• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.1
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• pp.20-27
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• 2022

Present modeling study of nanoenergetics focuses on the numerical simulation of reaction wave propagation in normal direction across nanoscale multilayers of aluminum and nickel combination. The governing equations for atomic and thermal diffusion are employed in one-dimensional semi-infinitely alternating Al/Ni multilayered structures and the numerical results show the established patterns of quasi-steady intermetallic reaction waves. Also, the reaction wave speed is confirmed to be highly independent of reaction wave directions in such nanoenergetic structures.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1059-1069
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• 2023

Dam-break flow occurs when an elevated dam suddenly collapses, resulting in the catastrophic release of rapid and uncontrolled impounded water. This study compares laminar and turbulent closure models for simulating three-dimensional dam-break flows using OpenFOAM. The Reynolds-Averaged Navier-Stokes (RANS) model, specifically the k-ε model, is employed to capture turbulent dissipation. Two scenarios are evaluated based on a laboratory experiment and a modified multi-layered block obstacle scenario. Both models effectively represent dam-break flows, with the turbulent closure model reducing oscillations. However, excessive dissipation in turbulent models can underestimate water surface profiles. Improving numerical schemes and grid resolution enhances flow recreation, particularly near structures and during turbulence. Model stability is more significantly influenced by numerical schemes and grid refinement than the use of turbulence closure. The k-ε model's reliance on time-averaging processes poses challenges in representing dam-break profiles with pronounced discontinuities and unsteadiness. While simulating turbulence models requires extensive computational efforts, the performance improvement compared to laminar models is marginal. To achieve better representation, more advanced turbulence models like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) are recommended, necessitating small spatial and time scales. This research provides insights into the applicability of different modeling approaches for simulating dam-break flows, emphasizing the importance of accurate representation near structures and during turbulence.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.4
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• pp.46-53
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• 2008

In this study, we developed RBFN(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method considers harmonic information as well as fundamental frequency and voltage considered as essential factors in conventional method. Thus, the reposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. RBFN has some advantage such as simple structure and rapid computation ability compared with multi-layer perceptorn which is extensively applied for load modeling. To verify the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynomial method, MLPN and RBFN with no harmonic components.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.3
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• pp.133-142
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• 2010

The present work focuses on the characterization of material parameters of the Overlay(multilinear hardening) model for analyzing the non-isothermal cyclic deformation. In the previous study, all the parameters were especially based on the Overlay theories, and a simple method was suggested to find out the best material parameters for the isothermal cyclic deformation analysis. Based on the previous research this paper f dther improves the isothermal parameters and suggests how to apply the isothermal parameters to the non-isothermal conditions especially for the description of TMF(Thermo-Mechanical Fatigue) hysteresis behavior. The parameters are determined and calibrated using 400 series stainless steel test data in the reference papers. For the implementation into ABAQUS, a user subroutine is developed by means of ABAQUS/UMAT. The finite element results show good agreement with test for the case of uniaxial non-isothermal cyclic loading, signifying the proposed method can be used in the TMF analysis of the converter-inserted heavy duty muffler system and the stainless steel exhaust-manifold system which are to be done in our future research.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3063-3068
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• 1997

In this paper, we have extended the generalization back-propagation algorithm to multi-layer polynomial higher order neural networks. The purpose of this paper is to describe various pattern recognition using polynomial higher-order neural network. And we have applied shift position T-C test pattern for invariant pattern recognition and measured generalization by mirror symmetry problem. simulation result shows that the ability for invariant pattern recognition increase with the proposed technique. Recognition rate of invariant T-C pattern is 90% effective and of mirror symmetry problem is 70% effective when the proposed technique is utilized. These results are much better than those by the conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1856-1863
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• 2003

Overlay model had several advantages to describe hysteretic behavior of material and showed good capability for many engineering materials. However, this model is only applicable to material obeying Masing postulate. Some materials such as 316L stainless steel do not follow Masing postulate and show cyclic hardening(or softening) and strain range dependence. Low cycle fatigue tests of 316L stainless steel at 600$^{\circ}C$ were performed to investigate the characteristics of cyclic behavior of non-Masing material. From all tests cyclic softening was observed. There were differences in elastic limit of hysteresis loop according to applied strain range. To consider these features, modified overlay model was developed. Yield stresses of subelements were divided into isotropic and anisotropic part to describe the non-Masing behavior. The plastic strain range memorization was introduced to consider the strain range dependence. The prediction using modified overlay model showed a good accordance to actual hysteresis loops.

