• Title/Summary/Keyword: 모델 축소

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Perceptual Model-Based Image Transcoding for UMA (지각도 모델에 근거한 UMA를 위한 영상 변환 기법)

  • 이건섭;김유남;설상훈
    • Proceedings of the Korean Information Science Society Conference
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    • 2000.10b
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    • pp.356-358
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    • 2000
  • 본 논문에서는 인간의 시각적인 감각을 멀티미디어 컨텐츠의 UMA 변환 서비스에 적용하여 영상의 다양한 디스플레이 크기의 사용자 단말기에 맞게 효율적으로 변화(해상도 축소나 Cropping) 기법을 제안하고 있다. 즉, 영상의 중요한 객체를 사각형 경계박스로 표시한 후 각각의 객체의 저자의 의도대로 사용자가 지각적으로 인식할 수 있는 최소의 공간 해상도 축소 정보를 정의하여, 영상의 변환 시 각각 객체를 사용자가 충분히 인식할 수 있는 한계치로 사용하여 효율적인 UMA 서비스를 보장하는 사용자 자원 재분배 기법을 제안한다. 또한, 본 논문에서 제안된 알고리즘을 기존의 방식과 비교하여 실험적으로 그 장단점을 비교한다.

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액체금속로용 축소규모 면진베어링 특성시험고찰 및 적용예

  • Yoo, Bong;Lee, Jae-Han;Koo, Kyung-Hoe
    • Proceedings of the Korean Nuclear Society Conference
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    • 1997.05b
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    • pp.520-525
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    • 1997
  • 액체금속로의 안전성과 경제성을 향상시키는데 중요한 요소로 부각된 면진설계기술을 개발하기 위하여 고감쇠 면진베어링의 축소모델을 제작하고, 성능확인을 위한 다양한 시험을 실시하였다. 면진베어링의 성능을 나타내는 전단강성, 감쇠특성, 항복하중특성값, 전단변형능력 등에 대한 시험결과 전단강성은 목표값에 비하여 작았지만 감쇠값과 전단변형은 목표값에 근접하였다. 이를 이용한 면진 원자로건물의 지진응답을 분석한 결과 면진베어링은 건물의 지진응답 가속도를 대폭 줄여주는 것으로 나타났다.

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A Re-Ranking Retrieval Model based on Two-Level Similarity Relation Matrices (2단계 유사관계 행렬을 기반으로 한 순위 재조정 검색 모델)

  • 이기영;은희주;김용성
    • Journal of KIISE:Software and Applications
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    • v.31 no.11
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    • pp.1519-1533
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    • 2004
  • When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively importance degree. Furthermore, we perform a cluster retrieval to reflect the user's Query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. We have proven the performance of a proposed re-ranking model based on the similarity union of the fuzzy retrieval model and the document cluster retrieval model.

A Study on the Accuracy of CFD Prediction for Small Scaled 4 Nozzle Clustered Engine Using Air (공기를 이용한 축소형 4노즐 클러스터드 엔진 저부 유동의 CFD 해석 검증)

  • Kim, Seong-Lyong;Kim, In-Sun
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2011.11a
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    • pp.78-84
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    • 2011
  • CFD simulation has been conducted on a small scaled 4 nozzle clustered engine operating with air. In the present paper, the effects of grid size, turbulence models, flux difference methods have been compared. The results show that the base flows are somewhat different as the turbulence models, while Roe and AUSM flux differences produced almost the same results. Spalart-Allmaras turbulence model produces more accurate results rather than famous SST k-w model. The calculated Mach number and pressure profile in the engine base reveal the complex base flow structure, which is somewhat different from the generally estimated flow fields.

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Seismic Behavior and Performance Assesment of a One-story Building with a Flexible Diaphragm (유연한 지붕으로 된 단층 구조물의 지진 거동과 성능 분석)

  • ;;Donald W. White
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.16 no.4
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    • pp.377-386
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    • 2003
  • The proposed simplified MDOF model is applied to a half-scale single-story reinforced masonry test building with a single diagonally-sheathed diaphragm. Comparisons of analytical studies to experimental tests can be valuable for understanding the seismic response of these types of buildings and for determining the qualities and limitations of the simplified models. A model calibration process is performed in this paper to determine the required structural properties based on the elastic and inelastic test responses for test building. This approach is necessary since established methods to determine the in-plane and out-of-plane stiffness, strength, and hysteresis do not exist.

