• 천문학회보
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• 제42권2호
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• pp.78.3-78.3
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• 2017

The positions of galaxies in comoving Cartesian space varies under different cosmological parameter choices, inducing a redshift-dependent scaling in the galaxy distribution. The shape of the two-point correlation of galaxies exhibits a significant redshift evolution when the galaxy sample is analyzed under a cosmology differing from the true, simulated one. In our previous works, we can made use of this geometrical distortion to constrain the values of cosmological parameters governing the expansion history of the universe. This current work is a continuation of our previous works as a strategy to constrain cosmological parameters using redshift-invariant physical quantities. We now aim to understand the redshift evolution of the full shape of the small scale, anisotropic galaxy clustering and give a firmer theoretical footing to our previous works.

• 대한기계학회논문집A
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• 제25권11호
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• pp.1796-1801
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• 2001

For micro-machines, very few design methodologies based on optimization hale been developed so far. To overcome the difficulties of design optimization of micro-machines, the search method for multi-dimensional design window (DW)s is proposed. The proposed method is defined as areas of satisfactory design solutions in a design parameter space, using both continuous evolutionary algorithms (CEA) and the modified K-means clustering algorithm . To demonstrate practical performance of the proposed method, it was applied to an optimal shape design of micro electrostatic actuator of optical memory. The shape design problem has 5 design parameters and 5 objective functions, and finally shows 4 specific design shapes and design characters based on the proposed DWs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4933-4956
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• 2016

Use-After-Free (UAF) is a common lethal form of software vulnerability. By using tools such as Web Browser Fuzzing, a large amount of samples containing UAF vulnerabilities can be generated. To evaluate the threat level of vulnerability or to patch the vulnerabilities, automatic deduplication and exploitability determination should be carried out for these samples. There are some problems existing in current methods, including inadequate pertinence, lack of depth and precision of analysis, high time cost, and low accuracy. In this paper, in terms of key dangling pointer and crash context, we analyze four properties of similar samples of UAF vulnerability, explore the method of extracting and calculate clustering eigenvalues from these samples, perform clustering by fast search and find of density peaks on a large number of vulnerability samples. Samples were divided into different UAF vulnerability categories according to the clustering results, and the exploitability of these UAF vulnerabilities was determined by observing the shape of class cluster. Experimental results showed that the approach was applicable to the deduplication and exploitability determination of a large amount of UAF vulnerability samples, with high accuracy and low performance cost.

• 한국전자거래학회지
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• 제27권2호
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• pp.193-204
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• 2022

스마트교통카드 데이터는 대표적인 모빌리티 데이터로 이를 이용하여 대중교통 이용행태를 분석하고 정책 개발에 활용할 수 있다. 본 논문은 이러한 연구의 하나로 전철 이용패턴을 이용하여 전철역들을 분류하는 문제를 다룬다. 전철역의 클러스터링을 다룬 기존 논문들은 이용행태 중 통행량만을 고려하였기에 본 논문은 이에 대한 보완적인 방법의 하나로 통행시간을 고려한 클러스터링을 제안한다. 각 역의 승객들을 출근 시간 출발, 출근 시간 도착, 퇴근 시간 출발, 퇴근 시간 도착 승객들로 분류한 다음 각각의 통행시간을 와이블 분포로 모형화하여 추정한 형상모수를 역의 특성값으로 정의하였다. 그리고 특성 벡터들을 K-평균 클러스터링 기법을 사용하여 클러스터링하였다. 실험결과 통행시간을 고려하여 역의 클러스터링을 수행하면 기존 연구의 클러스터링 결과와 유사한 결과가 나올 뿐만 아니라 더 세분화 된 클러스터링이 가능함을 관찰하였다.

• 한국측량학회지
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• 제25권6_1호
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• pp.507-516
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• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.24-29
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• 2016

One of the main applications of digital image processing is the estimation of the number of certain types of objects (cells, seeds, peoples etc.) in an image. Difficulties of these counting problems depends on various factors including shape and size variation, degree of object clustering, contrast between object and background, object texture and its variation, and so on. In this paper, a new automatic colony counting algorithm is proposed. We focused on the two applications: counting the bacteria colonies on the agar plate and estimating the number of seeds from images captured by smartphone camera. To overcome the shape and size variations of the colonies, we adopted the distance transformation and peak detection approach. To estimate the reference size of the colony robustly, we also used k-means clustering algorithm. Experimental results show that our method works well in real world applications.

• 한국기계가공학회지
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• 제20권1호
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• pp.95-102
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• 2021

Analysis of the fracture surface is one of the most important methods for determining the cause of equipment structural failure. Whether structural failure is caused by impact or fatigue is necessary information in industrial fields. For ferrous and non-ferrous metal materials, two fracture phenomena are generated on the fracture surface: ductile and brittle fractures. In this study, machine learning predicts whether the fracture is based on ductile or brittle when structurural failure is caused by impact. The K-means algorithm calculates this ratio by clustering the brittle and ductile fracture data from a photograph of the impact fracture surface, unlike the existing method, which calculates the fracture surface ratio by comparison with the grid type or the reference fracture surface shape.

• 식물분류학회지
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• 제48권3호
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• pp.153-162
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• 2018

Lespedeza maximowiczii C. K. Schneid. (Fabaceae) is a deciduous shrub which is known to be distributed in the temperate forests of China, Korea and on Tsushima Island of Japan. Due to severe morphological variations within species, numerous examinations have been conducted for Korean L. maximowiczii. However, the morphology of Chinese plants has not been studied as thoroughly, despite doubts about their taxonomy. To clarify this taxonomic issue, we investigated morphological characters and undertook a Bayesian clustering analysis with microsatellite markers. The morphological and genetic traits of Chinese individuals varied considerably from those of typical L. maximowiczii growing in Korea. For example, petals of the former had a different shape and bore long claws, while the calyx lobes were diverged above the middle and the upper surface of the leaflet was pubescent. Their terete buds and spirally arranged bud scales were distinct from those within the series/section Heterolespedeza, which includes L. maximowiczii. Our Bayesian clustering analysis additionally included L. buergeri as an outgroup. Those results indicated that the Chinese samples clustered into a lineage separated from L. maximowiczii (optimum cluster, K = 2), despite the fact that the latter is grouped into the same lineage with L. buergeri. Therefore, we treat those Chinese plants as a new species with the name L. pseudomaximowiczii.

• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1584-1595
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• 2017

Each cultural community has developed a variety of fonts to express their own language and characters. Hangul has also diversified its font shapes through changing the composition ratio and look of the consonants and vowels. Rather, thanks to the variety of these fonts, a considerable amount of time and effort must be devoted to the selection of a specific font shape. This is related to the fact that the current Hangul service and classification system process the font only with its name or the name of the manufacturer. It means that there is no consensus about the font shape classification system for Hangul. In this study, we propose a shape property set that can be a basis for classifying Hangul fonts. The font shape property set was generated by performing statistical analysis with features which have been studied by the font design experts and was verified through questionnaire using representative fonts based on the classification scheme defined by the Hangul font design classification system standard. This study is meaningful in that it is a study on shape classification properties of K-means and PCA statistical techniques based on font data rather than design field study.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.