• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.84-94
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• 2011

In this paper, we propose a method to provide the distribution of option price under local volatility model when market-provided implied volatility data are given. The local volatility model is one of the most widely used smile-consistent models. In local volatility model, the volatility is a deterministic function of the random stock price. Before estimating local volatility surface (LVS), we need to estimate implied volatility surfaces (IVS) from market data. To do this we use local polynomial smoothing method. Then we apply the Dupire formula to estimate the resulting LVS. However, the result is dependent on the bandwidth of kernel function employed in local polynomial smoothing method and to solve this problem, the proposed method in this paper makes use of model averaging approach by means of bandwidth priors, and then produces a robust local volatility surface estimation with a confidence interval. After constructing LVS, we price barrier option with the LVS estimation through Monte Carlo simulation. To show the merits of our proposed method, we have conducted experiments on simulated and market data which are relevant to KOSPI200 call equity linked warrants (ELWs.) We could show by these experiments that the results of the proposed method are quite reasonable and acceptable when compared to the previous works.

• Korean Journal of Computational Design and Engineering
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• v.11 no.3
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• pp.164-171
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• 2006

As collaborative design and configuration design are of increasing importance in product development, it becomes essential to exchange the feature and parametric CAD models among participants. A history-based parametric method has been proposed and implemented. But each translator which exchanges the feature and parametric information tends to be heavy because to implement duplicated functions such as the identification of the selected geometries, mapping between features which have different attributes. Furthermore. because the history-based parametric translator uses the procedural model as the neutral format, which is the XML macro file, the history-based parametric translators need a geometric modeling kernel to generate an internal explicit geometric model. To ease the problem, we implemented a shared integration platform, the TransCAD. The TransCAD separates translators from the XML macro files. The translators for various CAD systems need to communicate with only the TransCAD. To support the communication with the TransCAD, we exposed the functions of the TransCAD by using the Automation APIs, which is developed by Microsoft. The Automation APIs of the TransCAD consist of the part modeling functions, the data extraction functions, and the utility functions. Each translator uses these functions to translate a parametric CAD model from the sending CAD system into the XML format, or from the in format into the model of the receiving CAD system This paper introduces what the TransCAD is and how it works for the exchange of the feature and parametric models.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.3-9
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• 1999

Using Information systems to process massive data, quickly and exactly, organizations have chances to enhance their performance. The limitations of IS function to support decision-making, however, have been frequently mentioned. In this context, in addition to traditional mathematical model that is a kernel of DSS, the needs for Data Warehouse which is a system supporting business process analysis are emerging. In this study, for those needs, first, we introduce issues of implementation methodology for D/W, especially various models relating development process. Then, we investigate correlation between these models and key factors for success of D/W.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.101-119
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• 2000

US DoD designated the High LEvel Architecture (HLA) as the standard technical architecture for all military simulation since 1996. HLA will supercede the current Distributed Interactive Simulation(DIS) and Aggregated LEvel Simulation Protocol(ALSP) methods by no funds for developing/modifying non-HLA compliant simulations. The new architecture specifies Rules which define relationships among federation components, an Objects Model Template which species the form which simulation elements are described, and an Interface Specification which describes the way simulations interact during operations. HLA is named as standard architecture in NATO, Australia and many other militaries. Also, it will be IEEE standard in the near future. It goes without saying that ROK military whose simulation models are almost from US must be prepared in areas such as ROK-US combined exercise, training, weapon system acquisition, interface models with C4I system, OPLAN analysis, operations, and os on. In this paper, we propose several effective alternatives and issues for ROK Defense Modeling and Simulation under the transition of new HLA architecture. Those include secure the kernel of new simulation technology and develop our own conceptual model, RTI software, prototype federation for each service and aggregated one. In order to challenge the new simulation architecture effectively, we should innovate our current defense modeling and simulation infrastructure such s manpower, organization, budget, research environment, relationships among academia and industry, and many others.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.23-31
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• 1999

Using Information Systems to process massive data, quickly and exactly, organizations have chances to enhance their performance. The limitations of IS function to support decision-making, however, have been frequently mentioned In this context, in addition to traditional mathematical model that is a kernel DSS, the needs for Data Warehouse which is a system supporting business process analysis are emerging. In this study, for those needs first we introduce issues of implementation methodology for D/W, especially various models relating development process. Then we investigate correlation between these models and key factors for success of R/W.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Advances in aircraft and spacecraft science
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• v.7 no.3
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• pp.215-228
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• 2020

As is shown in the paper, the Koltunov-Rzhanitsyn singular kernel of heredity (when constructing mathematical models of the dynamics problem of the hereditary theory of viscoelasticity) adequately describes real mechanical processes, best approximates experimental data for a long period of time. A mathematical model of the problem of the flutter of viscoelastic plates moving in a gas with a high supersonic velocity is given. Using the Bubnov-Galerkin method, discrete models of the problem of the flatter of viscoelastic plates flowed over by supersonic gas flow are obtained. A numerical method is developed to solve nonlinear integro-differential equations (IDE) for the problem of the hereditary theory of viscoelasticity with weakly singular kernels. A general computational algorithm and a system of application programs have been developed, which allow one to investigate the nonlinear dynamic problems of the hereditary theory of viscoelasticity with weakly singular kernels. On the basis of the proposed numerical method and algorithm, nonlinear problems of the flutter of viscoelastic plates flowed over in a gas flow at an arbitrary angle are investigated. In a wide range of changes in various parameters of the plate, the critical velocity of the flutter is determined. It is shown that the singularity parameter α affects not only the oscillations of viscoelastic systems, but the critical velocity of the flutter as well.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1993-1998
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• 2004

This paper proposes a new framework for control system design, called the data-based control approach or data space approach, in which the input and output data of a dynamical system is directly and solely used to analyze or design a control system without the employment of any mathematical models like transfer functions, state space equations, and kernel representations. Since, in this approach, most of the analysis and design processes are carried out in the domain of the data space, we introduce some notions of geometrical objects, e.g., the openloop and closed-loop data spaces, which serve as the system representations in the data space. In addition, we establish a relationship between the open-loop and closed-loop data spaces that the closed-loop data space is contained in the open-loop data space as one of its subspaces. By using this relationship, we can derive the data-based stabilization condition for a linear time-invariant discrete-time system, which leads to a linear matrix inequality with a rank constraint.