• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5643-5646
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• 2012

Background and Objectives: Increase of mortality rates of gastric cancer in Iran and the world in recent years reveal necessity of studies on this disease. Here, hazard function for gastric cancer patients was estimated using Wavelet and Kernel methods and some related factors were assessed. Materials and Methods: Ninety-five gastric cancer patients in Fayazbakhsh Hospital between 1996 and 2003 were studied. The effects of age of patients, gender, stage of disease and treatment method on patient's lifetime were assessed. For data analyses, survival analyses using Wavelet method and Log-rank test in R software were used. Results: Nearly 25.3% of patients were female. Fourteen percent had surgery treatment and the rest had treatment without surgery. Three fourths died and the rest were censored. Almost 9.5% of patients were in early stages of the disease, 53.7% in locally advance stage and 36.8% in metastatic stage. Hazard function estimation with the wavelet method showed significant difference for stages of disease (P<0.001) and did not reveal any significant difference for age, gender and treatment method. Conclusion: Only stage of disease had effects on hazard and most patients were diagnosed in late stages of disease, which is possibly one of the most reasons for high hazard rate and low survival. Therefore, it seems to be necessary a public education about symptoms of disease by media and regular tests and screening for early diagnosis.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.85-94
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• 2010

This paper introduces a novel way to leverage convolution parse tree kernel to extract the interaction information between two proteins in a sentence without multiple features, clues and complicated kernels. Our approach needs only the parse tree alone of a candidate sentence including pairs of protein names which is potential to have interaction information. The main contribution of this paper is two folds. First, we show that for the PPI, it is imperative to execute parse tree pruning removing unnecessary context information in deciding whether the current sentence imposes interaction information between proteins by comparing with the latest existing approaches' performance. Secondly, this paper presents that tree kernel decay factor can play an pivotal role in improving the extraction performance with the identical learning conditions. Consequently, we could witness that it is not always the case that multiple kernels with multiple parsers perform better than each kernels alone for PPI extraction, which has been argued in the previous research by presenting our out-performed experimental results compared to the two existing methods by 19.8% and 14% respectively.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.255-264
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• 2008

It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.

• Journal of Ship and Ocean Technology
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• v.11 no.4
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• pp.29-54
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• 2007

Systematic sensitivity analysis of smoothed particle hydrodynamics method (SPH), a gridless Lagrangian particle method, was carried out in this study. Unlike traditional grid-based numerical schemes, systematic sensitivity study for computational parameters is very limited for SPH. In this study, the effect of computational parameters in SPH simulation is explored through two-dimensional dam-breaking and sloshing problem. The parameters to be considered are the speed of sound, the type of kernel function, the frequency of density re-initialization, particle number, smoothing length and pressure extraction method. Through a series of numerical test, detailed information was obtained about how SPH solution can be more stabilized and improved by adjusting computational parameters.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.511-521
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• 2020

In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.796-798
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• 2005

본 연구에서는 엔트로피 이론을 사용하여 ICA(Independent Component Analysis) 점수함수를 생성하는 새로운 밀도추정자(Density Estimator)를 제안한다. 원 신호에 대한 밀도함수의 추정은 적당한 점수함수를 생성하기 위해 필요하고, 미분 가능한 밀도함수인 커널을 이용한 밀도추정법(Kernel Density Estimation)을 이용하여 점수함수를 생성하였다. 보다 빠른 점수함수의 생성을 위해서 식의 형태를 convolution 형태로 표현하였으며, ICA 학습을 위해서 결합엔트로피를 최대화(Joint Entropy Maximization)하는 방향으로 커널의 폭을 학습하였다. 이를 위해서 기울기 강하법(Gradient descent method)를 사용하였으며, 이러한 제약 사항은 새로운 밀도 추정자를 설계하기 위한 기본적인 개념을 나타낸다. 실험결과, 커널의 폭을 담당하는 smoothing parameters들이 일정한 값으로 학습함을 알 수 있었다.

• Smart Media Journal
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• v.4 no.3
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.387-390
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• 2024

본 논문에서는 효율적인 2차 오차 함수를 이용하여 입자 기반에서 EMC(Extended Marching Cubes) 알고리즘을 구현할 수 있는 새로운 알고리즘을 제안한다. Smoothing 커널(Kernels)을 통해 계산한 입자 평균 위치에서 레벨셋(Level-set)을 계산해 스칼라장을 구축한다. 그리고 난 뒤 SPH(Smoothed particle hydrodynamics)기반의 커널을 통해 밀도, 입자 평균 위치를 계산한다. 스칼라장을 이용해 등가 곡면(Isosurface)을 찾고 음함수로 표현된 표면을 구성한다. SPH 커널을 공간에서 미분하면 공간상의 어느 위치에서나 기울기를 계산할 수 있고, 이를 통해 얻어진 법선벡터를 이용하여 일반적인 EMC나 DC(Dual contouring)에서 사용하는 2차 오차 함수를 효율적으로 설계한다. 결과적으로 제안하는 방법은 메쉬와 같이 연결정보다 없는 입자 기반 데이터에서도 EMC 알고리즘을 구현하여 볼륨(Volume) 손실을 줄이고, 복잡한 음함수 표면을 표현할 수 있게 한다.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.9-20
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• 2010

In order to visualize participating media in 3D space, they usually calculate the incoming radiance by subdividing the ray path into small subintervals, and accumulating their respective light energy due to direct illumination, scattering, absorption, and emission. Among these light phenomena, scattering behaves in very complicated manner in 3D space, often requiring a great deal of simulation efforts. To effectively simulate the light scattering effect, several approximation techniques have been proposed. Volume photon mapping takes a simple approach where the light scattering phenomenon is represented in volume photon map through a stochastic simulation, and the stored information is explored in the rendering stage. While effective, this method has a problem that the number of necessary photons increases very fast when a higher variance reduction is needed. In an attempt to resolve such problem, we propose a different approach for rendering particle-based volume data where kernel smoothing, one of several density estimation methods, is explored to represent and reconstruct the light in-scattering effect. The effectiveness of the presented technique is demonstrated with several examples of volume data.

• Fibers and Polymers
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• v.2 no.3
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• pp.164-170
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• 2001

Authors studied on the applicability of image analysis technique using a scanner with a CCD (charged coupled deviced) to the evaluation of evenness of nonwovens because it has distinctive features to considerably save time and labor in the analysis compared with other classical methods. As specimens fur the experiment, two different types that are unpatterned and patterned ones were prepared. For the unpatterned specimen, webs were chemically bonded, while for the patterned specimen, webs being thermally calendered with engraved roller. Several webs having various areal densities were prepared and bonded. Coefficient of variation (CV%) was used as a parameter to evaluate the evenness. Scanning conditions could be suitably set up through comparing the total variance to the between-group variance and to the within-group variance, respectively, on the images scanned at the different conditions. The 2D convolution method with smoothing filter kernel was introduced to further filter the noises on the scanned images. After the filtering process, the increase of web areal densities gave an uniform decrease of the CV%. This showed that the scanned image analysis with proper filtering process could be successfully applicable to the evaluation of evenness in nonwovens.