• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.85-92
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• 2008

Meshes are the most appropriate data structures for representing 3D geometries. Surface meshes have been frequently used for representing 3D geometries, which only samples data on the surfaces of the given 3D geometries. Thanks to the improvements of computing powers, it is required to develop more complicated contents which utilize the volumetric information of 3D geometries. In this paper, we introduce a novel volumetric mesh libraries based on the half-face data structure, called OpenVolMesh, and describe its designs and implementations. The OpenVolMesh extends the OpenMesh, which is one of the most famous mesh libraries, by supporting volumetric meshes. The OpenVolMesh provides the generic programming, dynamic allocations of primitive properties, efficient array-based data structures, and source-level compatibility with OpenMesh. We show the usefulness of the OpenVolMesh in the developments of 3D volumetric contents with prototypic implementations such as volumetric mesh smoothing and CW-cell decompositions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.9
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• pp.59-70
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• 2020

Three-dimensional (3D) design data is used for various purposes throughout the life cycle of a plant construction project. Plant 3D CAD systems support 3D modeling based on specs-catalogs, which contain data that are used for different purposes such as design, procurement, production, and handover. Therefore, it is important to share the spec-catalog data in the 3D design model with other application systems. Sharing this data thus requires a system that extracts spec-catalog data from plant 3D CAD systems and converts them into neutral model data. In this paper, we analyze equipment spec-catalog data of plant 3D CAD systems and, based on these analyses, define the data structure for neutral spec-catalog data. We subsequently propose a procedure that translates native spec-catalog data to neutral model data and develop a prototype system that performs this operation. The proposed method is then experimentally validated for the test spec-catalog data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.153-154
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• 2006

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1399-1406
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• 2009

Proteins are one of essential part of organisms; many proteins are enzymes that catalyze biochemical reactions and are vital to metabolism. Researching for 3D structure of proteins is important because functions of proteins are determined by 3D structure of them. In this study, we developed graphic tool that supports comparison and observation of the two proteins' 3D structure in the single screen. It also supports some comparison data to help researcher's easy comparison and observation works.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.8
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• pp.251-260
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• 2014

3D point data is utilized in various industry domains for its high accuracy to the surface information of an object. It is substantially utilized in geography for terrain scanning and analysis. Generally, 3D point data need to be changed by Gridding which produces a regularly spaced array of z values from irregularly spaced xyz data. But it requires long processing time and high resource cost to interpolate grid coordination. Kriging interpolation in Gridding has attracted because Kriging interpolation has more accuracy than other methods. However it haven't been used frequently since a processing is complex and slow. In this paper, we presented a parallel Gridding algorithm which contains Kriging and an application of grid data structure to fit MapReduce paradigm to this algorithm. Experiment was conducted for 1.6 and 4.3 billions of points from Airborne LiDAR files using our proposed MapReduce structure and the results show that the total execution time is decreased more than three times to the convention sequential program on three heterogenous clusters.

• Journal of the Korea Computer Graphics Society
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• v.14 no.2
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• pp.35-41
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• 2008

H-Anim is an international standard that Humanoid Animation Working Group in Web3D Consortium defined the data structure necessary for human animation. Various libraries and tools have been generated according to the structure, but they still have restrictions to represent realistic humanoid motions. This paper presents the method of generating realistic human motion using motion capture data in order to define motion for humanoid animation based on H-Anim standard. In order to implement this, we have defined a data structure capable of receiving motion capture data and implemented a motion browser. The human motion data structure defined in this paper is based on X3D and intended to have compatibility through networks and various browsers.

• Journal of Fashion Business
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• v.23 no.3
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• pp.67-84
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• 2019

Since the early 2000s, various fashion design products that use 3D printing technology have constantly been introduced to the fashion industry. However, given the nature of 3D printing technology, the flexible characteristics of material of textile fabrics is yet to be achieved. The aim of this study is to develop the optimal design conditions for production of flexible and elastic 3D printing fabric structure based on plain weave, which is the basic structure in fabric weaving using SLS 3D printing technology. As a the result this study aims to utilize appropriate design conditions as basic data for future study of flexible fashion product design such as textile material. Weaving structural design using 3D printing is based on the basic plain weave, and the warp & weft thickness of 4mm, 3mm, 2mm, 1.5mm, 1mm, and 0.7mm as expressed in Rhino 6.0 CAD software program for making a 3D model of size $1800mm{\times}180mm$ each. The completed 3D digital design work was then applied to the EOS SLS Machine through Maker ware, a program for 3D printer output, using polyamide 12 material which has a rigid durability strength, and the final results obtained through bending flexibility tests. In conclusion, when designing the fabric structure design in 3D printing using SLS method through application of polyamide 12 material, the thickness of 1 mm presented the optimal condition in order to design a durable digital textile structure with flexibility and elasticity of the 3D printing result.

• Journal of Institute of Convergence Technology
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• v.10 no.1
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• pp.7-12
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• 2020

We describe a efficient surface reconstruction method that reconstructs a 3D manifold polygonal mesh approximately passing through a set of 3D oriented points. Our algorithm includes 3D convex hull, octree data structure, signed distance function (SDF), and marching cubes. The 3D convex hull provides us with a fast computation of SDF, octree structure allows us to compute a minimal distance for SDF, and marching cubes lead to iso-surface generation with SDF. Our approach gives us flexibility in the choice of the resolution of the reconstructed surface, and it also enables to use on low-level PCs with minimal peak memory usage. Experimenting with publicly available scan data shows that we can reconstruct a polygonal mesh from point cloud of sizes varying from 10,000 ~ 1,000,000 in about 1~60 seconds.