• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1549-1557
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• 2004

Fatigue life of welded joints is governed by the propagation of multiple collinear surface cracks distributed randomly along weld toe. These cracks propagate under the mechanisms of mutual interaction and coalescence of the adjacent two cracks. To estimate the fatigue life, its influences on the above two mechanisms should be taken into account, which appear through the stress intensity factors disturbed mutually. However, it is difficult to calculate the stress intensity factors of the multiple surface cracks located in vicinity of weld toe due to its geometrical complexity. They are calculated normally by using the Μk-factors, but such Mk-factors are very rare in literature. In this study, the Μ$textsc{k}$-factors were obtained from a parametric study on crack length and depth, for which a finite element method is used. A fatigue test for a cruciform welded Joint was conducted and the fatigue life of the tested specimen was estimated using the present method with the informations obtained from the test, such as the number, size, and locations of the cracks. The estimated and measured fatigue life showed a good agreement.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.177-180
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• 1997

Outliers, unexpected extraordinary observations that look discordant from most observation in a data set are commonplace in various kinds of data analysis. Since the effect of outliers on model identification could be serious, the aim of this paper is to present some ways of handling outliers in given data set and to specify a model in the presence of outliers. A procedure based on neural network which identifies outliers, removes their effects, and specifies a model for the underlying process is proposed. In contrast with traditional parametric methods requiring to estimate the model's structure and parameters before detecting outliers, the proposed procedure is a nonparametric method without the estimation of model's structure and parameters before handling outliers and could be applied for real problems in the presence of outliers. The proposed methodology is performed as followings. Firstly, outliers are detected and the detected outliers replace the prediction values using outliers detection neural network. The data set removing the effect of outliers is retraining using neural network. Therefore the effects of outliers are removed and the modeling precision can be improved. Experimental results show that the proposed method is suitable for predicting data set in the presence of outliers.

• 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.

• Environmental and Resource Economics Review
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• v.18 no.1
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• pp.105-138
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• 2009

This study investigates the spatial distribution of the degree of water pollutants in Han River using data obtained by the water pollution observation stations. This study estimates a non -parametric kernel density function for each water pollutants, and tests a significant difference between two estimated distribution functions. Next, Generalized Entropy inequality indices are evaluated and this research tests difference of inequality indices between two years using bootstrapping method. Lastly in a dynamic of view, it is analyzed that there are significant changes in the ranking of water pollution level. Estimation results show that the degree of inequality in spatial distribution of water pollution tends to be stable or decreasing for last 15 years in a great part of water pollutants, and ranking of water pollution level hardly changes in Han River.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.60-67
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• 2008

The vibrations and noise origin in electric material is due to several coupled physical phenomena. The revolving electric machine complete modeling is complex; it does not allow simple parametric machine structure studies for various operation modes. This work presents a simple electromagnetic model which makes possible the machine principal parts flow estimation from flux density. Special interest is given in determining Switched Reluctance Machine (S.R.M) radial acceleration in accordance with the current supply. Our focus will be only on the magnetic origin efforts that are dominating in the S.R.M. The efforts calculation versus the current is presented in the case of a machine with a linearized rate. These efforts are considered as a tangential force producing the torque and a radial force that generates no torque. The application is realized on a 6/4 low power S.R.M type (6 stator teeth and 4 teeth rotor). The mechanical response is substituted in a transfer function. The model takes account of the power supply of the machine, the relation between the current supply and the efforts as well as the vibratory response of the machine to these efforts. Finally, the model is validated by comparison with similar experimental results within the framework of the definite assumptions.

• Computers and Concrete
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• v.6 no.6
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• pp.451-472
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• 2009

Based on a numerical method to analyse the full-range behaviour of prestressed concrete beams with unbonded tendons, parametric studies are carried out to investigate the influence of 11 parameters on the curvature ductility of unbonded prestressed concrete (UPC) beams. It is found that, among various parameters studied, the depth to prestressing tendons, depth to non-prestressed tension steel, partial prestressing ratio, yield strength of non-prestressed tension steel and concrete compressive strength have substantial effects on the curvature ductility. Although the curvature ductility of UPC beams is affected by a large number of factors, rather simple equations can be formulated for reasonably accurate estimation of curvature ductility. Conversion factors are introduced to cope with the difference in partial safety factors, shapes of equivalent stress blocks and the equations to predict the ultimate tendon stress in BS8110, EC2 and ACI318. The same equations can also be used to provide conservative estimates of ductility of UPC beams with compression steel.

• Environmental and Resource Economics Review
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• v.13 no.1
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• pp.83-117
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• 2004

This study investigates the spatial distributions of the ambient levels of air pollutants ($SO_2$, $NO_2$, $O_3$, CO, and PM) in Seoul metropolitan area using the data obtained by the air pollution observation stations. This study estimated a non-parametric kernel density function and two types of inequality indices, Gini and Entropy. Our estimation results show that the degree of inequality in spatial distribution of air pollution, in general, tends to be stable or slightly decreasing for the period of 1990~2001. In addition, we found that there are significant dynamics of air pollution levels in terms of spatial ranking.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.567-575
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• 2016

With the popularity of smartphone, user's location information is of great interest as mobile apps based on the location information are increasing. In this paper, we are interested in analyzing user's speed data based on the location information. It is not uncommon to observe locations with great measurement errors, removing them is necessary. The distribution of speed can be considered as a mixture model in accordance with transportation means. We identify a tail part as a component of a mixture model and fit a simple parametric model to the tail part of the speed distribution.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.286-293
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• 2005

In soft ground the settlement criterion usually governs. Therefore, it is very important not only reasonable assessment of the allowable bearing capacity of the soil but also reasonable assessment of settlement. In the previous studies by many other researchers, load concentration ratio and settlement reduction factor are usually proposed for estimating the settlement of granular compaction piles. In the previous studies, the reinforced ground with granular compaction piles is simplified as composite ground and the analysis is performed with in the basis of this assumption. However, the lateral deformation of granular compaction pile could not be considered and only the relative vertical strength between pile and soils could be considered in the analysis. In this study, a method adapting the Tresca failure criterion is proposed for calculating settlement of granular compaction pile. Proposed method can be considered the strength of pile material, pile diameter, installing distance of pile and the deformation behavior of vertical and horizontal directions of pile. In the presented study, large-scale field load test is performed and the results are described. Also, predictions of settlements from the proposed method are compared with the results of the load test. In addition, a series of parametric study is performed and the design parameters are analyzed.