• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.161-177
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• 2016

In history based parametric CAD modeling systems, persistent identification of the topological entities after design modification is mandatory to keep the design intent by recording model creation history and modification history. Persistent identification of geometric and topological entities is necessary in the product design phase as well as in the re-evaluation stage. For the identification, entities should be named first according to the methodology which will be applicable for all the entities unconditionally. After successive feature operations on a part body, topology based persistent identification mechanism generates ambiguity problem that usually stems from topology splitting and topology merging. Solving the ambiguity problem needs a complex method which is a combination of topology and geometry. Topology is used to assign the basic name to the entities. And geometry is used for the ambiguity solving between the entities. In the macro parametrics approach of iCAD lab of KAIST a topology based persistent identification mechanism is applied which will solve the ambiguity problem arising from topology splitting and also in case of topology merging. Here, a method is proposed where no geometry comparison is necessary for topology merging. The present research is focused on the enhancement of the persistent identification schema for the support of ambiguity problem especially of topology splitting problem and topology merging problem. It also focused on basic naming of pattern features.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.215-224
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• 2013

This paper proposes a robust lane detection algorithm for bumpy or slope changing roads by estimating extrinsic camera parameters, which represent the pose of the camera mounted on the car. The proposed algorithm assumes that two lanes are parallel with the predefined width. The lane detection and the extrinsic camera parameter estimation are performed simultaneously by utilizing B-snake in motion compensated and merged feature map with consecutive sequences. The experimental results show the robustness of the proposed algorithm in various road environments. Furthermore, the accuracy of extrinsic camera parameter estimation is evaluated by calculating the distance to a preceding car with the estimated parameters and comparing to the radar-measured distance.

• International Journal of Contents
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• v.3 no.2
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• pp.25-29
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• 2007

In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.141-143
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• 2004

This paper presents an algorithm in which a feature map is built and localization of a mobile robot is carried out for indoor environments. The algorithm proposes an approach which extracts scale-invariant features of natural landmarks from a pair of stereo images. The feature map is built using these features and updated by merging new landmarks into the map and removing transient landmarks over time. And the position of the robot in the map is estimated by comparing with the map in a database by means of an Extended Kalman filter. This algorithm is implemented and tested using a Pioneer 2-DXE and preliminary results are presented in this paper.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.38.1-38.1
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• 2008

Tides caused by the Galactic gravitational field affect the current dynamical structure of globular clusters in the Galaxy. Indeed, the observed feature of tidal tails stretching beyond globular clusters' tidal radii provides a key information of interaction with the gravitational field of the Galaxy and kinematical orbit of the clusters, which can be an evidence of the merging scenario of the Galaxy formation and evolution. To find such a tidal feature, we have studied spatial density distribution of stars around five globular clusters in the Galactic halo and one cluster in the Galactic bulge, for which we have used wide-field deep photometric data of gri and JHK bands obtained from the MegaCam and WIRCam of the CFHT. Applying the statistical contrast filtering of field stars in the color-magnitude plane of detected stars around five halo clusters, we have found features of tidal tails for four clusters M53, M15, NGC 5053, and NGC 5466. The detected over-density tidal features are well aligned with the cluster's orbits and stretched into the direction of the Galactic center. Statistical analysis indicate that these tidal tails are believed to be cluster stars that have escaped due to the tidal effects to the clusters. A similar tidal feature to that of halo clusters is also detected for the bulge cluster NGC 6626, while the over-density feature seems to be extended into the Galactic plane rather than into the orbital direction and the Galactic center. Conclusively, our result adds further observational evidence of the merging scenario of the Galaxy formation and evolution.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.3
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• pp.9-23
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• 2015

A lane change maneuver which has a high driver cognitive workload and skills sometimes leads to severe traffic accidents. In this study, the Advanced Lane Change Assist System (ALCAS) was developed to assist with the automatic lane changes in merging sections which is mainly based on an automatic control algorithm for detecting an available gap, determining the Optimal Lane Change Start Point (OLCSP) in various traffic conditions, and positioning the merging vehicle at the OLCSP safely by longitudinal automatic controlling. The analysis of lane change behavior and modeling of fundamental lane change feature were performed for determining the default parameters and the boundary conditions of the algorithm. The algorithm was composed of six steps with closed-loop. In order to confirm the algorithm performance, numerical scenario tests were performed in various surrounding vehicles conditions. Moreover, feasibility of the developed system was verified in microscopic traffic simulation(VISSIM 5.3 version). The results showed that merging vehicles using the system had a tendency to find the OLCSP readily and precisely, so improved merging performance was observed when the system was applied. The system is also effective even during increases in vehicle volume of the mainline.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.463-464
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• 2015

