• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.43.2-43.2
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• 2010

We reconstruct the large-scale initial density field from the distribution of galaxies observed by the Sloan Digital Sky Survey (SDSS). After adding the small-scale fluctuations to match the power spectrum to that of the standard LCDM model, we make a cosmological N-body simulation of formation of structures from the initial conditions. Properties of the objects formed in the simulation can be statistically compared with those of the observed SDSS galaxies. The simulation makes it possible to know the past history of evolution of objects located in different environments, and also gives us information on the environmental parameters that cannot be directly obtained observationally. It is hoped that this comparative study leads us to better understanding of formation and evolution of galaxies in conjunction with large-scale structures in the universe.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2218-2221
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• 1998

Data compression of 3 dimensional objects has limitations such as large data size, slow processing time, uncertainties of noise and measurement errors. Recently, along with activities of information transmission in internet, the researches in 3 dimensional information processing become a very important issue as the exchanges of large data are available. In this paper, we propose a data compression algorithm which first estimates the surfaces of 3 dimensional objects and then apply them to the FCV(Fuzzy c-Varieties) Clustering to obtain 3 dimensional edges and vertices, and represent the results.

• Convergence Security Journal
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• v.8 no.4
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• pp.97-104
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• 2008

Recently, the wireless positioning techniques and mobile computing techniques have rapidly developed to use location data of moving objects. The more the number of moving objects is numerous and the more periodical sampling of locations is frequent, the more location data of moving objects become very large. Hence the system should be able to efficiently manage mass location data, support various spatio-temporal queries for LBS, and solve the uncertainty problem of moving objects. Therefore, in this paper, innovating the location data of moving object effectively, we propose Rend 3DR-tree method to decrease the dead space and complement the overlapping of nodes by utilizing 3DR-tree with the indexing structure to support indexing of current data and history data.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.229-235
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• 2017

In recent years, research has been actively carried out to recognize and recognize objects based on a large amount of data. In this paper, we propose a system that extracts objects that are thought to be obstacles in road driving images and recognizes them by car, man, and motorcycle. The objects were extracted using Optical Flow in consideration of the direction and size of the moving objects. The extracted objects were recognized using Alexnet, one of CNN (Convolutional Neural Network) recognition models. For the experiment, various images on the road were collected and experimented with black box. The result of the experiment showed that the object extraction accuracy was 92% and the object recognition accuracy was 96%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.822-828
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• 2002

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. However, the use have been confined because of OF estimation time as well as error problem. This paper shows a new method form effectively extracting movement information using the multi-constraint base approaches with sobel edge detection. The moving objects anr extraced in the input image sequence using edge detection and segmentation. Edge detection and difference of the two input image sequence gives us the moving objects in the images. The process of thresholding removes the moving objects detected due to noise. After thresholding the real moving objects, we applied the Combinatorial Hough Transform (CHT) and voting accumulation to find the optimal constraint lines for optical flow estimation. The moving objects found in the two consecutive images by using edge detection and segmentation greatly reduces the time for comutation of CHT. The voting based CHT avoids the errors associated with least squares methods. Calculation of a large number of points along the constraint line is also avoided by using the transformed slope-intercept parameter domain. The simulation results show that the proposed method is very effective for extracting optical flow vectors and hence recognizing moving objects in the images.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.96-96
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• 2016

