• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.143-152
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• 2015

Multimedia services in the cloud have become a popular trend in the big data environment. However, how to efficiently schedule a large number of multimedia services in the cloud is still an open and challengeable problem. Current cloud-based scheduling algorithms exist the following problems: 1) the content of the multimedia is ignored, and 2) the cloud platform is a known parameter, which makes current solutions are difficult to utilize practically. To resolve the above issues completely, in this work, we propose a novel distributed multimedia scheduling to satisfy the objectives: 1) Develop a general cloud-based multimedia scheduling model which is able to apply to different multimedia applications and service platforms; 2) Design a distributed scheduling algorithm in which each user makes a decision based on its local information without knowing the others' information; 3) The computational complexity of the proposed scheduling algorithm is low and it is asymptotically optimal in any case. Numerous simulations have demonstrated that the proposed scheduling can work well in all the cloud service environments.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.9-13
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• 1987

K-emans alsor/thm was used to classify cloud-type that is low, mix and cumuionimbus Tnitiat ciustercenters and K parameter is given in this paper by coatse computins and Fisher’s alsorithm. Results indicate that performance index is minimized and mix cloud is well clallified.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.1-9
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• 2015

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

• Publications of The Korean Astronomical Society
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• v.9 no.1
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• pp.55-68
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• 1994

We have reviewed three different techniques to estimate molecular cloud mass, and discussed the uncertainties involved. We found that determination of the most important parameter, the $^{13}CO$ abundance, is not very sensitive to the real LTE conditions, and that any possible error in deriving LTE column density may not introduce an error in the total gas column density, as far as the visual extinction is established for the object cloud. The virial technique always endows the largest mass estimate as there are several uncertainties, even if the cloud is in virial equilibrium. The strong indicator of the cloud perturbation is the centroid velocity dispersion. The mass using CO luminosity is based on the empirical law, but weakly dependent on the virial assumption, thus it still gives a larger mass estimate. The mass discrepancy is likely to be inevitable, and a factor of two or three difference between mass estimates could easily be attributed to the uncertainties mentioned above. The LTE mass estimate may be the most reliable one if we use the relation visual extinction and $^{13}CO$ column density of the object cloud, and the intercept is included.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.264-270
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• 2018

In our research study, we aim at optimizing multiple load in cloud, effective resource allocation and lesser response time for the job assigned. Using Hadoop on datacenter is the best and most efficient analytical service for any corporates. To provide effective and reliable performance analytical computing interface to the client, various cloud service providers host Hadoop clusters. The previous works done by many scholars were aimed at execution of workflows on Hadoop platform which also minimizes the cost of virtual machines and other computing resources. Earlier stochastic hill climbing technique was applied for single parameter and now we are working to optimize multiple parameters in the cloud data centers with proposed heuristic hill climbing. As many users try to priorities their job simultaneously in the cluster, resource optimized workflow scheduling technique should be very reliable to complete the task assigned before the deadlines and also to optimize the usage of the resources in cloud.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.98.2-98.2
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• 2012

We firstly present the unified Far-UV continuum map of the Taurus-Auriga-Perseus (TPA) complex, one of the largest local associations of dark cloud located in (l, b)=([154,180], [-28, -2]), by merging both FIMS and GALEX. The FUV continuum map shows that dust extinction correlate well with the FUV around the complex. It shows strong absorption in FUV toward the dense Taurus cloud while it does not in California cloud. It turned out that it is related to the relative location of each cloud and Perseus OB2 association. We also present some results of dust scattering simulation based on Monte Carlo Radiative Transfer technique (MCRT). Through this dust scattering simulation, we have derived the scattering parameter for this region, albedo(a)=$0.42^{+0.05}{_{-0.05}}$, asymmetry factor(g)=$0.47^{+0.11}{_{-0.27}}$. The optical parameters we obtained seem reasonable compared to the theoretical model values ~0.40 and ~0.65 for the albedo and the phase function though the phase function is rather small. Using the result of simulation, we figured out the geometries of each cloud in the complex region, especially their distances and thicknesses. Our predictions from the results are in good agreement with the previous studies related to the TPA complex. For example, the Taurus cloud is within ~200pc from the Sun and the Perseus seems to be multi-layered, at least two. The California cloud is more distant than the other cloud on average at ~350 pc and Auriga cloud seems to be between the Taurus cloud and the eastern end of the California cloud. We figured out that across the TPA complex region, there might be some correlation between the LSR velocity and the distance to each cloud in the complex.

• Atmosphere
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• v.27 no.4
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• pp.499-509
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• 2017

Possible links among cosmic ray, cloud, and climate have scientific uncertainties. The reputed topics have been highly controversial during several decades. A link between the atmospheric ionization by galactic cosmic rays (GCR), which is modulated by solar activities, and global cloud cover was firstly proposed in 1997. Some researchers suggested that the GCR can stimulate the formation of cloud condensation nuclei (CCN) in the atmosphere, and then the higher CCN concentrations may lead to an increase of cloud cover, resulting in a cooling of the Earth's climate, and vise versa. The CLOUD (Cosmic leaving outdoor droplets) experiment was designed to study the effect of GCR on the formation of atmospheric aerosols and clouds under precisely controlled laboratory conditions. A state-of-the-art chamber experiment has greatly advanced our scientific understanding of the aerosol formation in early stage and its nucleation processes if the GCR effect is considered or not. Many studies on the climate-GCR (or space weather) connection including the CLOUD experiment have been carried out during the several decades. Although it may not be easy to clarify the physical connection, the recent scientific approaches such as the laboratory experiments or modeling studies give some implications that the research definitively contributed to reduce the scientific uncertainties of natural and anthropogenic aerosol radiative forcing as well as to better understand the formation processes of fine particulate matters as an important parameter of air quality forecast.

• Atmosphere
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• v.18 no.1
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• pp.1-14
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• 2008

The cloud amount, one of the basic parameter in atmospheric observation, have been observed by naked eyes of observers, which is affected by the subjective view. In order to ensure reliable and objective observation, a new algorithm to retrieve cloud amount was constructed using true color images composed of red, green and blue (RGB). The true color image is obtained by the Skyview, an all-sky imager taking pictures of sky, at the Science Building of Yonsei University, Seoul for a year in 2006. The principle of distinguishing clear sky from cloudy sky lies in the fact that the spectral characteristics of light scattering is different for air molecules and cloud. The result of Skyview's algorithm showed about 77% agreement between the observed cloud amount and the calculated, for the error range, the difference between calculated and observed cloudiness, within ${\pm}2$. Seasonally, the best accuracy of about 83% was obtained within ${\pm}2$ range in summer when the cloud amounts are higher, thus better signal-to-noise ratio. Furthermore, as the sky turbidity increased, the error also increased because of increased scattering which can explain the large error in spring. The algorithm still need to be improved in classifying sky condition more systematically with other complimentary instruments to discriminate thin cloud from haze to reduce errors in detecting clouds.