• Bulletin of the Korean Chemical Society
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• v.7 no.5
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• pp.331-334
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• 1986

The crystal structure of thiamin tetrahydrofurfuryl disulfide, one of the ring-opened derivatives of thiamin, has been determined by the X-ray diffraction methods. The crystal is monoclinic with cell dimensions of a = 8.704 (1), b = 11.207 (2), c = 21.260 (3) ${\AA}$ and ${\beta}$ = 92.44 (2)$^{circ}$, space group P2$_{1}$/c and Z = 4. The structure was solved by direct methods and refined to R = 0.076 for 1252 observed reflections measured on a diffractometer. The molecule assumes a folded conformation in which the pyrimidine and the tetrahydrofurfuryl rings are on the same side of the ethylenic plane. The pyrimidinyl, N-formyl and ethylenic planes are mutually perpendicular to each other and the N(3)-C(4) bond retains a single bond character. The structure is stabilized by an intramolecular N(4'${\alpha})-H{\cdots}O(2{\alpha}$) hydrogen bond. The molecules are connected via N(4'${\alpha}$)-H{\cdots}(N3')$ and O(5${\gamma})-H{\cdots}(N1')$ hydrogen bonds, forming a two-dimensional hydrogen-bonding network. The tetrahydrofurfuryl ring is dynamically disordered. The overall conformation as well as the packing mode is very similar to that of thiamin propyl disulfide.

• Journal of KIBIM
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• v.14 no.2
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• pp.1-12
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• 2024

The paper proposes a methodology that combines the Random Sample Consensus (RANSAC) algorithm and the Point Cloud Encoder-Decoder Network (PCEDNet) algorithm to automatically extract the length of infrastructure elements from point cloud data acquired through 3D LiDAR scans of retaining walls. This methodology is expected to significantly improve time and cost efficiency compared to traditional manual measurement techniques, which are crucial for the data-driven analysis required in the precision-demanding construction sector. Additionally, the extracted positional and dimensional data can contribute to enhanced accuracy and reliability in Scan-to-BIM processes. The results of this study are anticipated to provide important insights that could accelerate the digital transformation of the construction industry. This paper provides empirical data on how the integration of digital technologies can enhance efficiency and accuracy in the construction industry, and offers directions for future research and application.

• Journal of The Korean Astronomical Society
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• v.49 no.3
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• pp.93-107
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• 2016

Augmenting the Wide Field Infrared Survey Telescope (WFIRST) microlensing campaigns with intensive observations from a ground-based network of wide-field survey telescopes would have several major advantages. First, it would enable full two-dimensional (2-D) vector microlens parallax measurements for a substantial fraction of low-mass lenses as well as planetary and binary events that show caustic crossing features. For a significant fraction of the free-floating planet (FFP) events and all caustic-crossing planetary/binary events, these 2-D parallax measurements directly lead to complete solutions (mass, distance, transverse velocity) of the lens object (or lens system). For even more events, the complementary ground-based observations will yield 1-D parallax measurements. Together with the 1-D parallaxes from WFIRST alone, they can probe the entire mass range M ≳ M_⊕. For luminous lenses, such 1-D parallax measurements can be promoted to complete solutions (mass, distance, transverse velocity) by high-resolution imaging. This would provide crucial information not only about the hosts of planets and other lenses, but also enable a much more precise Galactic model. Other benefits of such a survey include improved understanding of binaries (particularly with low mass primaries), and sensitivity to distant ice-giant and gas-giant companions of WFIRST lenses that cannot be detected by WFIRST itself due to its restricted observing windows. Existing ground-based microlensing surveys can be employed if WFIRST is pointed at lower-extinction fields than is currently envisaged. This would come at some cost to the event rate. Therefore the benefits of improved characterization of lenses must be weighed against these costs.

