• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1482-1496
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• 1999

기존의 스테레오 정합 알고리즘은 크게 명암기반기법과 특징기반기법의 두 가지로 나눌 수 있다. 그리고, 각 기법은 그들 나름대로의 장단점을 갖는다. 본 논문은 이 두 기법을 결합하는 새로운 알고리즘을 제안한다. 본 논문에서는 물체모델링을 목적으로 하기 때문에 배경을 제거하여 정합하는 방법을 사용한다. 이를 위해, 정합요소들과 정합유사함수가 정의되고, 정합유사함수는 두 기법사이의 장단점을 하나의 인수에 의해 조절한다. 그 외에도 거리차 지도의 오류를 제거하는 coarse-to-fine기법, 폐색문제를 해결하는 다중윈도우 기법을 사용하였고, 물체의 표면형태를 알아내기 위해 morphological closing 연산자를 이용하여 물체와 배경을 분리하는 방법을 제안하였다. 이러한 기법들을 기반으로 하여 여러가지 영상에 대해 실험을 수행하였으며, 그 결과들은 본 논문이 제안하는 기법의 효율성을 보여준다. 정합의 결과로 만들어지는 거리차 지도는 3차원 모델링을 통해 가상공간상에서 보여지도록 하였다.Abstract Classical stereo matching algorithms can be classified into two major areas; intensity-based and feature-based stereo matching. Each technique has advantages and disadvantages. This paper proposes a new algorithm which merges two main matching techniques. Since the goal of our stereo algorithm is in object modeling, we use images for which background is removed. Primitives and a similarity function are defined. The matching similarity function selectively controls the advantages and disadvantages of intensity-based and feature-based matching by a parameter.As an additional matching strategy, a coarse-to-fine method is used to remove a errorneous data on the disparity map. To handle occlusions, multiple windowing method is used. For finding the surface shape of an object, we propose a method that separates an object and the background by a morphological closing operator. All processes have been implemented and tested with various image pairs. The matching results showed the effectiveness of our method. From the disparity map computed by the matching process, 3D modeling is possible. 3D modeling is manipulated by VRML(Virtual Reality Manipulation Language). The results are summarized in a virtual reality space.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.6
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• pp.587-596
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• 2009

The fiber bridging model is the crucial factor to predict or analyze the tensile behavior of fiber reinforced cementitious composites. This paper presents the fiber bridging constitutive law considering the distribution of fiber inclined angle and the number of fibers in engineered cementitious composites. The distribution of fiber inclined angle and the number of fibers are measured and analyzed by the image processing technique. The fiber distribution are considerably different from those obtained by assuming two- or three-dimensional random distributions for the fiber inclined angle. The simulation of the uniaxial tension behavior was performed considering the distribution of fiber inclined angle and number of fibers measured by the sectional image analysis. The simulation results exhibit multiple cracking and strain hardening behavior that correspond well with test results.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.159-159
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• 2015

상수관망시스템의 신뢰도를 정량화하기 위한 연구가 최근 활발히 진행되고 있다. 대부분의 연구는 절점의 공급가능수요량, 절점의 수두, 그리고 유량과 수두를 동시에 고려한 에너지(energy)를 이용하여 신뢰도 지수를 개발하였다. 이 중, Energy를 기반으로 하는 신뢰도지수로써 Resilience index(Todini, 2000), Network Resilience index(Prasad, 2004), Modified Resilience index(Jayaram, 2008)와 Entropy Resilience index(Raad, 2010) 등의 연구가 대표적이다. 상수관망시스템에 정상적인 상황을 넘어서는 부담이 가해졌을 때, 이를 완충하거나, 정상적인 용수공급 상황으로 빠르게 회복하는 능력인 복원력(Resilience)을 판단하기 위한 지표로써 제시된 Todini의 Resilience index를 기점으로, 상수관망의 신뢰도(Reliability)을 Energy 측면에서 판단할 수 있는 관련 지표에 대한 연구가 진행되어왔다. 특히, 상수관망의 최적설계 시, 상수관망의 기능적 요소로써 관련 지표들을 비용(Cost)과 함께 다중목적함수로 고려하는 연구까지 다수 진행되고 있으나, 이러한 지표들이 상수관망에서 핵심적인 몇 가지 요소들의 변화에 대하여 어떻게 반응하는지에 대한 분석은 제대로 이루어지지 않고 있다. 본 연구에서는 동일한 상수관망시스템에서 용수의 흐름상황을 크게 좌우할 수 있을 것으로 판단되는 몇 가지 변동상황을 고려하여 이러한 지표들이 각각의 변동상황에 어느정도 민감하게 반영하는지 분석하였다. 여기서, 상수관망시스템의 중요 변화요소로써 1) 수요량의 변화, 2) Loop 개수의 변화, 3) 수원지(Source) 개수의 변화, 4) 해당시스템의 최소 요구수압 변화 등을 고려하였다. 본 연구를 통해, 상수관망시스템의 신뢰도 산정에 활용되고 있는 기존의 지수들이 다양한 변동 상황을 얼마나 잘 반영할 수 있는지를 파악하고, 기존의 지수들을 보완한 개선된 신뢰도 지수의 개발가능성을 모색하도록 한다.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.17-25
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• 2021

