• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.976-983
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• 2023

Purpose: The dynamic behavior of a bridge structure under seismic loading depends on many uncertainties, such as the nature of the seismic waves and the material and geometric properties. However, not all uncertainties have a significant impact on the dynamic behavior of a bridge structure. Since probabilistic seismic performance evaluation considering even low-impact uncertainties is computationally expensive, the uncertainties should be identified by considering their impact on the dynamic behavior of the bridge. Therefore, in this study, a global sensitivity analysis was performed to identify the main parameters affecting the dynamic behavior of bridges with I-curved girders. Method: Considering the uncertainty of the earthquake and the material and geometric uncertainty of the curved bridge, a finite element analysis was performed, and a surrogate model was developed based on the analysis results. The surrogate model was evaluated using performance metrics such as coefficient of determination, and finally, a global sensitivity analysis based on the surrogate model was performed. Result: The uncertainty factors that have the greatest influence on the stress response of the I-curved girder under seismic loading are the peak ground acceleration (PGA), the height of the bridge (h), and the yield stress of the steel (fy). The main effect sensitivity indices of PGA, h, and fy were found to be 0.7096, 0.0839, and 0.0352, respectively, and the total sensitivity indices were found to be 0.9459, 0.1297, and 0.0678, respectively. Conclusion: The stress response of the I-shaped curved girder is dominated by the uncertainty of the input motions and is strongly influenced by the interaction effect between each uncertainty factor. Therefore, additional sensitivity analysis of the uncertainty of the input motions, such as the number of input motions and the intensity measure(IM), and a global sensitivity analysis considering the structural uncertainty, such as the number and curvature of the curved girders, are required.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.73-86
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• 2007

Risk-based remediation strategy (RBRS) is a consistent decision-making process for the assessment and response to chemical release based on protecting human health and the environment. The decision-making process described integrates exposure and risk assessment practices with site assessment activities and remedial action selection to ensure that the chosen actions are protective of human health and the environment. The general sequences of events in Tier 1 is as follows: initial site assessment, development of conceptual site model with all exposure pathways, data collection on pollutants and receptors, and identification of risk-based screening level (RBSL). If site conditions do not meet RBSL, it needs further site-specific tier evaluation, Tier 2. In most cases, only limited number of exposure pathways, exposure scenarios, and chemicals of concern are considered the Tier 2 evaluation since many are eliminated from consideration during the Tier 1 evaluation. In spite of uncertainties due to the conservatism applied to risk calculations, limitation in site-specific data collections, and variables affecting the selection of target risk levels and exposure factors, RBRS provides us time- and cost-effectiveness of the remedial action. To ensure reliance of the results, the development team should consider land and resource use, cumulative risks, and additive effects. In addition, it is necessary to develop appropriate site assessment guideline and reliable toxicity assessment method, and to study on site-specific parameters and exposure parameters in Korea.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.835-841
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• 2013

Modern solid-state gyroscopes (HRG) with hemispherical resonators from high-purity quartz glass and special surface superfinishing and ultrathin gold coating become the best instruments for precise-grade inertial reference units (IRU) targeting long-term space missions. Designing of these sensors could be a notable contribution into development of Korea as a space nation. In participial, 40mm diameter thin-shell resonator from high-purity fused quartz, fabricated as a single-piece with its supporting stem has been designed, machined, etched, tuned, tested, and delivered by STM Co. (ATS of Ukraine) several years ago; an extremely-high Q-factor (upto 10~20 millions) has been shown. Understanding of the best way how to match such a unique sensor with inner glass assembly of the gyro means how to use the high potential in a maximal extent; and this has become the urgent task. Inner quartz glass assembly has a very thin indium (In) layer soldered the resonator and its silica base (case), but effects of internal resonances between operational modal pair of the shell-cup and its side (parasitic) modes can notable degrade the potential of the sensor as a whole, instead of so low level of resonator's intrinsic losses. Unfortunately, there are special combinations of dimensions of the parts (so-called, "resonant sizes"), when intensive losses of energy occurs. The authors proposed to use the length of stem's fixture as an additional design parameter to avoid such cases. So-called, a cyclic scheme of finite element method (FEM) and ANSYS software were employed to estimate different combinations of gyro assembly parameters. This variant has no mismatches of numerical origin due to FEM's discrete mesh. The optimum length and dangerous "resonant lengths" have been found. The special attention has been paid to analyses of 3D effects in a cup-stem transient zone, including determination of a difference between the positions of geometrical Pole of the resonant hemisphere and of its "dynamical Pole", i.e., its real zone of oscillation node. Boundary effects between the shell (cup) and 3D short "beams" (inner and outer stems) have been ranged. The results of the numerical experiments have been compared with the classic model of a quasi-hemispherical shell band with inextensional midsurface, and the solution using Rayleigh's functions of the $1^{st}$ and $2^{nd}$ kinds. To guarantee the truth of the recommended sizes to a designer of the real device, the analytical and FEM results have been compared with experimental data for a party of real resonators. The consistency of the results obtained by different means has been shown with errors less than 5%. The results notably differ from the data published earlier by different researchers.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.221-237
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• 2005

