• International Journal of CAD/CAM
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• v.7 no.1
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• pp.103-112
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• 2007

This paper presents a practical approach for surface reconstruction from unoriented point clouds. Instead of estimating local surface orientation, we first generate a set of depth images from the input point clouds, and a coarse mesh is then generated based on them by space carving techniques. The resultant mesh is progressively refined by local mesh refinement and optimization according to surface distance measure. A manifold mesh approximating the input points within an given tolerance is finally obtained. Our approach is easy to implement, but has the ability to outputs high quality meshes in different resolutions. We show that the proposed approach is not sensitive to several types of data disfigurement and is able to reconstruct models robustly from variance input data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.171-181
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• 2002

As the result of the analysis of the underground utility map made before, we could have divided the kind of errors into the two types, position error of geometric data and attribute error and knew the fact that mainly position error is derived from the process of detecting and surveying the underground utility, and attribute is inputting data. In this study we showed the way improving accuracy in detecting and surveying underground utility by field test, and the way to reduce attribute error by process refinement.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.38.1-38.1
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• 2018

A merging galaxy cluster is a useful laboratory to study many interesting astrophysical processes such as intracluster medium heating, particle acceleration, and possibly dark matter self-interaction. However, without understanding the merger scenario of the system, interpretation of the observational data is severely limited. In this work, we focus on the off-axis binary cluster merger Abell 115, which possesses many remarkable features. The cluster has two cool cores in X-ray with disturbed morphologies and a single giant radio relic just north of the northern X-ray peak. In addition, there is a large discrepancy (almost a factor of 10) in mass estimate between weak lensing and dynamical analyses. To constrain the merger scenario, we perform a hydrodynamical simulation with the adaptive mesh refinement code RAMSES. We use the multi-wavelength observational data including X-ray, weak-lensing, radio, and optical spectroscopy to constrain the merger scenario. We present detailed comparisons between the simulation results and these multi-wavelength observations.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.127-150
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• 2022

We discuss multiple change-point estimation as edge detection in piecewise smooth functions with finitely many jump discontinuities. In this paper we propose change-point estimators using concentration kernels with Fourier coefficients. The change-points can be located via the signal based on Fourier transformation system. This method yields location and amplitude of the change-points with refinement via concentration kernels. We prove that, in an appropriate asymptotic framework, this method provides consistent estimators of change-points with an almost optimal rate. In a simulation study the proposed change-point estimators are compared and discussed. Applications of the proposed methods are provided with Nile flow data and daily won-dollar exchange rate data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.380-389
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• 2021

This study aimed to reveal the perceptions and trends of digital fashion technology through an informational approach. A big data analysis was conducted after collecting the text shown in a web environment from April 2019 to April 2021. Key words were derived through text mining analysis and network analysis, and the structure of perception of digital fashion technology was identified. Using textoms, we collected 8144 texts after data refinement, conducted a frequency of emergence and central component analysis, and visualized the results with word cloud and N-gram. The frequency of appearance also generated matrices with the top 70 words, and a structural equivalent analysis was performed. The results were presented with network visualizations and dendrograms. Fashion, digital, and technology were the most frequently mentioned topics, and the frequencies of platform, digital transformation, and start-ups were also high. Through clustering, four clusters of marketing were formed using fashion, digital technology, startups, and augmented reality/virtual reality technology. Future research on startups and smart factories with technologies based on stable platforms is needed. The results of this study contribute to increasing the fashion industry's knowledge on digital fashion technology and can be used as a foundational study for the development of research on related topics.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.85-90
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• 2022

