• 전자통신동향분석
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• 제39권3호
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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.

• 한국컴퓨터그래픽스학회논문지
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• 제5권2호
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• pp.1-7
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• 1999

구조화되어있지 않은 점 집합으로부터 파이프 곡면을 복원해 내는 알고리즘을 기술한다. 파이프 곡면은 척추 곡선과 스위핑 구의 반지름으로 정의된다. 수축과 동적 최소자승법에 의해 점 집합은 얇은 곡선 형태로 변환된다. 이 얇은 곡선 형태의 점 집합은 쉽게 척추곡선으로 근사될 수 있다. 얇은 점 집합과 원래 주어진 점 집합의 대응하는 두 점간의 거리로 파이프 곡면을 정의하는 스위핑 구의 반지름을 구할 수 있다.

• Elastomers and Composites
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• 제38권1호
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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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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• 제10권2호
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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.

• 정보과학회 논문지
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• 제43권5호
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• pp.562-568
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• 2016

포인트 클라우드 데이터는 어떠한 형태를 표현하기 위해 무수히 많은 점들을 갖는 데이터 집합으로 특정 벡터 시스템에서 표현될 수 있으며, 일반적으로 3차원 좌표 공간에서 다양한 특성을 갖게 된다. 본 논문은 3차원 좌표 시스템의 포인트 클라우드에서 기존 방법(Hough Transform)보다 빠른 실린더 형태의 파이프 추정을 목표로 한다. 이를 위해 비교적 빠른 RANdom SAmple Consensus(RANSAC)를 사용한다. 본 논문에서 제안하는 실린더 형태의 파이프 추정은 두 가지 형태의 수학적 모델을 근거로 파라미터를 계산하고, 결과를 조합하여 예측한다. 두 가지 수학적 모델은 구(Sphere)와 직선(Line)이며, RANSAC 적합을 통해 실린더의 축과 반지름이 될 수 있는 구의 파라미터(중심과 반지름)를 계산하고, 이를 직선화하여 실린더를 추정한다. 이는 법선 추정(Normal Estimation) 및 분할(Segmentation) 없이 비교적 정확도를 유지하며, 빠르게 실린더 매칭을 할 수 있게 한다. 빠른 실린더 매칭은 실시간 파이프 추정이 필요한 레이저 스캐닝 및 건설 역설계 분야에서 활용할 수 있을 것이다.

• 한국정밀공학회지
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• 제23권5호
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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.

• 방송공학회논문지
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• 제24권4호
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• pp.660-669
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• 2019

최근 3차원 스캐닝 장비 및 다차원 어레이 카메라의 발달로 AR(Augmented Reality)/VR(Virtual Reality), 자율 주행과 같은 응용분야에서 3차원 데이터를 다루는 기술에 관한 연구가 지속해서 이루어지고 있다. 특히, AR/VR 분야에서는 3차원 영상을 포인트 데이터로 표현하는 콘텐츠가 등장하였으나, 이는 기존의 2차원 영상보다 많은 양의 데이터가 필요하다. 따라서 3차원 포인트 클라우드 콘텐츠를 사용자에게 서비스하기 위해서는 고효율의 부호화/복호화와 저장 및 전송과 같은 다양한 기술 개발이 요구된다. 본 논문에서는 MPEG-I(MPEG-Immersive) V-PCC(Video based Point Cloud Compression) 그룹에서 제안한 V-PCC 부호화기를 통해 생성된 V-PCC 비트스트림을 MPEG-DASH(Dynamic Adaptive Streaming over HTTP) 표준에서 정의한 세그먼트로 구성하는 방안을 제안한다. 또한, 사용자에게 3차원 좌표계 정보를 제공하기 위해 시그널링 메시지에 깊이 정보 파라미터를 추가로 정의한다. 그리고 본 논문에서 제안된 기술을 검증하기 위한 검증 플랫폼을 설계하고, 제안한 기술의 알고리듬 측면에서 확인한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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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.

• 한국측량학회지
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• 제30권6_2호
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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).