• 한국CDE학회논문집
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• 제11권5호
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• pp.335-343
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• 2006

Design changes for an original surface model are frequently required in a manufacturing area: for example, when the physical parts are modified or when the parts are partially manufactured from analogous shapes. In this case, an efficient 3D model updating method by locally adding scan data for the modified area is highly desirable. For this purpose, this paper presents a new procedure to update an initial model that is composed of combinatorial triangular facets based on a set of locally added point data. The initial surface model is first created from the initial point set by Tight Cocone, which is a water-tight surface reconstructor; and then the point cloud data for the updates is locally added onto the initial model maintaining the same coordinate system. In order to update the initial model, the special region on the initial surface that needs to be updated is recognized through the detection of the overlapping area between the initial model and the boundary of the newly added point cloud. After that, the initial surface model is eventually updated to the final output by replacing the recognized region with the newly added point cloud. The proposed method has been implemented and tested with several examples. This algorithm will be practically useful to modify the surface model with physical part changes and free-form surface design.

• 한국항행학회논문지
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• 제28권4호
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• pp.428-435
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• 2024

본 논문에서는 다중 입출력 주파수 변조 연속파 (MIMO FMCW; multiple input multiple output frequency modulation continuous wave) 레이다 기반 HAR (human activity recognition) 시스템의 설계 및 구현 결과를 제시하였다. 다중 입력 다중 출력 레이다 센서를 통한 포인트 클라우드 데이터를 활용하여 HAR 시스템을 구현하면 사생활 보호와 함께, 안전성 및 정확성 측면에서 장점이 있다. 본 논문에서는, MIMO FMCW 레이다 센서로부터의 포인트클라우드 데이터 기반 HAR을 위해 PointPillars와 DS-CNN (depthwise separable convolutional neural network)을 기반으로 최적 경량 네트워크를 개발하였다. 경량화된 네트워크를 통해 고해상도 포인트 클라우드 데이터를 처리하여 높은 인식 정확도와 함께 효율성을 달성하였다. 결과적으로, 98.27%의 정확도와 11.27M Macs (multiply-accumulates) 연산 복잡도로 구현 가능함을 확인하였다. 또한, 개발한 모델을 라즈베리파이(Raspberry-Pi) 시스템에 구현하여 최대 8 fps의 속도로 포인트 클라우드 데이터 처리가 가능함을 확인하였다.

• Bulletin of the Korean Chemical Society
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• 제27권10호
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• pp.1604-1608
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• 2006

A simple and efficient cloud point extraction-spectrofluorimetric method for the determination of thiamine in human urine is proposed. The procedure is based on the oxidation of thiamine with ferricyanide to form thiochrome, its extraction to Triton X-114 micelles and spectrofluorimetric determination. The variables affecting oxidation of thiamine, extraction and phase separation were studied and optimized. Under the experimental conditions used, the calibration graphs were linear over the range 2.5-1000 ng $mL^{-1}$. The limit of detection was 0.78 ng $mL^{-1}$ of thiamine and the relative standard deviation for 5 replicate determinations of thiamine at 400 ng $mL^{-1}$ concentration level was 2.42%. Average recoveries between 93-107% were obtained for spiked samples. The proposed method was applied to the determination of thiamine in human urine.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1564-1569
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• 2007

This study concerns an advanced NURBS surface reconstruction method, which is based on the NURBS surface model fitting to the unstructured point cloud measured from an arbitrary complex shape. The concept of generating a simple triangular mesh model was introduced to generate a quadrilateral mesh model well-representing the topological characteristics of point cloud. The NURBS surface reconstruction processes required the use of the various methodologies such as QEM algorithm, merging scheme of pair-wise triangular mesh, creation algorithm of $G^1$ continuous tensor product NURBS surface patch, and so on. The effectiveness and reliability of the proposed NURBS surface reconstruction method were validated through the simulation results for the geometrically and topologically complex shapes.

• 제어로봇시스템학회논문지
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• 제9권9호
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• pp.700-706
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• 2003

This paper presents a 3D last design system that provides the 3-dimensional last data based on the FFD(Free Form Deformation) method. The proposed system utilizes the control points for deformation factor to convert from the 3D point cloud foot data to the 3D point cloud last data. The deformation factor of the FFD is obtained from the conventional last design technique, and constructed on the FFD lattice based on the bottom view and lateral view of the measured 3D point cloud foot data. In addition, the control points of FFD lattice is decided on the anatomical points of foot. The deformed 3D last obtained from the proposed FFD is saved as a 3D dxf foot data. The experimental results demonstrate that the proposed system have the descent 3D last data based on the openGL window.

• Bulletin of the Korean Chemical Society
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• 제27권11호
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• pp.1780-1784
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• 2006

Cloud point extraction was used to extract mefenamic acid (MF) from human urine, and spectrofluorimetry and spectrophotometry were used to analyze extracted MF. The variables affecting extraction and phase separation, i.e. HCl and Triton X-114 concentration, temperature and time of equilibration, were optimized. Under the experimental conditions used the limit of detection for extraction of 25 mL of sample was 0.006 and 0.045 mg $L^{-1}$, with relative standard deviations of 2.52 and 1.45% (n = 5) for spectrofluorimetric or spectrophotometric methods, respectively. Good recoveries in the range of 95-107% were obtained for spiked samples. The proposed methods were applied to the determination of MF in human urine.

• 한국염색가공학회지
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• 제15권3호
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• pp.185-191
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• 2003

Surface modification of poly(ethylene terephthalate) (PEI) films by treatment with ethoxylated alkylaminoanthraquinoes which was synthesized by the reaction of 1-aminoanthraquinone with poly(ethylene glycol) via a series of methylene spacer were investigated. The synthesized ethoxylated alkylaminoanthraquinones showed definite cloud point as in nonionic surfactants, and the adsorption of the compounds on PET increased near the cloud point. At same temperature the adsorption increased with the length of methylene spacer; hexyl-octyl-, and decyl-. The adsorption was limited to the extreme surface of PET film, which made the surface of PET film hydrophillic by reducing water contact angle.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• 한국BIM학회 논문집
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• 제9권2호
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• pp.21-28
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• 2019

Recently, many scan projects are gradually increasing for maintenance, construction. The scan data contains useful data, which can be generated in the target application from the facility, space. However, modeling the scan data required for the application requires a lot of cost. In example, the converting 3D point cloud obtained from scan data into 3D object is a time-consuming task, and the modeling task is still very manual. This research proposes Scan-to-Geometry Mapping Rule Definition (S2G-MD) which maps point cloud data to geometry for irregular building plane objects. The S2G-MD considers user use case variability. The method to define rules for mapping scan to geometry is proposed. This research supports the reverse engineering semi-automatic process for the building planar geometry from the user perspective.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.