• Electronics and Telecommunications Trends
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• v.39 no.3
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• pp.69-78
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• 2024

Cloud computing technology based on centralized high-performance computing has brought about major changes across the information technology industry and led to new paradigms. However, with the rapid development of the industry and increasing need for mass generation and real-time processing of data across various fields, centralized cloud computing is lagging behind the demand. This is particularly critical in emerging technologies such as autonomous driving, the metaverse, and augmented/virtual reality that require the provision of services with ultralow latency for real-time performance. To address existing limitations, distributed and edge cloud computing technologies have recently gained attention. These technologies allow for data to be processed and analyzed closer to their point of generation, substantially reducing the response times and optimizing the network bandwidth usage. We describe distributed and edge cloud computing technologies and explore the latest trends in their standardization.

• Journal of the Korea Computer Graphics Society
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• v.5 no.2
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• pp.1-7
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• 1999

We present an algorithm to reconstruct a pipe surface from a set of unorganized points. A pipe surface is defined by a spine curve and a radius of a swept sphere. In this paper, by using the shrinking and moving least-squares methods, a point cloud is reduced to a thin curve-like point set that can be easily approximated with a spine curve of a pipe surface. The distance between a point in the thin point cloud and a corresponding point in the original point set represents the radius of a swept sphere of a pipe surface.

• Elastomers and Composites
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• v.38 no.1
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• pp.51-56
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• 2003

Cloud-point and bubble-point data to $170^{\circ}C$ and 50 bar are presented for four different solvents, normal pentane. n-hexane, n-heptane, and n-octane with poly(ethylene-co-42 wt% octene) ($PEO_{42}$) copolymer. The pressure-concentration isotherms measured for $PEO_{42}$ - normal pentane have maximums at around 5 wt% of the copolymer concentrations in the solution. $PEO_{42}$- normal pentane system exhibits LCST-type phase behavior at temperatures greater than $130^{\circ}C$. Below $120^{\circ}C$, bubble-point type transitions are observed. However, the binary mixtures for $PEO_{42}$ in n-hexane, n-heptane, and n-octane have only bubble-point type transitions at the pressure-temperature region investigated in this study. The single-phase region of PEO - alkane mixtures increases with the molecular size of alkane solvent due to the increasing dispersion interactions between PEO and the alkane.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.783-786
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• 2021

In recent years, the use of LiDAR sensors is increasing as autonomous driving, robot control, and drones are considered more. Contrary to ordinary cameras, LiDAR sensors make it possible to handle challenging problems by calculating the distance between objects. This crucial characteristic makes more active research on deep learning models dealing with point clouds which are data of LiDAR. In this paper, among the schemes of using the point cloud, the Point-based approach is mainly discussed. Furthermore, future streams and insights can be considered by looking at solving methods and the limitations.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.240-255
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• 2014

Cloud computing has increasingly been drawing attention these days. Each big company in IT hurries to get a chunk of meat that promises to be a whopping market in the future. At the same time, information is always associated with security and risk problems. Nowadays, the handling of these risks is no longer just a technology problem, with a good deal of literature focusing on risk or security management and framework in the information system. In this paper, we find the specific business meaning of the BMIS model and try to apply and leverage this model to cloud risk. Through a previous study, we select and determine the causal risk factors in cloud service, which are also known as CSFs (Critical Success Factors) in information management. Subsequently, we distribute all selected CSFs into the BMIS model by mapping with ten principles in cloud risk. Finally, by using the leverage points, we try to leverage the model factors and aim to make a resource-optimized, dynamic, general risk control business model for cloud service providers.

• Journal of KIISE
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• v.43 no.5
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• pp.562-568
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• 2016

Point cloud data can be expressed in a specific coordinate system of a data set with a large number of points, to represent any form that generally has different characteristics in the three-dimensional coordinate space. This paper is aimed at finding a cylindrical pipe in the point cloud of the three-dimensional coordinate system using RANSAC, which is faster than the conventional Hough Transform method. In this study, the proposed cylindrical pipe is estimated by combining the results of parameters based on two mathematical models. The two kinds of mathematical models include a sphere and line, searching the sphere center point and radius in the cylinder, and detecting the cylinder with straightening of center. This method can match cylindrical pipe with relative accuracy; furthermore, the process is rapid except for normal estimation and segmentation. Quick cylinders matching could benefit from laser scanning and reverse engineering construction sectors that require pipe real-time estimates.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.5 s.182
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• pp.143-148
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• 2006

It is still very difficult to generate a geometry model and finite element model, which has complex and many free surface, even though 3D CAD solutions are applied. Furthermore, in the medical field, which is a big growth area of recent years, there is no drawing. For these reasons, making a geometry model, which is used in finite element analysis, is very difficult. To resolve these problems and satisfy the requests of the need to create a 3D digital file for an object where none had existed before, new technologies are appeared recently. Among the recent technologies, there is a growing interest in the availability of fast, affordable optical range laser scanning. The development of 3D laser scan technology to obtain 3D point cloud data, made it possible to generate 3D model of complex object. To generate CAD and finite element model using point cloud data from 3D scanning, surface reconstruction applications have widely used. In the early stage, these applications have many difficulties, such as data handling, model creation time and so on. Recently developed point-based surface generation applications partly resolve these difficulties. However there are still many problems. In case of large and complex object scanning, generation of CAD and finite element model has a significant amount of working time and effort. Hence, we concerned developing a good direct finite element model generation method using point cloud's location coordinate value to save working time and obtain accurate finite element model.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.660-669
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• 2019

Recently, with the development of three-dimensional scanning devices and multi-dimensional array cameras, research is continuously conducted on techniques for handling three-dimensional data in application fields such as AR (Augmented Reality) / VR (Virtual Reality) and autonomous traveling. In particular, in the AR / VR field, content that expresses 3D video as point data has appeared, but this requires a larger amount of data than conventional 2D images. Therefore, in order to serve 3D point cloud content to users, various technological developments such as highly efficient encoding / decoding and storage, transfer, etc. are required. In this paper, V-PCC bit stream created using V-PCC encoder proposed in MPEG-I (MPEG-Immersive) V-PCC (Video based Point Cloud Compression) group, It is defined by the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standard, and provides to be composed of segments. Also, in order to provide the user with the information of the 3D coordinate system, the depth information parameter of the signaling message is additionally defined. Then, we design a verification platform to verify the technology proposed in this paper, and confirm it in terms of the algorithm of the proposed technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.733-734
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• 2016

This study proposes a novel approach for ground segmentation of 3D point cloud. We combine two techniques: gradient threshold segmentation, and mean height evaluation. Acquired 3D point cloud is represented as a graph data structures by exploiting the structure of 2D reference image. The ground parts nearing the position of the sensor are segmented based on gradient threshold technique. For sparse regions, we separate the ground and nonground by using a technique called mean height evaluation. The main contribution of this study is a new ground segmentation algorithm which works well with 3D point clouds from various environments. The processing time is acceptable and it allows the algorithm running in real time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).