• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.167-170
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• 2007

본 논문에서는 텍스춰매핑, 재메쉬화, 메쉬의 단순화와 모핑 및 압축 등 다양한 분야에 적용되는 메쉬분할 문제를 다룬다. 메쉬분할은 주어진 삼차원 메쉬를 서로 떨어진 집합(disjoint sets)으로 분할하는 것으로서 여러 연구자들에 의해 많은 연구 결과들이 제시되어 왔다. 본 논문에서는 삼차원 메쉬가 가지고 있는 기하학적 특성을 고려하여 메쉬를 분할하는 방법을 제시하고자 한다. 먼저 메쉬의 국부적 기하 특성인 곡률 정보와 전역적 기하 특성인 볼록성을 이용하여 삼차원 메쉬를 구성하는 첨예정점을 추출하였고, 이들간의 거리 정보를 이용하여 이 첨예정점들을 군집화(clustering)하였다. 최종 메쉬분할을 위해 분할된 첨예정점에 속하지 않는 나머지 정점들에 대해 거리 정보를 이용하여 군집화를 수행하였다. 본 논문에서 제안한 메쉬분할 방법을 검증하기 위해 벤치마크로 공개된 여러 메쉬 모델에 대해 실험하여 그 결과를 보여주었다.

• Annual Conference of KIPS
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• 2010.11a
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• pp.471-473
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• 2010

본 논문은 선형회귀(LR: Linear Regression)와 국부적인 방사기저함수 네트워크(RBFN: Radial Basis Function Networks)를 결합한 점진적인 모델(incremental model)의 설계와 관련되어진다. 전형적인 RBFN에 의한 모델링과는 달리, 제안된 방법의 근본적인 원리는 두 단계에 의해 고려되어진다. 첫째, 전체 모델의 설계과정에서 전역적인 모델로써 선형회귀에 의해 데이터의 선형부분을 구축한다. 다음으로, 모델링 오차는 오차가 존재하는 국부적인 공간에서 RBFN에 의해 보상되어진다. 여기서, 오차의 분포로부터 RBFN을 설계하기 위해 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)를 통해 정보입자의 형태로 구축되어진다. 실험은 자동차 mpg 연료소비량 예측과 부동산 가격예측문제를 통해 제안된 방법의 우수성을 증명한다.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.47.3-47.3
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• 2021

In this talk I will review recent progress that the SDSS-IV / eBOSS collaboration has made in constraining cosmology from the clustering of galaxies, quasars and the Lyman-alpha forest. The SDSS-IV / eBOSS collaboration has measured the baryon acoustic oscillation (BAO) and redshift space distortion (RSD) features in the correlation function in redshift bins from z~0.15 to z~2.33. These features constitute measurements of angular diameter distances, Hubble distances, and growth rate measurements. A number of consistency tests have been performed between the BAO and RSD datasets and additional cosmological datasets such as the Planck cosmic microwave background constraints, the Pantheon Type Ia supernova compilation, and the weak lensing results from the Dark Energy Survey. Taken together, these joint constraints all point to a broad consistency with the standard model of cosmology LCDM + GR, though they remain in tension with local measurements of the Hubble parameter.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.66-77
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• 2011

We propose a framework for grouping photographic advertising images that employs a hierarchical indexing scheme based on efficient feature combinations. The study provides one specific application of effective tools for monitoring photographic advertising information through online and offline channels. Specifically, it develops a preprocessor for advertising image information tracking. We consider both global features that contain general information on the overall image and local features that are based on local image characteristics. The developed local features are invariant under image rotation and scale, the addition of noise, and change in illumination. Thus, they successfully achieve reliable matching between different views of a scene across affine transformations and exhibit high accuracy in the search for matched pairs of identical images. The method works with global features in advance to organize coarse clusters that consist of several image groups among the image data and then executes fine matching with local features within each cluster to construct elaborate clusters that are separated by identical image groups. In order to decrease the computational time, we apply a conventional clustering method to group images together that are similar in their global characteristics in order to overcome the drawback of excessive time for fine matching time by using local features between identical images.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• Journal of Korea Game Society
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• v.9 no.1
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• pp.105-114
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• 2009

In the game of Go, the life-and-death problem is a fundamental problem to be definitely overcome when implementing a computer Go program. To solve local Go problems such as life-and-death problems, an important consideration is how to tackle the game tree's huge branching factor and its depth. The basic idea of the experiment conducted in this article is that we modelled the human behavior to get the recognized first moves to kill the surrounded group. In the game of Go, similar life-and-death problems(patterns) often have similar solutions. To categorize similar patterns, we implemented Kohonen Neural Network(KNN) based clustering and found that the experimental result is promising and thus can compete with a pattern matching method, that uses supervised learning with a neural network, for solving life-and-death problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.15-21
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• 2009

The goal of wireless sensor networks is to collect sensing data on specific region over wireless communication. Sink node gathers all local sensing data, processes and transmits them to users who use sensor networks. Generally, senor nodes are low-cost, low power devices with limited sensing, computation and wireless communication capabilities. And sensor network applies to multi-hop communication on large-scale network. As neighboring sensor nodes have similar data, clustering is more effective technique for 'data-aggregation'. In cluster formation technique based on multi-hop, it is necessary that the number of cluster member nodes should be distributed equally because of the balance of cluster formation To achieve this, we propose a method to customize cluster member nodes for energy-efficiency in wireless sensor networks.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1044-1056
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• 2013

This paper presents an approach for detecting and measuring human skin pigmentation. In the proposed scheme, we extract a skin area by a GMM-EM clustering based skin color model that is estimated from the statistical analysis of training images and remove tiny noises through the morphology processing. A skin area is decomposed into two components of hemoglobin and melanin by an independent component analysis (ICA) algorithm. Then, we calculate the intensities of hemoglobin and melanin by using the projection transformed block coefficient and determine the existence of skin pigmentation according to the global and local distribution of two intensities. Furthermore, we measure the area and density of the detected skin pigmentation. Experimental results verified that our scheme can both detect the skin pigmentation and measure the quantity of that and also our scheme takes less time because of the location histogram.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).