• Bulletin of the Korean Mathematical Society
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• v.52 no.4
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• pp.1113-1122
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• 2015

This paper is concerned with the pattern formation of a diffusive predator-prey model with two time delays. Based upon an analysis of Hopf bifurcation, we demonstrate that time delays can induce spatial patterns under some conditions. Moreover, by use of a series of numerical simulations, we show that the type of spatial patterns is the spiral wave. Finally, we demonstrate that the spiral wave is asymptotically stable.

• Progress in Medical Physics
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• v.1 no.1
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• pp.75-84
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• 1990

We have claculated the absorbed dose distributions in water phantom irradiated by high energy photon beam. PDD (Percent Depth Dose) and Beam Profile can be represented by functions of depths and distances by using one dimensional model model based on transport theory. The parameters on scattering and absorption are evaluated by using non-linear regression process method. The values neeessary for calculation are obtained by simple experiment. The calculated values are in good agreement with the measured values.

• Journal of The Korean Association For Science Education
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• v.7 no.1
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• pp.41-51
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• 1987

The contents which is related to energy in current science textbooks of high school have been investigated. Based on the analysis of the results, we attempt to develop a model of energy curriculum on high school level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.47-58
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• 2019

The paper presents the results of a multi-criteria comparative evaluation of 12 feasible Russian nuclear energy deployment scenarios with thermal and fast reactors in a closed nuclear fuel cycle. The comparative evaluation was performed based on 6 performance indicators and 5 different MCDA methods (Simple Scoring Model, MAVT / MAUT, AHP, TOPSIS, PROMETHEE) in accordance with the recommendations elaborated by the IAEA/INPRO section. It is shown that the use of different MCDA methods to compare the nuclear energy deployment scenarios, despite some differences in the rankings, leads to well-coordinated and similar results. Taking into account the uncertainties in the weights within a multi-attribute model, it was possible to rank the scenarios in the absence of information regarding the relative importance of performance indicators and determine the preference probability for a certain nuclear energy deployment scenario. Based on the results of the uncertainty/sensitivity analysis and additional analysis of alternatives as well as the whole set of graphical and attribute data, it was possible to identify the most promising nuclear energy deployment scenario under the assumptions made.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.966-969
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• 2006

An optical model based on the optical thin-film theory is derived to calculate the output radiance of small molecules organic light-emitting diodes (OLEDs). We have designed the high efficiency OLEDs using the reflectance phase control of dielectric layers. It is found that OLED with a single $TiO_2$ dielectric layer is a good candidate to enhance the outcoupling efficiency and increase the color purity.

• Journal of Magnetics
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• v.9 no.2
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• pp.47-51
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• 2004

Giant magneto-impedance (GMI) effect in zero-magnetostrictive Co-based amorphous ribbons samples in their as-quenched and stress-released states as well as with intentionally induced magnetic anisotropy were investigated. Magnetic and impedance properties of the samples exhibiting different anisotropy were compared and the optimum operation conditions for the studied samples from the view-point of their utilization as a sensor element have been determined. A design of a model of magnetic field sensor and characteristics of the constructed prototype are presented.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.55.3-55.3
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• 2019

We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75 h^-1 Mpc)³ volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts --- in other words, the machine better mimics IllustrisTNG's galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1 h^-1 Gpc)³ volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.

• Journal of Science Education
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• v.43 no.1
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• pp.173-193
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• 2019

An expert teachers' instruction can be helpful to other teachers because good teaching effectively guides students to develop meaningful learning. Feynman is an excellent physics lecturer as well as one of the greatest physicists of the 20th century who presented and explained physics with his unique teaching style based on his great store of knowledge. However, it is not easy to capture and visualize teaching because it is not only the complex phenomena interrelated to various factors with the content to be taught but also the tacit representation. In this study, the framework of knowledge & belief based on the integrated mental model theory was used as a tool to capture and visualize complex and tacit representation of Feynman's teaching of 'The theory of gravitation,' a chapter in The Feynman Lectures on Physics. Feynman's teaching was found to go beyond the transmission of physics concepts by showing that components of the framework of knowledge & belief were effectively intertwined and integrated in his teaching and the storyline was well-organized. On the basis of these discussions, the implications of Feynman's teaching analyzed within the framework of knowledge & belief for physics teacher education are derived. Finally, the characteristics of the framework of knowledge & belief as tools for the analysis of teaching are presented.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1406-1409
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• 2018

We have frequently encountered the rapid changes that prevalent opinion of the social community is toppled by a new and opposite opinion against the pre-exiting one. To understand this interesting process, mean-field model with infinite-interaction range has been mostly considered in previous studies S. A. Marvel et al., Phys. Rev. Lett. 110, 118702 (2012). However, the mean-field interaction range is lack of reality in the sense that any individual cannot interact with all of the others in the community. Based on it, in the present work, we consider a simple model of opinion consensus so-called basic model on the low-dimensional lattices (d = 1, 2) with finite interaction range. The model consists of four types of subpopulations with different opinions: A, B, AB, and the zealot of A denoted by $A_c$, following the basic model shown in the work by S. A. Marvel et al.. Comparing with their work, we consider the finite range of the interaction, and particularly reconstruct the lattice structure by adding new links when the two individuals have the distance < ${\sigma}$. We explore how the interaction range ${\sigma}$ affects the opinion consensus process on the reconstructed lattice structure. We find that the critical fraction of population for $A_c$ required for the opinion consensus on A shows different behaviors in the small and large interaction ranges. Especially, the critical fraction for $A_c$ increases with the size of ${\sigma}$ in the region of small interaction range, which is counter-intuitive: When the interaction range is increased, not only the number of nodes affected by $A_c$ but also that affected by B grows, which is believed to cause the increasing behavior of the critical fraction for $A_c$. We also present the difference of dynamic process to the opinion consensus between the regions of small and large interaction ranges.