• Spatial Information Research
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• v.19 no.1
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• pp.93-105
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• 2011

In recent years, large and complex three-dimensional building has been constructed by the development of building technology and advanced IT skills, and people have lived there and spent a considerable time so far. Accordingly. in this sophisticatcd three-dimensional space, emergencies services or convenient information services have been in demand. In order to provide these services efficiently, understanding of topological relationships among the complex space should be supported naturally. Not on1y each method of understanding the topological relationships but also its efficiency can be different depending on different topological data models. B-rep based data model is the most widely used for storaging and representing of topological relationships. And from early 2000s, many researches on a network based topological data model have been conducted. The purpose of this study is to verify the efficiency of performance on spatial queries. As a result, Network-based topological data model is more efficient than B-rep based data model for determining the spatial relationship.

• Journal of Information Display
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• v.3 no.3
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• pp.17-23
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• 2002

In this work we report methods of formation of three-dimensional structures of particles in a liquid crystal host. We found that, under the appropriate conditions, the particles are captured and dragged by the moving isotropic/nematic front during the phase transition process. This movement of the particles can be enhanced significantly or suppressed drastically with the influence of an electric field and/or with changing the conditions of the phase transition, such as the rate of cooling. As a result, a wide variety of particle structures can be obtained ranging from a fine-grained cellular structure to stripes of varying periods to a course-grained "root" structures. Changing the properties of the materials, such as the size and density of the particles and the surface anchoring of the liquid crystal at the particle surface, can also be used to control the morphology of the three-dimensional particle network and adjust the physical properties of the resulting dispersions. These particle structures may be used to affect the performance of LCD's much as polymers have been used in the past.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.157-159
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• 2019

Hologram technology has been actively developed in terms of generation, transmission, and reproduction of 3D objects, but it is currently in a state of rest because of various limitations. Beyond VR and AR, the pseudo-hologram market is growing at an intermediate stage to meet the needs of new technologies. The key to the technology of hologram is to generate vast 3 dimensional data in the form of a point cloud, transmit the vast amount of data through the communication network in real time, and reproduce it like the original at the destination. In this paper, we propose a method to transmit massive 3 - D data in real - time and transmit the minutiae points of 3 - dimensional object information to reproduce the object as similar to original.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.186-192
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• 2014

Monolithic three-dimensional integrated chips (3D ICs) are an emerging technology that offers an integration density that is some orders of magnitude higher than the conventional through-silicon-via (TSV)-based 3D ICs. This is due to a sequential integration process that enables extremely small monolithic inter-tier vias (MIVs). For a monolithic 3D memory, we first explore the static random-access memory (SRAM) design. Next, for digital logic, we explore several design styles. The first is transistor-level, which is a design style unique to monolithic 3D ICs that are enabled by the ultra-high-density of MIVs. We also explore gate-level and block-level design styles, which are available for TSV-based 3D ICs. For each of these design styles, we present techniques to obtain the graphic database system (GDS) layouts, and perform a signoff-quality performance and power analysis. We also discuss various challenges facing monolithic 3D ICs, such as achieving 50% footprint reduction over two-dimensional (2D) ICs, routing congestion, power delivery network design, and thermal issues. Finally, we present design techniques to overcome these challenges.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.47-54
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• 2016

This study aims to analyze the accuracy for feasibility study of Network-RTK(VRS) surveying inside and outside of Korean CORS network. The southwest coast of Korea where some part of mainland and islands are outside of CORS network is chosen as the test area. To evaluate the accuracy of VRS surveying at surveying points, several Unified Control Points (UCPs) inside and outside of Korean CORS network were selected as the points in the test area. The feasibility of VRS surveying was analyzed by investigating the errors related to the location of points inside and outside of CORS network and the difference of 3-dimensional coordinates observed on different date. As the results of this study, the orthometric height errors of points outside of CORS network based on KNGeoid14 were improved about 5.0 cm in RMSE in comparison with KNGeoid13. The height errors of VRS surveying were considered to be less relevant to the results from PDOP and number of satellites (GPS and GLONASS). However, the orthometric errors caused by the geoidal height of KNGeoid and the ellipsoidal height of VRS surveying at points located outside of CORS network need to be addressed carefully for control surveying. When a point surveyed twice on different date, the difference of the ellipsoidal height of the point outside of CORS network was larger than that of the point inside of CORS network.