When a person wears clothing, it is important to configure an outfit appropriate to the intended occasion. Therefore, T.P.O(Time, Place, Occasion) of the outfit is considered in various fashion recommendation systems based on artificial intelligence. However, there are few studies that directly infer the T.P.O from outfit images, as the nature of the problem causes multi-label and class imbalance problems, which makes model training challenging. Therefore, in this study, we propose a model that can infer the T.P.O of outfit images by employing a label-distribution-aware margin(LDAM) loss function. Datasets for the model training and evaluation were collected from fashion shopping malls. As a result of measuring performance, it was confirmed that the proposed model showed balanced performance in all T.P.O classes compared to baselines.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.2
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• pp.25-33
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• 2012

A genetic algorithm (GA) has been employed to optimize the major design variables of the liquid rocket engine. Pressure of the main combustion chamber, nozzle expansion ratio and O/F ratio have been selected as design variables. The target engine has the open gas generator cycle using the LO2/RP-1 propellant. The gas properties of the combustion chamber have been obtained from CEA2 and the mass has been estimated using reference data. The objective function has been set as multi-objective function with the specific impulse and thrust to weight ratio using the weight method. The result shows about 4% improvement of the specific impulse and 23% increase of the thrust to weight ratio. The Pareto frontier line has been also obtained for various thrust requirements.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.127-134
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• 2011

A genetic algorithm (GA) has been employed to optimize the major design variables of the liquid rocket engine. Pressure of the main combustion chamber, nozzle expansion ratio and O/F ratio have been selected as design variables. The target engine has the open gas generator cycle using the LO2/RP-1 propellant. The gas properties of the combustion chamber have been obtained from CEA2 and the mass has been estimated using reference data. The objective function has been set as multi-objective function with the specific impulse and thrust to weight ratio using the weight method. The result shows about 4% improvement of the specific impulse and 23% increase of the thrust to weight ratio. The Pareto frontier line has been also obtained for various thrust requirements.

• Proceedings of the Population Association of Korea Conference
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• 2006.12a
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• pp.193-230
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• 2006

본 논문은 국가별 추계인구와 미래 9개년 인구관련 설명변수들의 추정값을 통하여 적정인구 모형을 구형하고 한국의 적정인구를 추정하는데 목적이 있다. 후생을 고려한 생산함수 확장모형, 세계 176개국의 표본자료 그리고 다중회귀분석을 이용하여 $2000{\sim}2300$년 한국의 적정인구를 추정하고 그 추세곡선을 나타내는 것이다. 모형의 종속변수는 UN에 의한 세계각국의 추계인구이고, 설명변수는 9개 변수 즉 PPP GDP, 인접지역 경제통합율, 교육수준, 영어구사비율, 국토유효면적, 에너지량, 기온, 수자원량, 무역거리이다. 연구결과는 다음과 같다. $2000{\sim}2300$년 한국의 적정인구는 $4,350{\sim}4,950$만명으로 추정되며, 2000년${\sim}$2050년은 $4,700{\sim}5,010$만명, 2050년${\sim}2100$년은 $4,770{\sim}4,400$만명이 된다. 2125년 최저점 4,350만명을 통과한 후 점진적으로 2300년의 4,870만명으로 접근해 갈 것으로 추정된다. 연구결과의 시사점은 네 가지이다. 첫째 한국의 적정인구가 2125년을 기준으로 이전은 감소 이후는 증가 추세일 것이므로 정책결정시 적정한 목표인구를 설정해야 한다는 점이다. 둘째 현 추세로 진행되면 2050년 이후 적정인구가 추계인구보다 $500{\sim}600$만명 더 많아진다는 점이다. 셋째 2125년 이전의 적정인구는 좁게 $4,770{\sim}4,545$만명으로서, 출산율 향상을 통한 적정인구의 유지 노력이 필요하다는 점이다. 넷째 적정인구 추세의 기복은 출산기피로 인한 인구감소 때문으로 출산지원과 입양 및 이민 유입의 정책에 따라 변화될 수 있다는 점이다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.769-781
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• 2007

Factor analysis has been usually employed in reducing quantity of data and summarizing information on a system or phenomenon. In this analysis methodology, variables are grouped into several factors by consideration of statistic characteristics, and the results are used for dropping variables which have lower weight than others. In this study, factor analysis was applied for extracting primary factors influencing multi-dam system operation in the Han River basin, where there are two multi-purpose dams such as Soyanggang Dam and Chungju Dam, and water has been supplied by integrating two dams in water use season. In order to fulfill factor analysis, first the variables related to two dams operation were gathered and divided into five groups (Soyanggang Dam: inflow, hydropower product, storage management, storage, and operation results of the past; Chungju Dam: inflow, hydropower product, water demand, storage, and operation results of the past). And then, considering statistic properties, in the gathered variables, some variables were chosen and grouped into five factors; hydrological condition, dam operation of the past, dam operation at normal season, water demand, and downstream dam operation. In order to check the appropriateness and applicability of factors, a multiple regression equation was newly constructed using factors as description variables, and those factors were compared with terms of objective function used in operation water resources optimally in a river basin. Reviewing the results through two check processes, it was revealed that the suggested approach provided satisfactory results. And, it was expected for extracted primary factors to be useful for making dam operation schedule considering the future situation and previous results.

• Investigative Magnetic Resonance Imaging
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• v.2 no.1
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• pp.58-66
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• 1998

Purpose : This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. Material and Methods : The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier. Results : MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is $E^{3}$ space rather thatn the conventional $E^{2}$ space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification. Conclusion : We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.