• Journal of the Korean Chemical Society
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• v.47 no.4
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• pp.334-338
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• 2003

The crystal structure of $[Co(phen)_2(Cl)(H_2O)] Clㆍ2H_2O$(phen=1,10-phenanthroline) has been determined by X-ray crystallography. It crystallizes in the triclinic system, space group P1, with lattice parameters a=9.662(2), b=11.445(1), c=13.037(2)${\AA}$ ${\alpha}$=64.02(1), ${\beta}$=86.364(9), ${\gamma}=78.58(2)^°$, and Z=2. The coordinated cations contain a six-coordinated cobalt atom chelated by two phen ligands and one chloride anion and one water ligand in cis arrangement. In addition to the chloride coordinated to the cobalt, there are one chloride ion and four water molecules which complete the crystal structure. In the solid state, the title compound forms three dimensional network structure through hydrogen bonds, within which exists the strongest hydrogen bond (O(3)-O(4)=2.33${\AA}$). The intermolecular hydrogen bonds connect the $[Co(phen)_2(Cl)(H_2O)]1+,\;H_2O$ moieties and chloride ion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.453-462
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• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.943-961
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• 2020

With the advent of IoT technology and Big Data computing, the importance of WSNs (Wireless Sensor Networks) has been on the rise. For energy-efficient and collection-efficient delivery of any sensed data, lots of novel wireless medium access control (MAC) protocols have been proposed and these MAC schemes are the basis of many IoT systems that leads the upcoming fourth industrial revolution. WSNs play a very important role in collecting Big Data from various IoT sensors. Also, due to the limited amount of battery driving the sensors, energy-saving MAC technologies have been recently studied. In addition, as new IoT technologies for Big Data computing emerge to meet different needs, both sensors and sinks need to be mobile. To guarantee stability of WSNs with dynamic topologies as well as frequent physical changes, the existing MAC schemes must be tuned for better adapting to the new WSN environment which includes energy-efficiency and collection-efficiency of sensors, coverage of WSNs and data collecting methods of sinks. To address these issues, in this paper, a self-organization scheme for mobile sensor networks with mobile multiple sinks has been proposed and verified to adapt both mobile sensors and multiple sinks to 3-dimensional group management MAC protocol. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of the various usage cases. Therefore, the proposed self-organization scheme might be adaptable for various computing and networking environments with big data.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.

• The Transactions of the Korea Information Processing Society
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• v.5 no.12
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• pp.3275-3284
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• 1998

In this paper, we deseribe a Medical Image Information System. Our system stores and manages 5 dimensional medical image data and provides the 3 dimensional medical data via the Internet. The Internet standard VR format. VRML(Virtual Reality Modeling Language) is used to represent the 3I) medical image data. The 3D images are reconstructed from medical image data which are enerated by medical imaging systems such ans CT(Computerized Tomography). MRI(Magnetic Resonance Imaging). PET(Positron Emission Tomograph), SPECT(Single Photon Emission Compated Tomography). We implemented the medical image information system shich rses a surface-based rendering method for the econstruction of 3D images from 2D medical image data. In order to reduce the size of image files to be transfered via the Internet. The system can reduce more than 50% for the triangles which represent the surfaces of the generated 3D medical images. When we compress the 3D image file, the size of the file can be redued more than 80%. The users can promptly retrieve 3D medical image data through the Internet and view the 3D medical images without a graphical acceleration card, because the images are represented in VRML. The image data are generated by various types of medical imaging systems such as CT, MRI, PET, and SPECT. Our system can display those different types of medical images in the 2D and the 3D formats. The patient information and the diagnostic information are also provided by the system. The system can be used to implement the "Tele medicaine" systems.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.415-423
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• 2010

Pore network models are useful tools to investigate soil pore geometry. These models provide quantitative information of pore geometry from 3D images. This study presents a pore network model to quantify pore structure and hydraulic characteristics. The objectives of this work were to apply the pore network model to characterize pore structure from large images to quantify pore structure, calculate water retention and hydraulic conductivity properties from a three dimensional soil image, and to combine measured hydraulic properties from experiments with calculated hydraulic properties from image. Soil samples were taken from a site located at the Baltimore science center, which is located inside of the city. Undisturbed columns were taken from the site and scanned with a computer tomographer at resolutions of 22 ${\mu}m$. Pore networks were extracted by medial-axis transformation and were used to measure pore geometry from one of the scanned samples. Water retention and unsaturated hydraulic conductivity values were calculated from the soil image. Properties of soil bulk density, water retention and unsaturated hydraulic conductivity were measured from three replicates of scanned soil samples. 3D image analysis provided accurate detailed pore properties such as individual pore volumes, pore length, and tortuosity of all pores. These data made possible to calculate accurate estimations of water retention and hydraulic conductivity. Combination of the calculated and measured hydraulic properties gave more accurate information on pore sizes over wider range than measured or calculated data alone. We could conclude that the hydraulic property computed from soil images and laboratory measurements can describe a full structure of intra- and inter-aggregate pores in soil.