, and their ferroelectric and electrical properties were investigated depending on crystal structure and orientation. C-axis oriented $Bi_4Ti_3O_{12}$ films were grown on well lattice-matched pseudo-cubic $SrRuO_3$ films deposited on $SrTiO_3(100)$ substrate, while random-oriented polycrystalline $Bi_4Ti_3O_{12}$ films were grown on $SrRuO_3$ films deposited on Si(100) substrate. The random-oriented polycrystalline film showed a good ferroelectric hysteresis property with remanent polarization ($P_r$) of $9.4{\mu}C/cm^2$ and coercive field ($E_c$) of 84.9 kV/cm, while the c-axis oriented film showed $P_r=0.64{\mu}C/cm^2$ and $E_c=47kV/cm$ in polarizaion vs electric field curve. The c-axis oriented $Bi_4Ti_3O_{12}$ film showed a dielectric constant of about 150 and lower thickness dependence in dielectric constant compared to the random-oriented film. Furthermore, the c-axis oriented $Bi_4Ti_3O_{12}$ film showed leakage current lower than that of the polycrystalline film. The difference of ferroelectric properties in two films was explained from the viewpoint of depolarization effect due to orientation of spontaneous polarization and layered crystal structure of bismuth-base ferroelectric oxide.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.2
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• pp.80-87
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• 2020

As the Arctic sea ice decreases due to various reasons such as global warming, the demand for ships and offshore structures operating in the Arctic region is steadily increasing. In the case of sea ice, the anisotropy is caused by the uncertainty inside the material. For most of the research, nevertheless, estimating the ice load has been treated deterministically. With regard to this, in this paper, a four-point bending strength analysis of an ice specimen was attempted using a stochastic finite element method. First, spatial distribution of the material properties used in the yield criterion was assumed to be a multivariate Gaussian random field. After that, a direct method, which is a sort of stochastic finite element method, and a sensitivity method using the sensitivity of response for random variables were proposed for calculating the probabilistic distribution of ice specimen strength. A parametric study was conducted with different mean vectors and correlation lengths for each material property used in the above procedure. The calculation time was about ten seconds for the direct method and about three minutes for the sensitivity methods. As the cohesion and correlation length increased, the mean value of the critical load and the standard deviation increased. On the contrary, they decreased as the friction angle increased. Also, in all cases, the direct and sensitivity methods yielded very similar results.