• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.306-319
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• 2009

A name is a key feature for distinguishing people, but we often fail to discriminate people because an author may have multiple names or multiple authors may share the same name. Such name ambiguity problems affect the performance of document retrieval, web search and database integration. Especially, in bibliography information, a number of errors may be included since there are different authors with the same name or an author name may be misspelled or represented with an abbreviation. For solving these problems, it is necessary to disambiguate the names inputted into the database. In this paper, we propose a method to solve the name ambiguity by using social networks constructed based on the relations between authors. We evaluated the effectiveness of the proposed system based on DBLP data that offer computer science bibliographic information.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.35-42
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• 1998

Finding a technique to achieve high machining precision and high productivity is an important issue for CNC machining. One of the solutions to meet better performance of machining is feedrate control. In this paper we present an adaptive feedrate neuro-control method for high precision and high speed machining. The adaptive neuro-control architecture consists of a neural network identifier(NNI) and an iterative learning control algorithm with inversion of the NNI. The NNI is an identifier for the nonlinear characteristics of feedrate and contour error, which is utilized in iterative learning for adaptive feedrate control with specified contour error tolerance. The proposed neuro-control method has been successfully evaluated for machining circular, corner and involute contours by computer simulations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.973-984
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• 2017

There is a growing need to apply TBM tunnelling method for the construction of underground facilities such as subways and utility tunnels in urban areas. Due to the variability and uncertainty of the ground, tunnelling in urban areas has various safety hazards which could cause damage to people and properties and it is very costly to recover from accidents. Therefore, it is very important to identify hazards from the planning and design phase and to establish risk mitigation measures. In this study, a total of 31 hazards affecting the stability of TBM tunnelling works in urban areas were listed from both the technical literature and correspondence with experts in tunnelling area. The importance and priorities of the hazards were analyzed by conducting Delphi technique, which is a decision-making method by consensus among experts. Finally, 12 hazards that satisfy the content validity criteria were settled and could be used as major control factors for accident prevention during TBM tunnelling works.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.1
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• pp.37-43
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• 2010

In this paper, a reliability-based design optimization is developed for the topology design of linear structures using a performance measure approach. Spatial domain is discretized using three dimensional Reissner-Mindlin plate elements and design variable is taken as the material property of each element. A continuum based adjoint variable method is employed for the efficient computation of sensitivity with respect to the design and random variables. The performance measure approach of RBDO is employed to evaluate the probabilistic constraints. The topology optimizationproblem is formulated to have probabilistic displacement constraints. The uncertainties such as material property and external loads are considered. Numerical examples show that the developed topology optimization method could effectively yield a reliable design, comparing with the other methods such as deterministic, safety factor, and worst case approaches.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.

• Journal of the Korea Institute of Building Construction
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• v.17 no.3
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• pp.287-294
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• 2017

Recently the construction project is becoming large-sized, complicated, and modernize. This has increased the uncertainty of construction risk. Therefore, studies should be followed regarding scientifically identifying the risk factors, quantifying the frequency and severity of risk factors in order to develop a model that can quantitatively evaluate and manage the risk for response the increased risk in construction. To address the problem, this study analyze the probability distribution of risk causes, the probability of occurrence and frequency of the specific risk level through Monte Carlo simulation method based on the accident data caused at construction sites. In the end, this study derives quantitative analysis by analyzing the amount of risk and probability distributions of accident causes. The results of this study will be a basis for future quantitative risk management models and risk management research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.91-99
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• 2022

The shipping industry is a service industry that operates its business by transporting cargo on ships and receiving freight. Therefore, large-scale capital investment is required for ship operation, and if the value of the ship is uncertain, the risk of shipping management increases. This study aims to identify the factors affecting changes in ship value and to analyze the importance of each variable. To achieve the goal, the factors affecting changes in ship value were identified and structured using the techniques of text mining and topic modeling, and classified into three main factors and 12 sub-factors. This study used AHP analysis to examine the relative importance of each factor. Results indicated that the main factor influencing the change in the vessel value was the shipping factor, followed by the investment factor and the environment factor. Other auxiliary factors that substantially affect the ship value include the volatility of the shipping market and of shipping freight.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.93-102
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• 2023

Building supervision can be defined as a major task that involves managing and supervising the construction process to support the creation of high-quality results. To successfully perform supervision work, it is necessary to collect various information generated in uncertain field conditions, but today, supervision is performed based on documents such as reports, so there are limitations in collecting information. In fact, it has also been reported that the recent collapse of an apartment building in Korea was caused by limitations of information communication in th supervision work. Accordingly, this study analyzed the types of digital technologies that can be used to improve the efficiency of building supervision work, and presented the prioritized application of them. Priority application was quantitatively evaluated using analytic hierarchy process on data through a survey. It is expected that the results of this study can be used as basic data to set the roadmap of digital technology for building supervision in the future.