It has been suggested that only the most luminous AGNs ($L{\gtrsim}10^{45}erg/s$) are triggered by galaxy mergers, while less luminous AGNs ($L{\sim}10^{43}erg/s$) are driven by other internal processes. The lack of merging features in low luminosity AGN host galaxies has been a primary argument against the idea of merger triggering of low luminosity AGNs. But a merger, especially a rather minor one, might still have played an important role in low luminosity AGNs, as minor merging features at low luminosities are more difficult to identify than major merging features. Using SNUCAM on the 1.5 m telescope at Maidanak observatory, we obtained deep optical images of NGC 7743, a barred spiral galaxy classified as a Seyfert 2 AGN with a low bolometric luminosity of $5{\times}10^{42}erg/s$. Surprisingly, we discovered a merging feature around the galaxy, which indicates past merging activity in the galaxy. This example indicates that the merging fraction of low luminosity AGNs may be much higher than previously thought, hinting at the importance of galaxy mergers even in low luminosity AGNs.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.41.2-41.2
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• 2017

Discovered as the second highest S/N detection of the Planck SZ survey, PLCK G287.0+32.9 is a massive galaxy cluster that belongs to a rare collection of merging clusters that exhibit two radio relics and a radio halo. A feature that makes this cluster even more unique is the separation of the radio relics with one $\sim 400$ kpc to the north-west of the X-ray peak and the other $\sim 2.8$ Mpc to the south-east. This asymmetric configuration requires a complex merging scenario. A key to gaining insight into the events that caused the formation of the merging features is to understand the dark matter mass distribution. Using a weak-lensing technique on deep Subaru and Hubble Space Telescope observations, we map the dark matter mass distribution of PLCK G287.0+32.9. Our investigation detects five significant mass structures. The mass is dominated by a primary structure that is centered near the X-ray peak of the intracluster medium. Four lesser mass structures are detected with two located within $\sim 1\arcmin$ of the primary mass structure, a third to the north-west, and a fourth near the south-east radio relic. Along with these detections, we estimate the mass of each structure and relate their distributions to the intracluster medium and galaxy distributions. In addition, we discuss the relation of the mass structures to the formation of the relics and plausible merging scenarios.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.289-294
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• 2002

A valid speech-sound block can be classified to provide important information for speech recognition. The classification of the speech-sound block comes from the MRA(multi-resolution analysis) property of the DWT(discrete wavelet transform), which is used to reduce the computational time for the pre-processing of speech recognition. The merging algorithm is proposed to extract valid speech-sounds in terms of position and frequency range. It needs some numerical methods for an adaptive DWT implementation and performs unvoiced/voiced classification and denoising. Since the merging algorithm can decide the processing parameters relating to voices only and is independent of system noises, it is useful for extracting valid speech-sounds. The merging algorithm has an adaptive feature for arbitrary system noises and an excellent denoising SNR(signal-to-nolle ratio).

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.89-89
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• 2014

While the current consensus view holds that galaxy mergers are commonplace, it is sometimes speculated that Globular Clusters (GCs) may also have undergone merging events, possibly resulting in massive objects with a strong metallicity spread such as Omega Centauri. Galaxies are mostly far, unresolved systems whose mergers are most likely wet, resulting in observational as well as modeling difficulties, but GCs are resolved into stars that can be used as discrete dynamical tracers, and their mergers might have been dry, therefore easily simulated with an N-body code. It is however difficult to determine the observational parameters best suited to reveal a history of merging based on the positions and kinematics of GC stars, if evidence of merging is at all observable. To overcome this difficulty, we investigate the applicability of supervised and unsupervised machine learning to the automatic reconstruction of the dynamical history of a stellar system. In particular we test whether statistical clustering methods can classify simulated systems into monolithic versus merger products. We run direct N-body simulations of two identical King-model clusters undergoing a head-on collision resulting in a merged system, and other simulations of isolated King models with the same total number of particles as the merged system. After several relaxation times elapse, we extract a sample of snapshots of the sky-projected positions of particles from each simulation at different dynamical times, and we run a variety of clustering and classification algorithms to classify the snapshots into two subsets in a relevant feature space.