• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1968-1971
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• 2003

We propose a knowledge-based algorithm for extracting an object boundary from low-quality image like the forward looking infrared image. With the multi-classes training data set, the global shape is modeled by multispace KL(MKL)[1] and curvature model. And the objective function for fitting the deformable boundary template represented by the shape model to true boundary in an input image is formulated by Bales rule. Simulation results show that our method has more accurateness in case of multi-classes training set and performs better in the sense of computation cost than point distribution model(PDM)[2]. It works well in distortion under the noise, pose variation and some kinds of occlusions.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1604-1615
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• 2023

In an open-pool type research reactor with a downward forced flow in the core, pipes can be under sub-atmospheric pressure because of the large pressure drop at the reactor core in the atmospheric pool. Sub-atmospheric pressure can result in air inflow into the pipe from the pressure difference between the atmosphere and the inside of the pipe, which in a postulated pipe break scenario can lead to the breakdown of the cooling pump. In this study, a plant-scale experiment was conducted to study air inflow in large piping systems by considering the actual operational conditions of an advanced research reactor. The air inflow rate was measured, and the entrained air was visualized to investigate the behavior of air inflow and flow regime depending on the pipe break size. In addition, the developed drift-flux model for a large vertical pipe with a diameter of 600 mm was compared with other correlations. The flow regime transition in a large vertical pipe under downward flow was also studied using the newly developed drift-flux model. Consequently, the characteristics of two-phase flow in a large vertical pipe were found to differ from those in small vertical pipes where liquid recirculation was not dominant.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.27-36
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• 2014

This paper presents an adaptive wide-area damping controller (WADC) based on generalized predictive control (GPC) and model identification for damping the inter-area low frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm (RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized model which contains dominant inter-area low frequency oscillations. Based on this linearlized model, the generalized predictive control scheme considering control output constraints is employed to obtain the optimal control signal in each sampling interval. Case studies are undertaken on a two-area four-machine power system and the New England 10-machine 39-bus power system, respectively. Simulation results show that the proposed adaptive WADC not only can damp the inter-area oscillations effectively under a wide range of operation conditions and different disturbances, but also has better robustness against to the time delay existing in the remote signals. The comparison studies with the conventional lead-lag WADC are also provided.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.585-590
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• 1997

The condonation models in the standard RELAP5/MOD3.2 code are assessed and improved based on the database, which is constructed from the previous experimental data on various condonation phenomena The default model the laminar film condonation in RELAP5/MOD3.2 does not give any reliable predictions, and its alternative model always predicts higher values than the experimental data Therefore, it is needed to develop a new correlation based on the experimental data of various operating ranges in the constructed database. The Shah correlation, which is used to calculate the turbulent film condensation heat transfer coefficients in the standard RELAP5/MOD3.2, well predicts the experimental data in the database. The horizontally stratified condonation model of RELAP5/MOD3.2 overpredicts both cocurrent and countercurrent experimental data The correlation proposed by H.J.Kim predicts the database relatively well compared with that of RELAP5/MOD3.2 The RELAP5/MOD3.2 model should use the liquid velocity for the calculation of the liquid Reynolds number and be modified to conifer the effects of the gas velocity and the film thickness.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.132.1-132.1
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• 2012

Radiation Belt Environment (RBE) model has developed to understand radiation belt dynamics as it considers whistler mode hiss and chorus waves which is responsible for relativistic electron acceleration and precipitation. Recently, many studies on electron loss by pitch-angle scattering have reported that elctromagnetic ion cyclotron (EMIC) wave is also responsible for main loss mechanism in dusk and equatorial regeion. Here, we attempt to incorporate EMIC into RBE model simulation code to understand more detailed physical dynamics in Radiation belt environemnt. We compare this developed model to data during storm events where both of electron loss and EMIC waves were detected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5588-5613
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• 2018

Virtualization technology has been widely applied in the area of computer security research that provides a new method for system protection. It has been a hotspot in system security research at present. Virtualization technology brings new risk as well as progress to computer operating system (OS). A multi-level perception security model using virtualization is proposed to deal with the problems of over-simplification of risk models, unreliable assumption of secure virtual machine monitor (VMM) and insufficient integration with virtualization technology in security design. Adopting the enhanced isolation mechanism of address space, the security perception units can be protected from risk environment. Based on parallel perceiving by the secure domain possessing with the same privilege level as VMM, a mechanism is established to ensure the security of VMM. In addition, a special pathway is set up to strengthen the ability of information interaction in the light of making reverse use of the method of covert channel. The evaluation results show that the proposed model is able to obtain the valuable risk information of system while ensuring the integrity of security perception units, and it can effectively identify the abnormal state of target system without significantly increasing the extra overhead.

• Journal of the Korean Society for Heat Treatment
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• v.37 no.3
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• pp.103-113
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• 2024

Martensite volume fraction significantly affects the mechanical properties of alloy steels. Martensite start temperature (M_s), transformation temperature for martensite 50 vol.% (M₅₀), and transformation temperature for martensite 90 vol.% (M₉₀) are important transformation temperatures to control the martensite phase fraction. Several researchers proposed empirical equations and machine learning models to predict the Ms temperature. These numerical approaches can easily predict the Ms temperature without additional experiment and cost. However, to control martensite phase fraction more precisely, we need to reduce prediction error of the Ms model and propose prediction models for other martensite transformation temperatures (M₅₀, M₉₀). In the present study, machine learning model was applied to suggest the predictive model for the Ms, M50, M90 temperatures. To explain prediction mechanisms and suggest feature importance on martensite transformation temperature of machine learning models, the explainable artificial intelligence (XAI) is employed. Random forest regression (RFR) showed the best performance for predicting the Ms, M50, M90 temperatures using different machine learning models. The feature importance was proposed and the prediction mechanisms were discussed by XAI.

• Journal of Radiation Industry
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• v.6 no.1
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• pp.23-29
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• 2012

In this study, we investigated the genetic variation in the general of monocot model plant (rice) in response to various ionizing irradiations including gamma-ray, ion beam and cosmic-ray. The non-irradiated and three irradiated (200 Gy of gamma-ray and 40 Gy of ion beam and cosmic-ray) plants were analyzed by AFLP technique using capillary electrophoresis with ABI3130xl genetic analyzer. The 29 primer combinations tested produced polymorphism results showing a total of 2,238 bands with fragments sizes ranged from 30 bp to 600 bp. The number of polymorphism generated by each primer combinations was varied significantly, ranging from 2 (M-CAC/E-ACG) to 158 (M-CAT/E-AGG) with an average of 77 bands. Polymorphic peaks were detected as 1,269 with an average of 44 per primer combinations. By UPGMA (Unweighted Pair Group Method using Arithmetic clustering) analysis method, the clusters were divided into non-irradiated sample and three irradiated samples at a similarity coefficient of 0.41 and three irradiation samples was subdivided into cosmic-ray and two irradiation samples (200 Gy of gamma-ray and 40 Gy of ion beam) at similarity coefficient of 0.48. Similarity coefficient values ranged from 0.41 to 0.55.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.175.2-175
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• 2001

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2016.05a
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• pp.45-46
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• 2016