• Journal of the Korean Magnetic Resonance Society
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• v.9 no.1
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• pp.38-47
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• 2005

Axin is a scaffold protein of the APC/axin/GSK complex, binding to all of the other signalling components. Axin interacts with Glycogen synthase kinase 3$\beta$ (GSK 3$\beta$) and functions as a negative regulator of Wnt signalling pathways. To determine the solution structure of the GSK3$\beta$ binding regions of the axin, we initiated NMR study of axin fragment comprising residues 3$Val^{388} - Arg^{401}$using circular dichroism (CD) and two-dimensional NMR spectroscopy. The CD spectra of 3$axin^{pep}$ in the presence of 30% TFE displayed a standard 3$\alpha$-helical conformation, exhibiting the bound structure of 3$axin^{pep}$ to GSK3$\bata$. On the basis of experimental restraints including $NOE_s$, and $^3J_{HN\alpha} $ coupling constants, the solution conformation of $axin^{pep}$ was determined with program CNS. The 20 lowest energy structures were selected out of 50 final simulated-annealing structures in both water and TFE environment, respectively. The $RMSD_s$ for the 20 structures in TFE solution were 0.086 nm for backbone atoms and 0.195 nm for all heavy atoms, respectively. The Ramachandran plot indicates that the $\varphi$, $\psi$ angles of the 20 final structures is properly distributed in energetically acceptable regions. $Axin^pep$ in aqueous solutions consists of a stable $\alpha$-helix spanning residues form $Glu^{391}$ to $Val^{391} $, which is an interacting motif with GSK3$\beta$.

• Bulletin of the Korean Chemical Society
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• v.32 no.6
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• pp.1859-1864
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• 2011

Two lanthanide complexes, $[Ln(NO_3)_2(H_2O)_3(L)_2](NO_3)(H_2O)$ {Ln = Eu (1), Tb (2); L = 2-(4-pyridylium)-ethanesulfonate, $(4-pyH)^+-CH_2CH_2-SO_3^-)$}, were prepared from lanthanide nitrate and 4-pyridineethanesulfonic acid in $H_2O$ under microwave-heating conditions. Complexes 1 and 2 are isostructural, and the lanthanide metal in both complexes is coordinated to nine oxygen atoms. The pyridyl nitrogen in the ligand is protonated to give a zwitter ion that possesses an $NH^+$ (pyridyl) positive end and an $SO_3^-$ negative end. All O-H and N-H hydrogen atoms participate in hydrogen bonds to generate a two-dimensional (complex 1) or a three-dimensional network (complex 2). Complex 1 exhibits an intense red emission, whereas complex 2 exhibits an intense green emission in the solid state at room temperature.

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.1-12
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• 2000

A number of numerical methods like Computational Fluid Dynamics(CFD) have been developed to predict the flow fields of a vessel but the present study is developed to infer the wake fields on propeller plane by Statistical Fluid Dynamics(SFD) approach which is emerging as a new technique over a wide range of industrial fields nowadays. Neural network is well known as one prospective representative of the SFD tool and is widely applied even in the engineering fields. Further to its stable and effective system structure, generalization of input training patterns into different classification or categorization in training can offer more systematic treatments of input part and more reliable result. Because neural network has an ability to learn the knowledge through the external information, it is not necessary to use logical programming and it can flexibly handle the incomplete information which is not easy to make a definition clear. Three dimensional stern hull forms and nominal wake values from a model test are structured as processing elements of input and output layer respectively and a neural network is trained by the back-propagation method. The inferred results show similar figures to the experimental wake distribution.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.15-22
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• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.

• BMB Reports
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• v.57 no.7
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• pp.311-317
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• 2024

Brain organoid is a three-dimensional (3D) tissue derived from stem cells such as induced pluripotent stem cells (iPSCs) embryonic stem cells (ESCs) that reflect real human brain structure. It replicates the complexity and development of the human brain, enabling studies of the human brain in vitro. With emerging technologies, its application is various, including disease modeling and drug screening. A variety of experimental methods have been used to study structural and molecular characteristics of brain organoids. However, electrophysiological analysis is necessary to understand their functional characteristics and complexity. Although electrophysiological approaches have rapidly advanced for monolayered cells, there are some limitations in studying electrophysiological and neural network characteristics due to the lack of 3D characteristics. Herein, electrophysiological measurement and analytical methods related to neural complexity and 3D characteristics of brain organoids are reviewed. Overall, electrophysiological understanding of brain organoids allows us to overcome limitations of monolayer in vitro cell culture models, providing deep insights into the neural network complex of the real human brain and new ways of disease modeling.