Nanotechnology mostly employs nano-materials and nano-structures with distinctive properties based on their size, structure, and composition. It is quite difficult to produce nano-materials and nano-structures with identical sizes, structures, and compositions in large quantities, because of spatiotemporal fluctuation of production processes. In other words, fluctuation is the bottleneck in nanotechnology. We propose three strategies to suppress such fluctuations: employing 1) difference between linear and nonlinear phenomena, 2) difference in time constants, and 3) nucleation as a bottleneck phenomenon. We are also developing nano- and micro-scale guided assembly using plasmas as a plasma nanofabrication.1-5) We manipulate nano- and micro-objects using electrostatic, electromagnetic, ion drag, neutral drag, and optical forces. The accuracy of positioning the objects depends on fluctuation of position and energy of an object in plasmas. Here we evaluate such fluctuations and discuss the mechanism behind them. We conducted in-situ evaluation of local plasma potential fluctuation using tracking analysis of fine particles (=objects) in plasmas. Experiments were carried out with a radio frequency low-pressure plasma reactor, where we set two quartz windows at the top and bottom of the reactor. Ar plasmas were generated at 200 Pa by applying 13.56MHz, 450V peak-to-peak voltage. The injected fine particles were monodisperse methyl methacrylate-polymer spheres of $10{\mu}m$ in diameter. Fine particles were injected into the reactor and were suspended around the plasma/sheath boundary near the powered electrode. We observed binary collision of fine particles with a high-speed camera. The frame rate was 1000-10000 fps. Time evolution of their distance from the center of mass was measured by tracking analysis of the two particles. Kinetic energy during the collision was obtained from the result. Potential energy formed between the two particles was deduced by assuming the potential energy plus the kinetic energy is constant. The interaction potential is fluctuated during the collision. Maximum amplitude of the fluctuation is 25eV, and the average is 8eV. The fluctuation can be caused by neutral molecule collisions, ion collisions, and fluctuation of electrostatic force. Among theses possible causes, fluctuation of electrostatic force may be main one, because the fine particle has a large negative charge of -17000e and the corresponding electrostatic force is large compared to other forces.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.13-24
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• 2005

Objects, which change their positions over time such as cars, are called moving objects. Trajectories of a moving object have large volumes because trajectories are accumulated. Efficient indexing techniques for searching these large volumes of trajectories are needed in the moving object databases. Especially the TB-tree which supports bundling trajectories is suitable for processing combined queries which have 2 steps: first step is selecting trajectories (range search), next is selecting the parts of each trajectory (trajectory search). But the TB-tree has unnecessary disk accesses cause of lack of spatial discrimination in range queries. In this paper, we propose and implement the splitting polity which can reduce dead spaces of non-leaf node in order to process range queries efficiently. The policy has better performance about range queries than the TB-tree as well as the advantages of the TB-tree, such as highly space utilization and efficient trajectory extraction. This paper shows that the newly proposed split policy has better performance in processing the range queries than that of the TB-tree by experimental evaluation.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.115-131
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• 2008

Small mobile devices have become commonplace in our everyday life, from cellular phones to PDAs. Discovering dense regions for the mobile devices is one of the problems of grate practical importance. It can be used in monitoring movement of vehicles, concentration of troops, etc. In this paper, we propose a novel algorithm on continuously clustering a large set of mobile objects. We assume that a mobile object reports its position only if it is too far away from the expected position and thus the location data received may be imprecise. To compute the location of each individual object could be costly especially when the number of objects is large. To reduce the complexity of the computation, we want to first cluster objects that are in proximity into a group and treat the members in a group indistinguishable. Each individual object will be examined only when the inaccuracy causes ambiguity in the final results. We conduct extensive experiments on various data sets and analyze the sensitivity and scalability of our algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.51.2-51.2
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• 2018

Polarimetry is a powerful technique to investigate the physical properties of surface materials on airless bodies in the solar system. It is known that the degree of linear polarization changes as a function of the phase angle (the angle between Sun-target-Observer). Especially, the dependency of the polarization degree at large phase angle allows us to obtain information related to the particle size and porosity, which is difficult to be determined via other observation techniques (i.e., photometry and spectroscopy). However, despite the advantage, only a few asteroids were observed with polarimetric devices at large phase angles. Here, we present our new polarimetric research of Near-Earth Asteroids (NEAs) observed at the large phase angles. Among the NEAs, we focus on S- and Q-type asteroids, which include: (331471) 1984 QY1, (90075) 2002 VU94, and (66391) 1999 KW4. The observation was conducted using the Pirka 1.6-m Telescope at the Nayoro Observatory of Hokkaido University at the phase angles ${\alpha}{\sim}100degree$, which provides us the maximum polarization degrees of these objects. Considering the observational results together with two objects ((1566) Icarus and (4179) Toutatis) in reference papers [1], [2], we will discuss the implication of the regolith size on their surfaces.