Performance Enhancement of Deep Learning-based Super-Resolution by Adjustment of Training Dataset (훈련 데이터세트의 조절을 통한 딥러닝 기반 Super-Resolution 의 성능 향상)

  • Kwon, Ki-Taek;Seo, Young-Ho
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • fall
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    • pp.218-220
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    • 2021
  • 본 논문에서는 CAR(content adaptive resampler)로 축소된 저해상도 이미지를 직접 다른 모델에 여러가지 방식으로 훈련을 시켜 성능을 개선시키고자 하였다. 본 논문에서는 단일 영상 super resolution 에 관하여 여러 기술이 존재하는 상황에 더 나은 기술을 테스트하려 하고 그를 위해 과거의 모델들에 대한 이해가 필요하여 이를 구현하였다. 현재 가장 뛰어난 성능을 보이고 있는 모델 중의 하나인 CAR 에서 복원 전 이미지를 사용하여 훈련을 시키면 더 나은 성능의 모델을 만들 수 있을 것이라고 가정하고 다양한 훈련을 통해 성능을 개선시키고자 하였다.

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A study on the comparison of descriptive variables reduction methods in decision tree induction: A case of prediction models of pension insurance in life insurance company (생명보험사의 개인연금 보험예측 사례를 통해서 본 의사결정나무 분석의 설명변수 축소에 관한 비교 연구)

  • Lee, Yong-Goo;Hur, Joon
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.179-190
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    • 2009
  • In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.

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The Numerical Study on the Ventilation of Non-isothermal Concentrated Fume (수치해석적 방법을 이용한 비등온 고농도 연무의 배기량 산정에 관한 연구)

  • Lim, Seok-Chai;Chang, Hyuk-Sang;Ha, Ji-Soo
    • Journal of Korean Society of Environmental Engineers
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    • v.30 no.5
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    • pp.534-543
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    • 2008
  • The experimental study with the prototype provides more acceptable data than the others. But there are so many limited conditions to perform the experimental study with the prototype. So the theoretical similitude with the scaled model and the numerical study with the CFD method have been chosen alternatively to analysis the fume movement. In this study, the ventilation was estimated from the results of the numerical study based on the experimental results as the boundary conditions. The grid A and B were same size and shape with the models which was used in the experimental study and consisted with 163,839, 122,965 cells respectively. The height of the fume layer was estimated form the mole fraction of fume components and the ventilation was determined by the velocity and temperature of the fume. The results of this study showed that the fume movements estimated from the numerical study are enough to apply to the prototype if there are proper heat loss correction factors. The numerical study is easier to change study conditions and faster to get results from the study than the experimental study. So if we find some proper heat loss correction factors, it's possible to execute the various and advanced study with the numerical study.

The Impact of the PCA Dimensionality Reduction for CNN based Hyperspectral Image Classification (CNN 기반 초분광 영상 분류를 위한 PCA 차원축소의 영향 분석)

  • Kwak, Taehong;Song, Ahram;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.35 no.6_1
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    • pp.959-971
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    • 2019
  • CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

A Model Reduction Method for Effective Analysis of Structures (구조물의 효율적인 해석을 위한 모델 축소기법 연구)

  • Park, Young-Chang;Hwang, Jai-Hyuk
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.14 no.1
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    • pp.28-35
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    • 2006
  • Substructure coupling or component mode synthesis may be employed in the solution of dynamic problems for large, flexible structures. The model is partitioned into several subdomains, and a generalized Craig-Bampton representation is derived. In this paper the mode sets (normal modes, constraint modes) is employed for model reduction. A generalized model reduction procedure is described. Vaious reduction methods that use constraint modes is described in detail. As examples, a flexible structure and a 10 DOF damped system are analyzed. Comparison with a conventional reduction method based on a complete model is made via eigenpair and dynamic responses.

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