• 한국통신학회논문지
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• 제38A권12호
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• pp.998-1012
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• 2013

The article is concerned with a mathematical modeling which can improve performances of PDE-based restoration models. Most PDE-based restoration models tend to lose fine structures due to certain degrees of nonphysical dissipation. Sources of such an undesirable dissipation are analyzed for total variation-based restoration models. Based on the analysis, the so-called equalized net diffusion (END) modeling is suggested in order for PDE-based restoration models to significantly reduce nonphysical dissipation. It has been numerically verified that the END-incorporated models can preserve and recover fine structures satisfactorily, outperforming the basic models for both quality and efficiency. Various numerical examples are shown to demonstrate effectiveness of the END modeling.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.660-672
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• 2016

In Part I of this paper, the two-temperature homogenized model for the fully ceramic microencapsulated fuel, in which tristructural isotropic particles are randomly dispersed in a fine lattice stochastic structure, was discussed. In this model, the fuel-kernel and silicon carbide matrix temperatures are distinguished. Moreover, the obtained temperature profiles are more realistic than those obtained using other models. Using the temperature-dependent thermal conductivities of uranium nitride and the silicon carbide matrix, temperature-dependent homogenized parameters were obtained. In Part II of the paper, coupled with the COREDAX code, a reactor core loaded by fully ceramic microencapsulated fuel in which tristructural isotropic particles are randomly dispersed in the fine lattice stochastic structure is analyzed via a two-temperature homogenized model at steady and transient states. The results are compared with those from harmonic- and volumetric-average thermal conductivity models; i.e., we compare $k_{eff}$ eigenvalues, power distributions, and temperature profiles in the hottest single channel at a steady state. At transient states, we compare total power, average energy deposition, and maximum temperatures in the hottest single channel obtained by the different thermal analysis models. The different thermal analysis models and the availability of fuel-kernel temperatures in the two-temperature homogenized model for Doppler temperature feedback lead to significant differences.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.48-54
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• 2004

This paper describes the development of automatic shape design program for spur gear. It produces automatically third-dimensional surface and solid model used in CAD/CAM system with inputting simple measurements. This program can maximize user's convenience and get surface and solid model quickly as accepting GUI(graphic user interface). Automatic shape design program for spur gear was developed by Visual LISP, a developer program. Besides, a geometrical method and a mathematical algerian are used in this program. Use frequency of a fine spur gear is on the increase recently and manufacture process of this gear is heat treatment after press processing with molding. In this press processing, the upper punch portion of a fine spur gear shape is drafted by CAM. Therefore, estimated that surface and solid model of spur gear used to CAM are needed in this research. In this research, after 2 ㎜ gear was modeled by auto shape design program, the upper punch portion of a fine spur gear was manufactured as giving third-dimensional model to CAM software and then, displayed the result as applying to press process.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.156-157
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• 2022

The purpose of this study is to propose a 40MPa mortar mixed design model that applies the neural network theory to minimize wasted effort in trial and error. A mixed design model was applied to each of the 60 data using fly ash, blast furnace slag fine powder and thickened rice husk powder. And in the neural network model, the optimized connection weight was obtained by repeatedly applying it to the MATLAB. The completed mixed design model was demonstrated by analyzing and comparing the predicted values of the mixed design model with those measured in the actual compressive strength test. As a result of the mixed design verification experiment, the error rates of the double mixed non-cement mortar using blast furnace slag fine powder and rice husk powder at a height of 40MPa were 3.24% and 3.4%. Mixed with fly ash and rice husk powder had an error rate of 3.94% and 5.8%. The error rate of the triple mixed non-cement mortar of the rice husk powder, fly ash, and blast furnace slag fine powder was 2.5% and 5.1%.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2004년도 총회 및 춘계학술발표회
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• pp.341-345
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• 2004

An attempt has been made in this study to evaluate an applicability of Relative Effective Porosity Model (REPM) as a method for estimating saturated hydraulic conductivity (K$_{s}$) for homogeneous coarse, medium, and fine sands. The saturated hydraulic conductivities obtained from REPM are converted into average linear velocities using Darcy's Law and compared with the results from experimental tracer tests for homogeneous coarse, medium, and fine sand layer. Two types of tracer tests analyses, analytical solution using CXTFIT and moment methods, are performed to obtain reasonable linear velocity range for each layer. For the coarse and medium sands, the converted average linear velocity from REPM is in the velocity range obtained from tracer tests. However, small difference between the results from REPM and tracer tests is found for the fine sands. These results show that REPM gives reasonable estimates of saturated hydraulic conductivity.y.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 동의신경정신과학회지
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• 제19권3호
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• pp.55-68
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• 2008

