• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.676-681
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• 2008

The evaluation of the effluent water quality of sewage treatment plant is one of the most important factor in calculating total maximum daily loads (TMDLs). Current method to calculate 95% upper limit of effluent water quality of sewage treatment plant assuming normal distribution of data needs to be implemented in case of non-normal distribution. We have investigated the applicability of percentile ranking method as a non-parametric statistical analysis in case of non-normal distribution of data.

• Structural Engineering and Mechanics
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• v.64 no.5
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• pp.621-640
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• 2017

This paper reports the numerical investigation conducted to study the influence of Local-Distortional (L-D) interaction mode buckling on post buckling strength erosion in fixed ended lipped channel cold formed steel columns. This investigation comprises of 81 column sections with various geometries and yield stresses that are carefully chosen to cover wide range of strength related parametric ratios like (i) distortional to local critical buckling stress ratio ($0.91{\leq}F_{CRD}/F_{CRL}{\leq}4.05$) (ii) non dimensional local slenderness ratio ($0.88{\leq}{\lambda}_L{\leq}3.54$) (iii) non-dimensional distortional slenderness ratio ($0.68{\leq}{\lambda}_D{\leq}3.23$) and (iv) yield to non-critical buckling stress ratio (0.45 to 10.4). The numerical investigation is carried out by conducting linear and non-linear shell finite element analysis (SFEA) using ABAQUS software. The non-linear SFEA includes both geometry and material non-linearity. The numerical results obtained are deeply analysed to understand the post buckling mechanics, failure modes and ultimate strength that are influenced by L-D interaction with respect to strength related parametric ratios. The ultimate strength data obtained from numerical analysis are compared with (i) the experimental tests data concerning L-D interaction mode buckling reported by other researchers (ii) column strength predicted by Direct Strength Method (DSM) column strength curves for local and distortional buckling specified in AISI S-100 (iii) strength predicted by available DSM based approaches that includes L-D interaction mode failure. The role of flange width to web depth ratio on post buckling strength erosion is reported. Then the paper concludes with merits and limitations of codified DSM and available DSM based approaches on accurate failure strength prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.767-776
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• 2014

This study estimated productivity change and RTS using Parametric and Non-Parametric approach with 5 year annual data from 2006 to 2010 of 20 public general hospitals in Vietnam collected from Ho Chi Min City Health Department of Health Database. The results could be summarized as follows; First, by the Non-Parametric approach. cumulated productivity growth of 5 years was increased by 2.8% due to regress 5.2% of technical efficiency, 0.3% of pure technical efficiency, but scale efficiency was decreased 5.1% Second, by the Non-Parametric approach, cumulated productivity growth was decreased by 12.8% to due to drop of 17.1% of technical change, 9.1% of scale efficiency. In conclusion, the common result of both approaches was that scale inefficiencies occurs in public hospitals in Vietnam, and they affected productivity change of public hospitals in Vietnam. Namely, 70% to 80% of the analyzed Vietnam public hospitals were at the status of DRS(decreased returns to scale), which suggested that they had oversupply beds relative to the number of occupied patients, it was meaningful that this study was the first study in Korea to measure efficiency and productivity change of Vietnamese public general hospitals and so it could be utilized as the basic information needed to enter hospital industry of Vietnam in the future.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.525-534
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• 2012

The issue is all about the study of warranty data collection and the analysis method to get a reasonable information of the products and improve reliability. In this paper, we consider the classification of warranty data analyses into a parametric and non-parametric analysis and method to get a reasonable information of the products. Also, it is considered the research trend by grouping the relationship among the studies. This study would be used to find the effective application and the condition of warranty data analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.863-868
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• 2002

Operational Modal Analysis also known as Ambient Modal Analysis has an increasing interest in mechanical engineering. Especially on big structures where the excitation and not less important the determination of the forces is most often a problem. In a structure like a wind turbine wing where the modes occur both close in frequency and hi-directional the ambient excitation has big advantages. In this paper modal parameters are identified from the wing by operational modal analysis. For the parameter identification both parametric and non-parametric techniques are used. Advantages and disadvantages are discussed and results from the different techniques are compared

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2999-3029
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• 2024

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

• Journal of the Korean Geosynthetics Society
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• v.12 no.1
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• pp.11-19
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• 2013

Coarse-grained soils are typical engineering materials commonly used in many civil engineering applications such as structural fills, subgrade and drainage fills for dam, railway and bridge. Various researches have been performed with related to constitutive laws for numerical analysis of such structures. This paper presents a parametric study for a constitutive model for coarse grained materials. The model is a kind of the bounding surface models based on critical state theory. A distinct feature of the model is to capture the response of coarse-grained materials with different void ratios and confining pressures using a single set of model parameters. The model behavior is defined with a set of elastic parameters, critical state parameters, and model-specific parameters. The parametric study was performed for the model-specific parameters. The result of parametric study shows that the model is capable to capture stress-dilatancy behavior and kinematic-hardening under non-associative plastic flow.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.45-55
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• 2005

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.