In this study, keywords from representative online portal sites such as NAVER, Google, and Youtube were collected based on text mining analysis technique using TEXTOM to check the changes in the restaurant industry before and after COVID-19. The collection keywords were selected as dining out, food service industry, and dining out culture. For the collected data, the top 30 words were derived, respectively, through the refinement process. In addition, comparative analysis was conducted by defining data from 2018 to 2019 before COVID-19, and from 2020 to 2021 after COVID-19. As a result, 8272 keywords before COVID-19 and 9654 keywords after COVID-19, a total of 17926 keywords, were derived. In order for the food service industry to develop after the COVID-19 pandemic, it is necessary to commercialize the recipes of restaurants to revitalize the distribution of home-use food products that replace home-cooked meals such as meal kits. Due to the social distancing caused by COVID-19, the dining out culture has changed and the trend has changed, and it has been confirmed that the consumption culture has changed to eating and delivering at home more safely than visiting restaurants. In addition, it has been confirmed that the consumption culture of existing consumers is changing to a trend of cooking at home rather than visiting restaurants.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.293-296
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• 2001

The dynamic softening mechanisms of AISI 316, AISI 304 and AISI 430 stainless steels were studied with torsion test in the temperature range of $900 - 1200^{\circ}C$ and the strain rate range of $5.0x10^{-2}-5.0x10^0/sec$. The austenitic stainless steels, such as AISI 316 and AISI 304 were softened by dynamic recrystallization (DRX) during hot deformation. Also, the evolutions of flow stress and microstructure of AISI 430 ferritic stainless steel show the characteristics of continuous dynamic recrystallization (CDRX). To establish the quantitative equations for DRX of AISI 316 stainless steel, the evolution of flow stress curve with strain was analyzed. The critical strain (${\varepsilon}_c$) and strain for maximum softening rate (${\varepsilon}^{*}$) could be confirmed by the analysis of work hardening rate ($d{\sigma}/d{\varepsilon}={\theta}$). The volume fraction of dynamic recrystallization ($X_{DRX}$) as a function of processing variables, such as strain rate ( $\varepsilon$ ), temperature (T), and strain ( $\varepsilon$ ) were established using the ${\epsilon}_c$ and ${\varepsilon}^{*}$. For the exact prediction the ${\varepsilon}_c,\;{\varepsilon}^{*}$ and Avrami' exponent (m') were quantitatively expressed by dimensionless parameter, Z/A, respectively. It was found that the calculated results were agreed with the experimental data for the steels at my deformation conditions. Also, we can reasonably conclude that the DRX, CDRX and grain refinement of stainless steels can be achieved by large strain deformation at high Z parameter condition.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.187-194
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• 2015

In this paper, we present an effective visual odometry estimation system to track the real-time pose of a camera moving in 3D space. In order to meet the real-time requirement as well as to make full use of rich information from color and depth images, our system adopts a feature-based sparse odometry estimation method. After matching features extracted from across image frames, it repeats both the additional inlier set refinement and the motion refinement to get more accurate estimate of camera odometry. Moreover, even when the remaining inlier set is not sufficient, our system computes the final odometry estimate in proportion to the size of the inlier set, which improves the tracking success rate greatly. Through experiments with TUM benchmark datasets and implementation of the 3D scene reconstruction application, we confirmed the high performance of the proposed visual odometry estimation method.

• Spatial Information Research
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• v.8 no.1
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• pp.155-170
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• 2000

When we use R-Tree,a spatial index, to find objects matches some predicate, it often leads to an incorrect result of perform filtering step only with MBR. And , each candidates need to be inspected to conform if it really satisfies with given query, so called, 'refinement step'. In refinement step. we should perform disk I/O and expansive spatial operations which is the cause of increasing retrieval costs. Therefore, to minimize the number of candidate after filtering step, two-phase filtering methods were studied, but there was many problems such as inefficiency of filtering,maintenance of additional informations and reconstruction of data resulted from the loss of original information. So , in this paper, I propose an Extended R-Tree which provides ability to retrieve spatial objects only with some simple logical operations using Grid Table, truth table strong the information about the existence of spatial objects, in second filtering step. Consequently , this Extended R-Tree using Grid Filter has low cost of operation for filtering because of efficient second filtering step, and better filtering efficiency caused by high quality of approximation.