Background: Microglia produces a barrage of factors (IL-l, TNF-$\alpha$, NO, superoxide) that are toxic to neurons and playa major role in the cellular immune response associated with the pathology of Alzheimer's disease(AD). OJaJiHwangEumJa(OJJHEJ) has been usually used for the treatment of senile disorders. For enhancing efficacy and convenience, the change of the drug delivery device of oriental herbal medicine is required. Objective: This experiment was designed to investigate the effect of the OJJHEJ hot water extract & ultra-fine powder on proinflammatory cytokine of microglia and memory deficit model. Method: The effects of the OJJHEJ hot water extract on production of IL-1$\beta$, IL-6, TNF-$\alpha$, in BV2 microglial cell line treated by lipopolysacchaide(LPS) were investigated. The effects of the OJJHEJ hot water extract & ultra-fine powder on the behavior of the memory deficit mice induced by scopolamine and AChE in serum of the memory deficit mice induced by scopolamine were investigated. Results: 1. The OJJHEJ hot water extract suppressed the production of IL-1$\beta$, IL-6, TNF-$\alpha$ in BV2 microglial cell line and the production of IL-6 was suppressed significantly. 2. The OJJHEJ hot water extract & ultra-fine powder decreased AChE significantly in the serum of the memory deficit mice induced by scopolamine. 3. The OJJHEJ hot water extract & ultra-fine powder groups showed significantly inhibitory effect on the scopolamine-induced impairment of memory in the experiment of Morris water maze. Conclusions: This experiment shows that the OJJHEJ hot water extract & ultra-fine powder might be effective for the prevention and treatment of memory impairment diseases. Investigation into the clinical use of the OJJHEJ hot water extract & ultra-fine powder for Alzheimer's disease is suggested for future research.

• 동의신경정신과학회지
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• 제20권1호
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• pp.43-57
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• 2009

Objectives : This experiment was designed to investigate the effect of the Hwanso-dan hot water extract & ultra-fine powder on microglia and memory deficit model Methods : The effects of the Hwanso-dan hot water extract on expression of IL-l${\beta}$, IL-6, TNF-${\alpha}$ mRNA and production of IL-l${\beta}$, IL-6, TNF-${\alpha}$ in BV2 microglial cell line treated by lipopolysacchaide were investigated. The effects of the Hwanso-dan hot water extract & ultra-fine powder on the behavior of the memory deficit mice induced by scopolamine and uric acid & AChE in serum of the memory deficit mice induced by scopolamine were investigated. Results : 1. The Hwanso-dan hot water extract suppressed the expression of IL-l${\beta}$, IL-6, TNF-${\alpha}$ mRNA in BV2 microglial cell line treated by lipopolysacchaide. 2. The Hwanso-dan hot water extract suppressed the production of IL-l${\beta}$, IL-6, TNF-${\alpha}$ in BV2 microglial cell line. 3. The Hwanso-dan hot water extract & ultra-fine powder decreased uric acid and AChE significantly in the serum of the memory deficit mice induced by scopolamine. 4. The Hwanso-dan hot water extract & ultra-fine powder groups showed significantly inhibitory effect on the scopolamine${\sim}$induced impairment of memory in the experiment of Morris water maze. Conclusions : This experiment shows that the Hwanso-dan hot water extract & ultra-fine powder might be effective for the prevention and treatment of Memory deficit disease. Investigation into the clinical use of the Hwanso-dan hot water extract & ultra-fine powder for Alzheimer's disease is suggested for future research.

• 인터넷정보학회논문지
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• 제24권4호
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• pp.127-135
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• 2023

본 논문에서는 금융 뉴스 데이터로 추가적인 사전 학습이 진행된 BERT 기반 모델인 FinBERT 모델을 사용하여 금융 영역에서 감성 분석 시 학습시킬 데이터와 그에 맞는 하이퍼파라미터를 찾는 방법을 소개한다. 우리의 목표는 다양한 데이터 세트를 활용하고 하이퍼파라미터를 미세 조정하여 정확한 감성 분석을 위해 FinBERT 모델을 가장 잘 활용하는 방법에 대한 포괄적인 가이드를 제공하는 것이다. 이 연구에서는 제안된 FinBERT 모델 미세 조정 접근법의 아키텍처와 워크플로우를 개괄적으로 설명하고, 감성 분석 태스크를 위한 다양한 데이터 세트와 하이퍼파라미터의 성능을 강조한다. 또한, 감성 라벨링 작업에 GPT-3를 사용함으로써 GPT-3가 적절한 라벨러 역할을 하는지에 대한 신뢰성을 검증한다. 결과적으로 미세 조정된 FinBERT 모델이 다양한 데이터 세트에서 우수한 성능을 발휘 한다는 것을 보여주었고, 각 데이터 세트에 대해 전반적으로 우수한 성능을 보이는 학습률 5e-5와 배치 크기 64의 최적의 조합을 찾았다. 또 일반 도메인의 뉴스보다 일반 도메인의 트위터 데이터 세트에서 성능이 크게 향상됨을 기반으로 금융 뉴스 데이터만으로만 추가적으로 학습시키는 FinBERT 모델에 대한 의구심을 제시한다. 이를 통해 FinBERT 모델에 대한 최적의 접근 방식을 결정하는 복잡한 프로세스를 간소화하고 금융 분야 감성 분석 모델을 위한 추가적인 학습 데이터 세트와 미세 조정 시 하이퍼파라미터 선정에 대한 가이드라인을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.