• Structural Engineering and Mechanics
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• v.32 no.5
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• pp.667-683
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• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1284-1284
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• 2001

The transesterification of molten ethylene/vinylacetate (EVA) copolymers by octanol as a reagent and sodium methoxide as a catalyst in an extruder has been monitored by on-line near infrared (NIR) spectroscopy. A total of 60 NIR spectra were acquired for 37 minutes with the last spectrum recorded 31 minutes after the addition of octanol and catalyst was stopped. The experimental spectra show strong baseline fluctuations which are corrected for by multiplicative scatter correction (MSC). The chemometric methods of orthogonal projection approach (OPA) and multivariate curve resolution (MCR) were used to resolve the spectra and to derive concentration profiles of the species. The detailed analysis reveals the absence of completely pure variables that leads to small errors in the calculation of pure spectra. The initial estimation of a concentration that is necessary as an input parameter for MCR also presents a non-trivial task. We obtained results that were not ideal but applicable for practical concentration control. They enable a fast monitoring of the process in real-time and resolve the spectra of the EVA copolymer and the ethylene/vinyl alcohol (EVAL) copolymer to be very close to the reference spectra. The chemometric methods used and the decomposed spectra are discussed in detail.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.248-255
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels. however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results are clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the firstpart are used to prepare a set of data for learning process. Tunnel behavior especially the displacements of the lining has been exclusively investigated for the back analysis.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.2 no.3
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• pp.243-255
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• 1990

The objective of this study is to present an efficient measurement method of the effective thermal diffusivity for the fibrous insulation material. The non-linear parameter estimation (NPE) method is adapted for this analysis because of its accuracy and its results are compared with those by other direct methods such as CTP, CHP and STD method. A experimental system is constructed with bell-jar vaccum chamber, diffusion pump, tube type furnace, control unit and data acquisition system included with A/D converter and IBM XT/AT personal computer. The typical results obtained from this study are as follows; 1) NPE method can be recommended as an useful and accurate method to measure the effective thermal diffusivity of insuation material because it is shown that the measurement error compared with those by other direct methods is reduced for standard material, NBS-1450b. 2) NPE method can minimize the effects of ill-measured temperature due to external disturbance, because the final value is found by point to point estimating. 3) NPE method dose not depend on the kinds of heat flux, since the surfac temperature are used to estimate the thermal diffusivity. 4) With NPE method, compared with the steady state method, a measuring time and a sample size could be reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.878-884
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• 2012

In this paper, we consider the sway suppression control problem for container cranes with the frictions between the trolley and the rail. If the friction effects in the system can be modelled, there is an improved potential to design controllers that can cancel the effects. The proposed control improves the trolley positioning and sway suppressing against various frictions. The proposed synthesis combines a variable structure control and the adaptive control to cope with various frictions including the unknown constants. First, the variable structure control with the simple switching action is designed, which is based on a class of feedback lineariztion methods for the fast stabilization of the under-actuated sway dynamics of container. Second, the adaptive control with a parameter estimation is designed, which is based on Lyapunov stability methods for suppressing the oscillation of the trolley travelling, especially due to Coulomb friction in the vicinity of the target position. The asymptotic stability of the overall closed-loop system is assured irrespective of variations of rope length. Simulation are shown under initial sway, external wind disturbances, and various frictions.

• Geomechanics and Engineering
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• v.1 no.3
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• pp.241-257
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• 2009

The one-dimensional compressibility of sand is an important property for the estimation of settlement or deformation of sand deposits. The $K_0$ value of sand is also an important design parameter. Experimental results are presented in this paper to study the compressibility of sand in $K_0$ consolidation tests. The $K_0$ consolidation tests were carried out using a triaxial cell and a plane-strain apparatus. Specimens prepared using both the moist tamping and the water sedimentation methods were tested. The testing data demonstrate that the type of testing apparatus does not affect the $K_0$ measurement if proper boundary conditions are imposed in the tests. The data also show that the compressibility and the $K_0$ value of loose sand specimens prepared using the moist tamping method are very sensitive to the variation of void ratio. The $K_0$ values measured from these tests do not agree with the $K_0$ values calculated from Jaky's equation. The compressibility and $K_0$ values of sand obtained from tests on specimens prepared using different preparation methods are different which may reflect the influence of soil fabrics or structures on the one dimensional compression behavior of sand.

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1008-1013
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• 2014

This paper introduces an adaptive anti-sway tracking control algorithm for container cranes with unknown payloads and friction between the trolley and the rail. If the friction effects in the system can be modeled, there is an improved potential to design controllers that can cancel these effects. The proposed control improves the sway suppressing and the positioning capabilities of the trolley and hoisting against uncertain payload and friction. The variable structure controls are first designed based on a class of feedback linearization methods for the stabilization of the under-actuated sway dynamics. The adaptation mechanism are then designed with parameter estimation of unknown payload and friction compensation for the trolley and hoisting, based on Lyapunov stability methods for the accurate positioning and fast attenuation of trolley oscillation due to frictions in the vicinity of the target position. The asymptotic stability of the overall closed-loop system is assured irrespective of variations of rope length. Simulations are shown under various frictions and external winds in the case of no priori information of payload mass.