• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.8 no.2
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• pp.1-8
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• 1990

A regular grid or a triangulated irregular network is generally used as the data structure of digital terrain models. A Regular grid is simple and easy to manipulate, but it can't describe well terrain surface features and requires vast volumes of data. In the meantime, a triangulated irregular network has complex data structure, but it can describe well terrain surface features and can achieve the accuracy suitable to its application with relatively little data. This paper aims at the construction of efficient digital terrain models by the improvment of a triangulated irregular network based on Delaunay triangulation. Regular and irregular data set are sampled from existing contour maps, and the efficiency and the accuracy of the two data structures are compared.

• Bulletin of the Korean Chemical Society
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• v.25 no.12
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• pp.1817-1821
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• 2004

Fourier transform Raman spectra of methyl red adsorbed on untreated and pretreated ${\gamma}$-alumina and silicaalumina calcined at 900 $^{\circ}C$ under 1 atm steam flowing were recorded. Spectral analysis shows that the active species adsorbed on ${\gamma}$-alumina was to be deprotonated methyl red, and on silica-alumina to be di-protonated. This indicates that ${\gamma}$-alumina adapted in this work holds Bronsted basicity, and silica-alumina Bronsted acidity. Raman intensities of methyl red on pretreated ${\gamma}$-alumina are about three times stronger than on untreated ${\gamma}$-alumina, while spectral features are unchanged. For silica-alumina, spectral features show modified vibrational characteristics upon surface hydroxylations generated from pretreatment. Consequently, the acidity loss for silica-alumina and the basicity gain for ${\gamma}$-alumina were observed by increasing the surface hydroxyl groups on the catalysts through pretreatment of the steam calcination.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• Journal of the Korea Computer Graphics Society
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• v.16 no.2
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• pp.29-37
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• 2010

In this paper, we present a novel deformation method for alleviating the asymmetry of a scanned 3D face considering facial features. To handle detailed areas of the face, we developed a new local 3D shape descriptor based on facial features and surface curvatures. Our shape descriptor can improve the accuracy when deforming a 3D face toward a symmetric configuration, because it provides accurate point pairing with respect to the plane of symmetry. In addition, we use point-based representation over all stages of symmetrization, which makes it much easier to support discrete processes. Finally, we performed a statistical analysis to assess subjects' preference for the symmetrized faces by our approach.

• Journal of Korean Society of Forest Science
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• v.80 no.3
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• pp.296-302
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• 1991

Compression wood and opposite wood formed in a branch of Taxodium distichum Rich. is described and compared in qualitative and quantitative anatomical aspects. The qualitative features of compression wood appeared to differ from those of opposite wood in very gradual tracheid transition from earlywood to latewood, roundish tracheid shape on cross surface, tracheid tip distortion on radial surface, and existence of intercellular spaces and helical cavities. In quantitative features, compression wood tracheids showed shorter lengths than opposite wood. The ray density and the number of uniseriate rays were greater in compression wood than in opposite wood but the height of uniseriate rays in compression wood was smaller than in opposite wood.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.57-63
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• 2004

This paper describes a method for digitizing the compound surfaces which are comprised of several unknown feature shapes such as base surface, and draft wall. From the reverse engineering's point of view, the main step is to digitize or gather three-dimensional points on an object rapidly and precisely. As well known, the non-contact digitizing apparatus using a laser or structured light can rapidly obtain a great bulk of digitized points, while the touch or scanning probe gives higher accuracy by directly contacting its stylus onto the part surface. By combining those two methods, unknown features can be digitized efficiently. The paper proposes a digitizing methodology using the approximated surface model obtained from laser-scanned data, followed by the use of a scanning probe. Each surface boundary curve and the confining area is investigated to select the most suitable digitizing path topology, which is similar to generating NC tool-paths. The methodology was tested with a simple physical model whose shape is comprised of a base surface, draft walls and cavity volumes.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.133-133
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• 2013

For both oxygen reduction (ORR) and hydrogen oxidation reactions (HOR) of proton electrolyte membrane fuel cells (PEMFCs), alloying Pt with another transition metal usually results in a higher activity relative to pure Pt, mainly due to electronic modification of Pt and bifunctional behaviour of alloy surface for ORR and HOR, respectively. However, activity and stability are closely related to the preparation of alloy nanoparticles. Preparation conditions of alloy nanoparticles have strong influence on surface composition, oxidation state, nanoparticle size, shape, and contamination, which result from a large difference in redox priority of metal precursors, intrinsic properties of metals, increasedreactivity of nanocrystallites, and interactions with constituents for the synthesis such as solvent, stabilizer, and reducing agent, etc. Carbon-supported Pt-Ni alloy nanoparticles were prepared by the borohydride reduction method in anhydrous solvent. Pt-Ru alloy nanoparticles supported on carbon black were also prepared by the similar synthetic method to that of Pt-Ni. Since electrocatalytic reactions are strongly dependent on the surface structure of metal catalysts, the atom-leveled design of the surface structure plays a significant role in a high catalytic activity and the utilization of electrocatalysts. Therefore, surface-modified electrocatalysts have attracted much attention due to their unique structure and new electronic and electrocatalytic properties. The carbon-supported Au and Pd nanoparticles were adapted as the substrate and the successive reduction process was used for depositing Pt and PtM (M=Ru, Pd, and Rh) bimetallic elements on the surface of Au and Pd nanoparticles. Distinct features of the overlayers for electrocatalytic activities including methanol oxidation, formic acid oxidation, and oxygen reduction were investigated.

• Transactions on Electrical and Electronic Materials
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• v.17 no.2
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• pp.98-103
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• 2016

The surface morphology of the front transparent conductive oxide (TCO) films plays a vital role in amorphous silicon thin film solar cells (a-Si TFSCs) due to their high transparency, conductivity and excellent light scattering properties. Recently, plasma textured glass surface morphologies received much attention for light trapping in a-Si TFSCs. We report various plasma textured glass surface morphologies for the high efficiency of a-Si TFSCs. Plasma textured glass surface morphologies showed high rms roughness, haze ratio with micro- and nano size surface features and are proposed for future high efficiency of a-Si TFSCs.

• Journal of the Korea Furniture Society
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• v.22 no.2
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• pp.83-90
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• 2011

In the radically changing market situation, surface materials of student furniture are a very important element to decide the consumption pattern and the market price. Thus, this study examined the characteristics of surface materials by classifying the student furniture into children's, juniors', and seniors' furniture while accessing the features of such surface materials through Glulam, veneer, poly paper, finishing foil, high pressure melamine (HPM), and low pressure melamine (LPM), and PVC vinyl. When taking a look at the surface materials that were most frequently produced in each domestic furniture company, the combined materials that used more than 2 elements (31.88%), LPM (23.19%), and Glulam (20.29%) were introduced at the market in the order. Regarding the analysis made on colors, the combination colors took more than half (55.32%) followed by brown (13.83%) and beige (11.7%) in the order. Based on the results derived from the analysis, the study suggests to stress more importance to surface materials when developing student furniture and to exert efforts for the consistent development of new materials and quality improvement, as a way to accelerate the development of the furniture industry in Korea.