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.6
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• pp.706-714
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• 2007

This study suggested a method for prediction of residual chlorine and THMs in water distribution system by measurement of residual chlorine, THMs, and other parameters, estimation of chlorine decay coefficients and THM formation coefficients, and simulation of water qualities using pipe network analysis. Bulk decay coefficients of parallel first-order were obtained by bottle tests, and pipe wall decay coefficients of first-order were estimated through evaluation of 5 models, which showed the lowest values of 0.03 for MAE(mean absolute error) and 0.037 MAE in comparison with the observed in field. And bottle tests were conducted to model first-order reaction of THM formation by nonlinear least square regression and the resultant coefficients were compared with the observed in field. As a result, the coefficients of determination$(R^2)$ for the observed and the predicted values were 0.98 in September and 0.82 in November, and the formation of THMs was predicted by modeling.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.325-332
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• 2022

Determination of VLBI IVP (Very Long Baseline Interferometry Invariant Point) position with high accuracy is required to compute local tie vectors between the space geodetic techniques. In general, reflective targets are attached on VLBI antenna and slant distances, horizontal and vertical angles are measured from the pillars. Then, adjustment computation is performed by using the mathematical model which connects measurements and unknown parameters. This indicates that the accuracy of the estimated solutions is affected by the accuracy of the measurements. One of issues in local tie surveying, however, is that the reflective targets are not in favorable condition, that is, the reflective sheet target cannot be perfectly aligned to the instrument perpendicularly. Deviation from the line of sight of an instrument may cause different type of measurement errors. This inherent limitation may lead to incorrect stochastic modeling for the measurements in adjustment computation procedures. In this study, error characteristics by measurement types and pillars are analyzed, respectively. The analysis on the studentized residuals is performed after adjustment computation. The normality of the residuals is tested and then equal variance test between the measurement types are performed. The results show that there are differences in variance according to the measurement types. Differences in variance between distances and angle measurements are observed when F-test is performed for the measurements from each pillar. Therefore, more detailed stochastic modeling is required for optimal solutions, especially in local tie survey.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.247-255
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• 2005

ice yield and plant growth response to nitrogen (N) fertilizer may vary within a field, probably due to spatially variable soil conditions. An experiment designed for studying the response of rice yield to different rates of N in combination with variable soil conditions was carried out at a field where spatial variation in soil properties, plant growth, and yield across the field was documented from our previous studies for two years. The field with area of 6,600 m2 was divided into six strips running east-west so that variable soil conditions could be included in each strip. Each strip was subjected to different N application level (six levels from 0 to 165kg/ha), and schematically divided into 12 grids $(10m \times10m\;for\;each\;grid)$ for sampling and measurement of plant growth and rice grain yield. Most of plant growth parameters and rice yield showed high variations even at the same N fertilizer level due to the spatially variable soil condition. However, the maximum plant growth and yield response to N fertilizer rate that was analyzed using boundary line analysis followed the Mitcherlich equation (negative exponential function), approaching a maximum value with increasing N fertilizer rate. Assuming the obtainable maximum rice yield is constrained by a limiting soil property, the following model to predict rice grain yield was obtained: $Y=10765{1-0.4704^*EXP(-0.0117^*FN)}^*MIN(I-{clay},\;I_{om},\;I_{cec},\;I_{TN},\; I_{Si})$ where FN is N fertilizer rate (kg/ha), I is index for subscripted soil properties, and MIN is an operator for selecting the minimum value. The observed and predicted yield was well fitted to 1:1 line (Y=X) with determination coefficient of 0.564. As this result was obtained in a very limited condition and did not explain the yield variability so high, this result may not be applied to practical N management. However, this approach has potential for quantifying the grain yield response to N fertilizer rate under variable soil conditions and formulating the site-specific N prescription for the management of spatial yield variability in a field if sufficient data set is acquired for boundary line analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1719-1729
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• 1994

In this paper, an adaptive CAC(Connection Admission Control) method is proposed. The adaptive CAC uses traffic estimates derived from both traffic parameter specified by user and cell flow measurements. Traffic estimation using user-specified parameters is performed at every moment of connection request or connection release by recursive formula which makes real-time calculation possible. Traffic estimation using cell flow measurement is carried out when the number of connected calls does not change during a measurement reflection period-renewal period. The most import ant thing for the traffic estimation using cell flow measurement is the determination of the length of a renewal period to trace a real traffic flow with an allowable time lag and the measurement reflection ratio(MRR) both to reduce the portion of overestimation and to avoid underestimation of real traffic flow. To solve these problems, the adaptive CAC updates renewal period and MRR adaptively according to the number of connections and the elapsed time after last connection or release respectively. Performance analysis for the proposed method is evaluated in several aspects for the cases of both homogeneous and heterogeneous bursty traffic. Numerical examples show the adaptive CAC method has the better performance compared with conventional CAC method based on burst model from the both utilization and QOS point of view.