• International conference on construction engineering and project management
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• 2015.10a
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• pp.597-598
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• 2015

In order to improve the reliability of cost estimation results using CBR, there has been a continuous issue on similarity measurement to accurately compute the distance among attributes and cases to retrieve the most similar singular or plural cases. However, these existing similarity measures have limitations in taking the covariance among attributes into consideration and reflecting the effects of covariance in computation of distances among attributes. To deal with this challenging issue, this research examines the weighted Mahalanobis distance based similarity measure applied to CBR cost estimation and carries out the comparative study on the existing distance measurement methods of CBR. To validate the suggest CBR cost model, leave-one-out cross validation (LOOCV) using two different sets of simulation data are carried out. Consequently, this research is expected to provide an analysis of covariance effects in similarity measurement and a basis for further research on the fundamentals of case retrieval.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• Journal of the Korean Society of Groundwater Environment
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• v.1 no.2
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• pp.90-99
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• 1994

Heterogeneous hydraulic conductivity in a flow domain is generated under the assumption that it is a random variable with a lognormal, spatially-correlated distribution. The hydraulic head and the conductivity in a groundwater flow system are represented as a stochastic process. The method of Monte Carlo Simulation (MCS) and the finite element method (FEM) are used to determine the statistics of the head and the logconductivity. The second moments of the head and the logconductivity indicate that the cross-covariance of the logconductivity with the head has characteristic distribution patterns depending on the properties of sources, boundary conditions, head gradients, and correlation scales. The negative cross-correlation outlines a weak-response zone where the flow system is weakly responding to a stress change in the flow domain. The stochastic approach has a potential to quantitatively delineate the zone of influence through computations of the cross-covariance distribution.

• Communications of the Korean Mathematical Society
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• v.16 no.1
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• pp.125-135
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• 2001

In this article we investigate the dependence between components of the random vector which is given as an asymptotic limit of an array of random vectors with interlaced mixing conditions. We discuss the cross covariance of the limiting vector process and give a stronger condition to have a central limit theorem for an array of random vectors with mixing conditions.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.10a
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• pp.88-92
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• 1983

The performance of noncoherent fast frequency-hopped spread spectrum multiple access communication systems with square-law combining over fading channel is presented. The expression for the probalility of error as a performance measure is derived by means of momenting function of the dedecision variables, and the cross-covariance of the fading process the ambiguty function of the transmitted signals.

• Nuclear Engineering and Technology
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• v.28 no.1
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• pp.72-78
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• 1996

A computer program COVAFIT has been developed and applied to the evaluation of experimental cross sections for MeV energy incident particles. The program utilizes weighted least-square linear regression method with high-order polynomials derived in this study. Meeting the growing demand for the treatment of covariances in nuclear data, it deals with the variance and covariance data provided along with experimental cross sections and yields those for the evaluated ones. The evaluated results on two sets of neutron total cross section of oxygen and three sets of proton cross section for $C^{11}$ production reactions confirm the methodology formulated in and the applicability of the program.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.229-237
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• 2007

In this study, an artificial neural network (ANN)-based damage detection algorithm using acceleration signals is developed for real-time alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed tot damage detection in real time. The cross-covariance of two acceleration-signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained lot potential loading Patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.523-531
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• 2022

In order to study the sensitivity and the uncertainty of the Moroccan research reactor TRIGA Mark II, a model of this reactor has been developed in our ERSN laboratory for use with the N-Particle MCNP Monte Carlo transport codes (version 6). In this article, the sensitivities of the effective multiplication factor of this reactor are evaluated using the ENDF/B-VII.0, ENDF/B-VII.1 and JENDL-4.0 libraries and in 44 energy groups, for the cross sections of the fuel (U-235 and U-238) and the moderator (H-1 and O-16). However, the quantification of the uncertainty of the nuclear data is performed using the nuclear code NJOY99 for the generation and processing of covariance matrices. On the one hand, the highest uncertainty deviations, calculated using the ENDFB-VII.1 and JENDL4.0 evaluations, are 2275, 386 and 330 pcm respectively for the reactions U₂₃₅(n, f), $ U_{235}(n\bar{\nu})$ and H₁(n, γ). On the other hand, these differences are very small for the neutron reactions of O-16 and U-238. Regarding the neutron spectra, in CT-mid plane, they are very close for the three evaluations (ENDF/B-VII.0, ENDF/B-VII.1 and JENDL-4.0). These spectra present two peaks (thermal and fission) around the energies 0.05 eV and 1 MeV.

• Smart Structures and Systems
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• v.32 no.1
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• pp.9-21
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• 2023

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a six-story concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.

• Journal of the Korean earth science society
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• v.23 no.1
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• pp.15-29
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• 2002

Bayesian inversion for gravity and resistivity data was performed to investigate the cavity structure appearing as a lava tunnel in Cheju Island, Korea. Dipole-dipole DC resistivity data were proposed for a prior information of gravity data and we applied the geostatistical techniques such as kriging and simulation algorithms to provide a prior model information and covariance matrix in data domain. The inverted resistivity section gave the indicator variogram modeling for each threshold and it provided spatial uncertainty to give a prior PDF by sequential indicator simulations. We also presented a more objective way to make data covariance matrix that reflects the state of the achieved field data by geostatistical technique, cross-validation. Then Gaussian approximation was adopted for the inference of characteristics of the marginal distributions of model parameters and Broyden update for simple calculation of sensitivity matrix and SVD was applied. Generally cavity investigation by geophysical exploration is difficult and success is hard to be achieved. However, this exotic multiple interpretations showed remarkable improvement and stability for interpretation when compared to data-fit alone results, and suggested the possibility of diverse application for Bayesian inversion in geophysical inverse problem.