• Journal of Hydrogen and New Energy
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• v.35 no.2
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• pp.129-139
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• 2024

The significance of hydrogen economy and production technology is steadily increasing. This research reviewed strategies for utilizing hydrogen production technology by combining a multi-layer model, strategic niche management, and the need factor for Hoship. The model was validated as a strategy considering hydrogen production technology and the transformation of the energy system. Using this, a new business model for hydrogen production technology was created, finding a strategic niche and sophisticating the technology. It also proposed ways to unlock the potential of hydrogen production technology and improve its efficiency. This work contributes to the commercialization of hydrogen production technology and its role in sustainable energy conversion. It proposes a new and effective approach for utilizing hydrogen production technology, going beyond its limitations to suggest a more efficient method. It is hoped that these results will be helpful to researchers in hydrogen energy, and serve as a reference for establishing ways to utilize hydrogen production technology.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.79-87
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• 1998

정확한 태풍진로 예측은 동아시아 최대의 자연재해인 태풍의 피해를 최소화하는데 필수적이다. 기상역학에 기초를 둔 수치모델과 회귀분석등의 통계적 접근법이 사용되어왔다. 본 논문에서는 비선형 신경망모델인 다층퍼셉트론을 제안한다. 즉, 태풍진로예측을 이동경로, 속도, 기압 등의 변수로 이루어진 시계열의 예측으로 본다. 1945년부터 1989년까지 한반도에 접근한 태풍 데이터를 이용하여 제안된 신경망을 학습한 후, 94, 95년도에 접근한 태풍의 진로를 예측하였다. 신경망의 예측성능은 수치모델의 성능보다 조금 우수하거나 비슷하였다. 신경망의 성능은 충분히 더 향상될 수 있는 여지가 있다. 또한, 고가의 슈퍼컴퓨터로 여러 시간 계산을 해야하는 수치모델에 비하여 PC상에서 수초만에 계산을 할 수 있는 신경망 모델은 비용 면에서도 장점이 있다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.108-111
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• 2020

한국어 의존 파싱은 전이 기반 방식과 그래프 기반 방식의 두 갈래로 연구되어 왔으며 현재 가장 높은 성능을 보이고 있는 그래프 기반 파서인 Biaffine 어텐션 모델은 입력 시퀀스를 다층의 LSTM을 통해 인코딩 한 후 각각 별도의 MLP를 적용하여 의존소와 지배소에 대한 표상을 얻고 이를 Biaffine 어텐션을 통해 모든 의존소에 대한 지배소의 점수를 얻는 모델이다. 위의 Biaffine 어텐션 모델은 별도의 High-Order 정보를 활용하지 않는 first-order 파싱 모델이며 학습과정에서 어떠한 트리 관련 손실을 얻지 않는다. 본 연구에서는 같은 부모를 공유하는 형제 노드에 대한 점수를 모델링하고 정답 트리에 대한 조건부 확률을 모델링 하는 Second-Order TreeCRF 모델을 한국어 의존 파싱에 적용하여 실험 결과를 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.620-622
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• 2018

The need for particulate matter prediction algorithms has increased as social interest in the effects of human on particulate matter increased. Many studies have proposed statistical modelling and machine learning techniques based prediction models using weather data, but it is difficult to accurately set the environment and detailed conditions of the models. In addition, there is a need to design a new prediction model for missing data in domestic weather monitoring station. In this paper, fine dust prediction is performed using multi-layer perceptron network as a previous study for particulate matter prediction. For this purpose, a prediction model is designed based on weather data of three monitoring station and the suitability of the algorithm for particulate matter prediction is evaluated through comparison with actual data.